Datasets:

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ftopal

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huggingface-datasets-processed

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[ "# Dataset Card for Evaluation run of louisbrulenaudet/Pearl-34B-dare\n\n\n\nDataset automatically created during the evaluation run of model louisbrulenaudet/Pearl-34B-dare on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T04:00:24.953384(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 louisbrulenaudet/Pearl-34B-dare\n\n\n\nDataset automatically created during the evaluation run of model louisbrulenaudet/Pearl-34B-dare on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T04:00:24.953384(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" ]

[ 6, 189, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of louisbrulenaudet/Pearl-34B-dare\n\n\n\nDataset automatically created during the evaluation run of model louisbrulenaudet/Pearl-34B-dare on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T04:00:24.953384(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]" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of Gille/StrangeMerges_23-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Gille/StrangeMerges_23-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T04:04:45.787844(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 Gille/StrangeMerges_23-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Gille/StrangeMerges_23-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T04:04:45.787844(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" ]

[ 6, 189, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Gille/StrangeMerges_23-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Gille/StrangeMerges_23-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T04:04:45.787844(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "language:en", "license:cc0-1.0", "image-text-dataset", "synthetic-dataset", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for LAION DALL·E 3 Discord Dataset\n\nDescription: This dataset consists of caption and image pairs scraped from the LAION share-dalle-3 discord channel. The purpose is to collect image-text pairs for research and exploration.\n\nSource Code: The code used to generate this data can be found here.", "## Contributors\n\n- Zach Nagengast\n- Eduardo Pach\n- Seva Maltsev\n- The LAION community", "## Data Attributes\n\n- caption: The text description or prompt associated with the image. Data type: string.\n- image: The embedded image data from the discord message attachment. Data type: image.\n- link: The URL to the associated image. Data type: string.\n- message_id: The discord message id where the image was posted. Data type: string.\n- timestamp: Time the original message was posted. Datatype: string." ]

[ "TAGS\n#language-English #license-cc0-1.0 #image-text-dataset #synthetic-dataset #region-us \n", "# Dataset Card for LAION DALL·E 3 Discord Dataset\n\nDescription: This dataset consists of caption and image pairs scraped from the LAION share-dalle-3 discord channel. The purpose is to collect image-text pairs for research and exploration.\n\nSource Code: The code used to generate this data can be found here.", "## Contributors\n\n- Zach Nagengast\n- Eduardo Pach\n- Seva Maltsev\n- The LAION community", "## Data Attributes\n\n- caption: The text description or prompt associated with the image. Data type: string.\n- image: The embedded image data from the discord message attachment. Data type: image.\n- link: The URL to the associated image. Data type: string.\n- message_id: The discord message id where the image was posted. Data type: string.\n- timestamp: Time the original message was posted. Datatype: string." ]

[ 32, 74, 23, 96 ]

[ "passage: TAGS\n#language-English #license-cc0-1.0 #image-text-dataset #synthetic-dataset #region-us \n# Dataset Card for LAION DALL·E 3 Discord Dataset\n\nDescription: This dataset consists of caption and image pairs scraped from the LAION share-dalle-3 discord channel. The purpose is to collect image-text pairs for research and exploration.\n\nSource Code: The code used to generate this data can be found here.## Contributors\n\n- Zach Nagengast\n- Eduardo Pach\n- Seva Maltsev\n- The LAION community## Data Attributes\n\n- caption: The text description or prompt associated with the image. Data type: string.\n- image: The embedded image data from the discord message attachment. Data type: image.\n- link: The URL to the associated image. Data type: string.\n- message_id: The discord message id where the image was posted. Data type: string.\n- timestamp: Time the original message was posted. Datatype: string." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of indischepartij/MiniCPM-3B-Bacchus\n\n\n\nDataset automatically created during the evaluation run of model indischepartij/MiniCPM-3B-Bacchus on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T04:24:43.886690(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 indischepartij/MiniCPM-3B-Bacchus\n\n\n\nDataset automatically created during the evaluation run of model indischepartij/MiniCPM-3B-Bacchus on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T04:24:43.886690(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" ]

[ 6, 187, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of indischepartij/MiniCPM-3B-Bacchus\n\n\n\nDataset automatically created during the evaluation run of model indischepartij/MiniCPM-3B-Bacchus on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T04:24:43.886690(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 \"marriage_classification_1738_1951\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"marriage_classification_1738_1951\"\n\nMore Information needed" ]

[ 6, 21 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"marriage_classification_1738_1951\"\n\nMore Information needed" ]

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

[]

[]

[ "### How to use:" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "### How to use:" ]

[ 14, 6 ]

[ "passage: TAGS\n#license-apache-2.0 #region-us \n### How to use:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of InnerI/InnerILLM-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model InnerI/InnerILLM-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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T05:45:44.319472(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 InnerI/InnerILLM-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model InnerI/InnerILLM-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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T05:45:44.319472(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" ]

[ 6, 185, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of InnerI/InnerILLM-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model InnerI/InnerILLM-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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T05:45:44.319472(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 uukuguy/speechless-thoughts-mistral-7b\n\n\n\nDataset automatically created during the evaluation run of model uukuguy/speechless-thoughts-mistral-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T05:55:56.018611(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of uukuguy/speechless-thoughts-mistral-7b\n\n\n\nDataset automatically created during the evaluation run of model uukuguy/speechless-thoughts-mistral-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T05:55:56.018611(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" ]

[ 6, 193, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of uukuguy/speechless-thoughts-mistral-7b\n\n\n\nDataset automatically created during the evaluation run of model uukuguy/speechless-thoughts-mistral-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T05:55:56.018611(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]" ]

[ "size_categories:n<1K", "language:en", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#size_categories-n<1K #language-English #region-us \n" ]

[ 20 ]

[ "passage: TAGS\n#size_categories-n<1K #language-English #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of saishf/Fimbulvetr-Kuro-Lotus-10.7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/Fimbulvetr-Kuro-Lotus-10.7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T07:02:24.840541(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 saishf/Fimbulvetr-Kuro-Lotus-10.7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/Fimbulvetr-Kuro-Lotus-10.7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T07:02:24.840541(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" ]

[ 6, 199, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of saishf/Fimbulvetr-Kuro-Lotus-10.7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/Fimbulvetr-Kuro-Lotus-10.7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T07:02:24.840541(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]" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"high_vs_randommin_100_issues_per_repo\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"high_vs_randommin_100_issues_per_repo\"\n\nMore Information needed" ]

[ 6, 26 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"high_vs_randommin_100_issues_per_repo\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of ArianAskari/SOLID-SFT-DPO-MixQV3-SOLIDRejected-SFTChosen-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-DPO-MixQV3-SOLIDRejected-SFTChosen-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T07:27:37.172195(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 ArianAskari/SOLID-SFT-DPO-MixQV3-SOLIDRejected-SFTChosen-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-DPO-MixQV3-SOLIDRejected-SFTChosen-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T07:27:37.172195(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" ]

[ 6, 233, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ArianAskari/SOLID-SFT-DPO-MixQV3-SOLIDRejected-SFTChosen-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-DPO-MixQV3-SOLIDRejected-SFTChosen-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T07:27:37.172195(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" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of ArianAskari/SOLID-SFT-DPO-MixQV2-SOLIDRejected-SFTChosen-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-DPO-MixQV2-SOLIDRejected-SFTChosen-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T07:34:28.878740(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 ArianAskari/SOLID-SFT-DPO-MixQV2-SOLIDRejected-SFTChosen-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-DPO-MixQV2-SOLIDRejected-SFTChosen-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T07:34:28.878740(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" ]

[ 6, 233, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ArianAskari/SOLID-SFT-DPO-MixQV2-SOLIDRejected-SFTChosen-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-DPO-MixQV2-SOLIDRejected-SFTChosen-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T07:34:28.878740(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" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of ArianAskari/SOLID-SFT-DPO-MixQV3-SOLIDChosen-SFTRejected-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-DPO-MixQV3-SOLIDChosen-SFTRejected-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T07:37:59.900682(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 ArianAskari/SOLID-SFT-DPO-MixQV3-SOLIDChosen-SFTRejected-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-DPO-MixQV3-SOLIDChosen-SFTRejected-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T07:37:59.900682(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" ]

[ 6, 233, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ArianAskari/SOLID-SFT-DPO-MixQV3-SOLIDChosen-SFTRejected-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-DPO-MixQV3-SOLIDChosen-SFTRejected-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T07:37:59.900682(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" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of ArianAskari/SOLID-SFT-DPO-MixQV2-SOLIDChosen-SFTRejected-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-DPO-MixQV2-SOLIDChosen-SFTRejected-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T07:45:36.772955(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 ArianAskari/SOLID-SFT-DPO-MixQV2-SOLIDChosen-SFTRejected-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-DPO-MixQV2-SOLIDChosen-SFTRejected-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T07:45:36.772955(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" ]

[ 6, 233, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ArianAskari/SOLID-SFT-DPO-MixQV2-SOLIDChosen-SFTRejected-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-DPO-MixQV2-SOLIDChosen-SFTRejected-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T07:45:36.772955(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" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"find_first_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"find_first_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ 6, 29 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"find_first_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"find_last_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"find_last_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ 6, 28 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"find_last_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"find_second_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"find_second_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ 6, 28 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"find_second_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"find_sent_before_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"find_sent_before_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ 6, 31 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"find_sent_before_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"wsd_myriade_synth_data_v3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"wsd_myriade_synth_data_v3\"\n\nMore Information needed" ]

[ 6, 24 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"wsd_myriade_synth_data_v3\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"find_sent_after_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"find_sent_after_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ 6, 31 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"find_sent_after_sent_train_400_eval_40_baseline\"\n\nMore Information needed" ]

[ "size_categories:1M<n<10M", "language:lt", "language:en", "license:cc-by-2.5", "region:us" ]

[]

[ "lt", "en" ]

[]

[ "TAGS\n#size_categories-1M<n<10M #language-Lithuanian #language-English #license-cc-by-2.5 #region-us \n" ]

[ 38 ]

[ "passage: TAGS\n#size_categories-1M<n<10M #language-Lithuanian #language-English #license-cc-by-2.5 #region-us \n" ]

[ "task_categories:image-to-image", "language:en", "region:us" ]

[]

[ "en" ]

[]

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

[ 22 ]

[ "passage: TAGS\n#task_categories-image-to-image #language-English #region-us \n" ]

[ "task_categories:feature-extraction", "task_categories:question-answering", "size_categories:1M<n<10M", "language:da", "license:apache-2.0", "region:us" ]

[]

[ "da" ]

[ "# GooAQ (Google Answers to Google Questions) question-answer pairs in Danish", "## About\n\nThis dataset is a version of the GooAQ question-answer pairs dataset machine-translated from English to Danish (link to original dataset).\n\nMachine translation is performed using the Helsinki NLP English-to-Danish OPUS-MT model.\n\nThe dataset contains ~3M question-answer pairs and can be used to train embedding and question-answer models. Each pair consists of one question ('query') and one passage containing the answer ('passage').", "## Usage\n\nUsing the HuggingFace datasets library:" ]

[ "TAGS\n#task_categories-feature-extraction #task_categories-question-answering #size_categories-1M<n<10M #language-Danish #license-apache-2.0 #region-us \n", "# GooAQ (Google Answers to Google Questions) question-answer pairs in Danish", "## About\n\nThis dataset is a version of the GooAQ question-answer pairs dataset machine-translated from English to Danish (link to original dataset).\n\nMachine translation is performed using the Helsinki NLP English-to-Danish OPUS-MT model.\n\nThe dataset contains ~3M question-answer pairs and can be used to train embedding and question-answer models. Each pair consists of one question ('query') and one passage containing the answer ('passage').", "## Usage\n\nUsing the HuggingFace datasets library:" ]

[ 55, 20, 114, 16 ]

[ "passage: TAGS\n#task_categories-feature-extraction #task_categories-question-answering #size_categories-1M<n<10M #language-Danish #license-apache-2.0 #region-us \n# GooAQ (Google Answers to Google Questions) question-answer pairs in Danish## About\n\nThis dataset is a version of the GooAQ question-answer pairs dataset machine-translated from English to Danish (link to original dataset).\n\nMachine translation is performed using the Helsinki NLP English-to-Danish OPUS-MT model.\n\nThe dataset contains ~3M question-answer pairs and can be used to train embedding and question-answer models. Each pair consists of one question ('query') and one passage containing the answer ('passage').## Usage\n\nUsing the HuggingFace datasets library:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of AbacusResearch/haLLAwa3\n\n\n\nDataset automatically created during the evaluation run of model AbacusResearch/haLLAwa3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T09:17:16.010723(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 AbacusResearch/haLLAwa3\n\n\n\nDataset automatically created during the evaluation run of model AbacusResearch/haLLAwa3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T09:17:16.010723(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" ]

[ 6, 181, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AbacusResearch/haLLAwa3\n\n\n\nDataset automatically created during the evaluation run of model AbacusResearch/haLLAwa3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T09:17:16.010723(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/babyllama-v0.6\n\n\n\nDataset automatically created during the evaluation run of model kevin009/babyllama-v0.6 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T10:06:30.565512(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/babyllama-v0.6\n\n\n\nDataset automatically created during the evaluation run of model kevin009/babyllama-v0.6 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T10:06:30.565512(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" ]

[ 6, 181, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of kevin009/babyllama-v0.6\n\n\n\nDataset automatically created during the evaluation run of model kevin009/babyllama-v0.6 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T10:06:30.565512(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:summarization", "size_categories:1K<n<10K", "language:en", "3d meshes", "point clouds", "synthetic", "realistic", "CAD", "statues", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-summarization #size_categories-1K<n<10K #language-English #3d meshes #point clouds #synthetic #realistic #CAD #statues #region-us \n" ]

[ 52 ]

[ "passage: TAGS\n#task_categories-summarization #size_categories-1K<n<10K #language-English #3d meshes #point clouds #synthetic #realistic #CAD #statues #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of saishf/West-Maid-7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/West-Maid-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T10:56:41.095810(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 saishf/West-Maid-7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/West-Maid-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T10:56:41.095810(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" ]

[ 6, 181, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of saishf/West-Maid-7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/West-Maid-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T10:56:41.095810(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 saishf/Kuno-Lake-7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/Kuno-Lake-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T11:02:51.081639(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 saishf/Kuno-Lake-7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/Kuno-Lake-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T11:02:51.081639(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" ]

[ 6, 183, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of saishf/Kuno-Lake-7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/Kuno-Lake-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T11:02:51.081639(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 saishf/Kuro-Lotus-10.7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/Kuro-Lotus-10.7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T11:03:22.904872(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 saishf/Kuro-Lotus-10.7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/Kuro-Lotus-10.7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T11:03:22.904872(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" ]

[ 6, 187, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of saishf/Kuro-Lotus-10.7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/Kuro-Lotus-10.7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T11:03:22.904872(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 SF-Foundation/Ein-72B-v0.12\n\n\n\nDataset automatically created during the evaluation run of model SF-Foundation/Ein-72B-v0.12 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T11:06:19.237402(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 SF-Foundation/Ein-72B-v0.12\n\n\n\nDataset automatically created during the evaluation run of model SF-Foundation/Ein-72B-v0.12 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T11:06:19.237402(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" ]

[ 6, 189, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of SF-Foundation/Ein-72B-v0.12\n\n\n\nDataset automatically created during the evaluation run of model SF-Foundation/Ein-72B-v0.12 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T11:06:19.237402(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]" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of saishf/Top-Western-Maid-7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/Top-Western-Maid-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T11:07:35.441841(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 saishf/Top-Western-Maid-7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/Top-Western-Maid-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T11:07:35.441841(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" ]

[ 6, 187, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of saishf/Top-Western-Maid-7B\n\n\n\nDataset automatically created during the evaluation run of model saishf/Top-Western-Maid-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T11:07:35.441841(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "task_categories:question-answering", "task_categories:conversational", "task_categories:text2text-generation", "size_categories:1K<n<10K", "language:tr", "region:us" ]

[]

[ "tr" ]

[]

[ "TAGS\n#task_categories-question-answering #task_categories-conversational #task_categories-text2text-generation #size_categories-1K<n<10K #language-Turkish #region-us \n" ]

[ 59 ]

[ "passage: TAGS\n#task_categories-question-answering #task_categories-conversational #task_categories-text2text-generation #size_categories-1K<n<10K #language-Turkish #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of SF-Foundation/Ein-72B-v0.13\n\n\n\nDataset automatically created during the evaluation run of model SF-Foundation/Ein-72B-v0.13 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T11:08:38.011897(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 SF-Foundation/Ein-72B-v0.13\n\n\n\nDataset automatically created during the evaluation run of model SF-Foundation/Ein-72B-v0.13 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T11:08:38.011897(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" ]

[ 6, 189, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of SF-Foundation/Ein-72B-v0.13\n\n\n\nDataset automatically created during the evaluation run of model SF-Foundation/Ein-72B-v0.13 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T11:08:38.011897(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]" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Dataset Name\n\nThis dataset has been meticulously curated by the AI team at IEEE Student Branch, Vishwakarma Institute of Technology (VIT) Pune, with the explicit purpose of training the Llama2 model. It encompasses a diverse range of topics essential for the development of an effective conversational AI system.", "## Dataset Details", "### Dataset Description\nThe dataset comprises a comprehensive selection of topics, including but not limited to:\n\nFrequently Asked Questions (FAQs) related to IEEE Student Branch at VIT Pune.\nInquiries pertaining to placements, encompassing strategies, tips, and common queries.\nQuestions related to fundamental concepts in Data Structures and Algorithms.\nQueries and discussions regarding research papers, methodologies, and academic pursuits.\n\n- Curated by: AI Team- IEEE SB VIT Pune", "## Uses\n\nThis data was particularly designed for a chatbot for IEEE SB VIT Pune so that university students could use it for their own benifits, but it includes some general topics related to Research Papers, Data Structure and Algorithms and Placements that can be used by others for their custom chatbot", "## Dataset Structure\n\nThe dataset consists of the following fields:\n\n- Instruction: This field represents the prompt or query posed to the chatbot.\n- Response: This field contains the corresponding generated response by the chatbot.", "## Dataset Structure Information\n\nThe dataset is structured in a JSON format, with each entry containing the following fields:\n\n'''json\n{\n \"instruction\": \"What is IEEE?\",\n \"response\": \"The IEEE or Institute of Electrical and Electronics Engineers is the world's largest professional technical organization dedicated to the advancement of technology for the benefit of humanity.\"\n}", "### Curation Rationale\n\nThe motivation behind curating this dataset stems from a genuine desire to empower and support university students pursuing B.Tech degrees. Recognizing the pivotal role that IEEE Student Branch at Vishwakarma Institute of Technology (VIT) Pune plays in students' academic journeys, the aim was to create a resource that elucidates the myriad ways in which IEEE SB VIT Pune can enrich and enhance students' educational experiences.\nAt its core, this dataset is a testament to the commitment of the AI team at IEEE SB VIT Pune to empower B.Tech students with valuable insights and resources. By curating a comprehensive collection of topics spanning FAQs, placement strategies, technical concepts, and research discussions, the dataset seeks to equip students with the knowledge and understanding necessary to navigate their academic pursuits effectively.", "## Dataset Card Authors\nAI Team- IEEE SB VIT Pune\nMrunmayee Phadke (Project Head)\nHritesh Maikap\nNidhish\nArya Lokhande\nApurva Kota\nSoham Nimale" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name\n\nThis dataset has been meticulously curated by the AI team at IEEE Student Branch, Vishwakarma Institute of Technology (VIT) Pune, with the explicit purpose of training the Llama2 model. It encompasses a diverse range of topics essential for the development of an effective conversational AI system.", "## Dataset Details", "### Dataset Description\nThe dataset comprises a comprehensive selection of topics, including but not limited to:\n\nFrequently Asked Questions (FAQs) related to IEEE Student Branch at VIT Pune.\nInquiries pertaining to placements, encompassing strategies, tips, and common queries.\nQuestions related to fundamental concepts in Data Structures and Algorithms.\nQueries and discussions regarding research papers, methodologies, and academic pursuits.\n\n- Curated by: AI Team- IEEE SB VIT Pune", "## Uses\n\nThis data was particularly designed for a chatbot for IEEE SB VIT Pune so that university students could use it for their own benifits, but it includes some general topics related to Research Papers, Data Structure and Algorithms and Placements that can be used by others for their custom chatbot", "## Dataset Structure\n\nThe dataset consists of the following fields:\n\n- Instruction: This field represents the prompt or query posed to the chatbot.\n- Response: This field contains the corresponding generated response by the chatbot.", "## Dataset Structure Information\n\nThe dataset is structured in a JSON format, with each entry containing the following fields:\n\n'''json\n{\n \"instruction\": \"What is IEEE?\",\n \"response\": \"The IEEE or Institute of Electrical and Electronics Engineers is the world's largest professional technical organization dedicated to the advancement of technology for the benefit of humanity.\"\n}", "### Curation Rationale\n\nThe motivation behind curating this dataset stems from a genuine desire to empower and support university students pursuing B.Tech degrees. Recognizing the pivotal role that IEEE Student Branch at Vishwakarma Institute of Technology (VIT) Pune plays in students' academic journeys, the aim was to create a resource that elucidates the myriad ways in which IEEE SB VIT Pune can enrich and enhance students' educational experiences.\nAt its core, this dataset is a testament to the commitment of the AI team at IEEE SB VIT Pune to empower B.Tech students with valuable insights and resources. By curating a comprehensive collection of topics spanning FAQs, placement strategies, technical concepts, and research discussions, the dataset seeks to equip students with the knowledge and understanding necessary to navigate their academic pursuits effectively.", "## Dataset Card Authors\nAI Team- IEEE SB VIT Pune\nMrunmayee Phadke (Project Head)\nHritesh Maikap\nNidhish\nArya Lokhande\nApurva Kota\nSoham Nimale" ]

[ 6, 76, 4, 123, 69, 54, 88, 195, 47 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Dataset Name\n\nThis dataset has been meticulously curated by the AI team at IEEE Student Branch, Vishwakarma Institute of Technology (VIT) Pune, with the explicit purpose of training the Llama2 model. It encompasses a diverse range of topics essential for the development of an effective conversational AI system.## Dataset Details### Dataset Description\nThe dataset comprises a comprehensive selection of topics, including but not limited to:\n\nFrequently Asked Questions (FAQs) related to IEEE Student Branch at VIT Pune.\nInquiries pertaining to placements, encompassing strategies, tips, and common queries.\nQuestions related to fundamental concepts in Data Structures and Algorithms.\nQueries and discussions regarding research papers, methodologies, and academic pursuits.\n\n- Curated by: AI Team- IEEE SB VIT Pune## Uses\n\nThis data was particularly designed for a chatbot for IEEE SB VIT Pune so that university students could use it for their own benifits, but it includes some general topics related to Research Papers, Data Structure and Algorithms and Placements that can be used by others for their custom chatbot## Dataset Structure\n\nThe dataset consists of the following fields:\n\n- Instruction: This field represents the prompt or query posed to the chatbot.\n- Response: This field contains the corresponding generated response by the chatbot.## Dataset Structure Information\n\nThe dataset is structured in a JSON format, with each entry containing the following fields:\n\n'''json\n{\n \"instruction\": \"What is IEEE?\",\n \"response\": \"The IEEE or Institute of Electrical and Electronics Engineers is the world's largest professional technical organization dedicated to the advancement of technology for the benefit of humanity.\"\n}" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of KnutJaegersberg/Deita-2b\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deita-2b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T12:11:14.292713(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of KnutJaegersberg/Deita-2b\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deita-2b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T12:11:14.292713(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" ]

[ 6, 181, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of KnutJaegersberg/Deita-2b\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deita-2b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T12:11:14.292713(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 KnutJaegersberg/Deita-4b\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deita-4b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T12:13:56.610648(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of KnutJaegersberg/Deita-4b\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deita-4b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T12:13:56.610648(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" ]

[ 6, 181, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of KnutJaegersberg/Deita-4b\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deita-4b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T12:13:56.610648(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 \"input-dataset\"\n\nMore Information needed" ]

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

[ 6, 15 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"input-dataset\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of AbacusResearch/jaLLAbi\n\n\n\nDataset automatically created during the evaluation run of model AbacusResearch/jaLLAbi on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T12:32:57.211662(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 AbacusResearch/jaLLAbi\n\n\n\nDataset automatically created during the evaluation run of model AbacusResearch/jaLLAbi on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T12:32:57.211662(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" ]

[ 6, 179, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AbacusResearch/jaLLAbi\n\n\n\nDataset automatically created during the evaluation run of model AbacusResearch/jaLLAbi on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T12:32:57.211662(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/mtor-2x7b\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/mtor-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 2024-02-13T12:35:53.883707(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/mtor-2x7b\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/mtor-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 2024-02-13T12:35:53.883707(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" ]

[ 6, 179, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Technoculture/mtor-2x7b\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/mtor-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 2024-02-13T12:35:53.883707(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:video-classification", "task_categories:image-to-video", "language:en", "license:cc-by-nc-nd-4.0", "code", "finance", "legal", "region:us" ]

[]

[ "en" ]

[ "# Liveness Detection - Video Classification\n\nThe biometric attack dataset with replay attacks on the real videos of people. Replay attack involves presenting a pre-recorded video or previously captured footage as if it were occurring in real-time. \nThe primary objective is to distinguish between genuine, real-time footage and manipulated recordings. \n\nThe videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users. \n\nThe dataset contains videos of real humans with various resolutions, views, and colors, making it a comprehensive resource for researchers working on anti-spoofing technologies.\n\n![](URL\n\nThe dataset provides data to combine and apply different techniques, approaches, and models to address the challenging task of distinguishing between genuine and spoofed inputs, providing effective anti-spoofing solutions in active authentication systems. These solutions are crucial as newer devices, such as phones, have become vulnerable to spoofing attacks due to the availability of technologies that can create replays, reflections, and depths, making them susceptible to spoofing and generalization.", "### People in the dataset\n\n![](URL\n\nOur dataset also explores the use of neural architectures, such as deep neural networks, to facilitate the identification of distinguishing patterns and textures in different regions of the face, increasing the accuracy and generalizability of the anti-spoofing models.", "# For Commercial Usage: Full version of the dataset includes 51 000+ videos, leave a request on TrainingData to buy the dataset", "### Metadata for the full dataset:\n- replay.assignment_id - unique identifier of the media file\n- real_assignment_id- unique identifier of the media file from the Antispoofing Real Dataset\n- worker_id - unique identifier of the person\n- age - age of the person\n- true_gender - gender of the person\n- country - country of the person\n- ethnicity - ethnicity of the person\n- video_extension - video extensions in the dataset\n- video_resolution - video resolution in the dataset\n- video_duration - video duration in the dataset\n- video_fps - frames per second for video in the dataset", "# Buy the Dataset: This is just an example of the data. Leave a request on URL to learn about the price and buy the dataset", "# Content\nThe dataset includes files folder with videos of people", "### File with the extension .csv\n- id: id of the person,\n- file: link to access the display spoof attack video", "## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: <URL\n\nTrainingData's GitHub: URL\n\n*keywords: liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, ibeta dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset*" ]

[ "TAGS\n#task_categories-video-classification #task_categories-image-to-video #language-English #license-cc-by-nc-nd-4.0 #code #finance #legal #region-us \n", "# Liveness Detection - Video Classification\n\nThe biometric attack dataset with replay attacks on the real videos of people. Replay attack involves presenting a pre-recorded video or previously captured footage as if it were occurring in real-time. \nThe primary objective is to distinguish between genuine, real-time footage and manipulated recordings. \n\nThe videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users. \n\nThe dataset contains videos of real humans with various resolutions, views, and colors, making it a comprehensive resource for researchers working on anti-spoofing technologies.\n\n![](URL\n\nThe dataset provides data to combine and apply different techniques, approaches, and models to address the challenging task of distinguishing between genuine and spoofed inputs, providing effective anti-spoofing solutions in active authentication systems. These solutions are crucial as newer devices, such as phones, have become vulnerable to spoofing attacks due to the availability of technologies that can create replays, reflections, and depths, making them susceptible to spoofing and generalization.", "### People in the dataset\n\n![](URL\n\nOur dataset also explores the use of neural architectures, such as deep neural networks, to facilitate the identification of distinguishing patterns and textures in different regions of the face, increasing the accuracy and generalizability of the anti-spoofing models.", "# For Commercial Usage: Full version of the dataset includes 51 000+ videos, leave a request on TrainingData to buy the dataset", "### Metadata for the full dataset:\n- replay.assignment_id - unique identifier of the media file\n- real_assignment_id- unique identifier of the media file from the Antispoofing Real Dataset\n- worker_id - unique identifier of the person\n- age - age of the person\n- true_gender - gender of the person\n- country - country of the person\n- ethnicity - ethnicity of the person\n- video_extension - video extensions in the dataset\n- video_resolution - video resolution in the dataset\n- video_duration - video duration in the dataset\n- video_fps - frames per second for video in the dataset", "# Buy the Dataset: This is just an example of the data. Leave a request on URL to learn about the price and buy the dataset", "# Content\nThe dataset includes files folder with videos of people", "### File with the extension .csv\n- id: id of the person,\n- file: link to access the display spoof attack video", "## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: <URL\n\nTrainingData's GitHub: URL\n\n*keywords: liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, ibeta dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset*" ]

[ 53, 293, 75, 31, 155, 30, 12, 30, 139 ]

[ "passage: TAGS\n#task_categories-video-classification #task_categories-image-to-video #language-English #license-cc-by-nc-nd-4.0 #code #finance #legal #region-us \n# Liveness Detection - Video Classification\n\nThe biometric attack dataset with replay attacks on the real videos of people. Replay attack involves presenting a pre-recorded video or previously captured footage as if it were occurring in real-time. \nThe primary objective is to distinguish between genuine, real-time footage and manipulated recordings. \n\nThe videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users. \n\nThe dataset contains videos of real humans with various resolutions, views, and colors, making it a comprehensive resource for researchers working on anti-spoofing technologies.\n\n![](URL\n\nThe dataset provides data to combine and apply different techniques, approaches, and models to address the challenging task of distinguishing between genuine and spoofed inputs, providing effective anti-spoofing solutions in active authentication systems. These solutions are crucial as newer devices, such as phones, have become vulnerable to spoofing attacks due to the availability of technologies that can create replays, reflections, and depths, making them susceptible to spoofing and generalization.### People in the dataset\n\n![](URL\n\nOur dataset also explores the use of neural architectures, such as deep neural networks, to facilitate the identification of distinguishing patterns and textures in different regions of the face, increasing the accuracy and generalizability of the anti-spoofing models.# For Commercial Usage: Full version of the dataset includes 51 000+ videos, leave a request on TrainingData to buy the dataset" ]

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

[]

[ "hu" ]

[ "# aya_dataset_hu\n\nAz aya adathalmaz magyar utasításokra szűrve." ]

[ "TAGS\n#language-Hungarian #region-us \n", "# aya_dataset_hu\n\nAz aya adathalmaz magyar utasításokra szűrve." ]

[ 12, 19 ]

[ "passage: TAGS\n#language-Hungarian #region-us \n# aya_dataset_hu\n\nAz aya adathalmaz magyar utasításokra szűrve." ]

[ "task_categories:time-series-forecasting", "multilinguality:monolingual", "language:en", "license:apache-2.0", "finance", "crypto", "economics", "trading", "blockchain", "quantitative-analysis", "machine-learning", "deep-learning", "time-series", "sequence-modeling", "price-prediction", "market-analysis", "investment-strategies", "technical-indicators", "historical-data-analysis", "region:us" ]

[]

[ "en" ]

[ "# CryptoData Dataset\n\nThe CryptoData dataset is a comprehensive collection of cryptocurrency market data, designed to support various analyses, including price prediction, market trend analysis, and the study of the impact of various indicators on cryptocurrency prices.\n\nThis dataset has been configured to provide flexibility in selecting specific types of market data through the use of different dataset configurations. Depending on the analysis needs, users can select one of the available configurations to load data tailored to their requirements.", "## Available Configurations:\n\n1. Default: Includes open, high, low, close, and volume for each cryptocurrency market and date.\n\n2. Close: Focuses on the close price and volume of each cryptocurrency market and date, optimized for simplicity and analyses centered on closing prices.\n\n3. Indicators: Expands upon the default configuration by including technical indicators such as RSI (Relative Strength Index), SMA (Simple Moving Average), and EMA (Exponential Moving Average), aimed at more advanced technical analyses.\n\n4. Sequences: Specifically designed for sequence prediction tasks, this configuration provides sequences of market data alongside the corresponding prediction targets, facilitating the development of models for future price prediction.", "## How to Use:\n\nBelow are Python code snippets demonstrating how to load the CryptoData dataset with each configuration. Before running the snippets, ensure you have the 'datasets' library from Hugging Face installed.", "## Dataset Structure:\n\n- 'market': The cryptocurrency market (e.g., \"BTC-USD\").\n- 'date'/'time': The date or time of the data point.\n- 'open', 'high', 'low', 'close': Open, high, low, and close prices for the cryptocurrency.\n- 'volume': The volume of transactions.\n- 'rsi', 'sma', 'ema': Technical indicators including Relative Strength Index, Simple Moving Average, and Exponential Moving Average (available in the 'indicators' configuration).\n- 'sequence', 'prediction': Arrays of historical data and the corresponding future data to predict (available in the 'sequences' configuration).", "## Important Notes:\n\n- This dataset is for academic and research purposes only. Ensure compliance with any usage restrictions set by the data provider.\n- When using technical indicators in your analysis, be aware that these indicators alone may not provide a complete picture of market dynamics.\n- The sequences configuration requires significant preprocessing, including the calculation of technical indicators and the formation of sequences. This configuration is best suited for those with experience in time series analysis and deep learning.\n\nand Acknowledgments:\n\nThis dataset is made available for public use by the cryptocurrency research community. While there is no specific citation for this dataset, users are encouraged to reference the dataset's URL and the corresponding author's contributions.\n\nHomepage: CryptoData Dataset on Hugging Face\n\nFor any questions or issues with the dataset, please raise an issue on the repository hosting the dataset." ]

[ "TAGS\n#task_categories-time-series-forecasting #multilinguality-monolingual #language-English #license-apache-2.0 #finance #crypto #economics #trading #blockchain #quantitative-analysis #machine-learning #deep-learning #time-series #sequence-modeling #price-prediction #market-analysis #investment-strategies #technical-indicators #historical-data-analysis #region-us \n", "# CryptoData Dataset\n\nThe CryptoData dataset is a comprehensive collection of cryptocurrency market data, designed to support various analyses, including price prediction, market trend analysis, and the study of the impact of various indicators on cryptocurrency prices.\n\nThis dataset has been configured to provide flexibility in selecting specific types of market data through the use of different dataset configurations. Depending on the analysis needs, users can select one of the available configurations to load data tailored to their requirements.", "## Available Configurations:\n\n1. Default: Includes open, high, low, close, and volume for each cryptocurrency market and date.\n\n2. Close: Focuses on the close price and volume of each cryptocurrency market and date, optimized for simplicity and analyses centered on closing prices.\n\n3. Indicators: Expands upon the default configuration by including technical indicators such as RSI (Relative Strength Index), SMA (Simple Moving Average), and EMA (Exponential Moving Average), aimed at more advanced technical analyses.\n\n4. Sequences: Specifically designed for sequence prediction tasks, this configuration provides sequences of market data alongside the corresponding prediction targets, facilitating the development of models for future price prediction.", "## How to Use:\n\nBelow are Python code snippets demonstrating how to load the CryptoData dataset with each configuration. Before running the snippets, ensure you have the 'datasets' library from Hugging Face installed.", "## Dataset Structure:\n\n- 'market': The cryptocurrency market (e.g., \"BTC-USD\").\n- 'date'/'time': The date or time of the data point.\n- 'open', 'high', 'low', 'close': Open, high, low, and close prices for the cryptocurrency.\n- 'volume': The volume of transactions.\n- 'rsi', 'sma', 'ema': Technical indicators including Relative Strength Index, Simple Moving Average, and Exponential Moving Average (available in the 'indicators' configuration).\n- 'sequence', 'prediction': Arrays of historical data and the corresponding future data to predict (available in the 'sequences' configuration).", "## Important Notes:\n\n- This dataset is for academic and research purposes only. Ensure compliance with any usage restrictions set by the data provider.\n- When using technical indicators in your analysis, be aware that these indicators alone may not provide a complete picture of market dynamics.\n- The sequences configuration requires significant preprocessing, including the calculation of technical indicators and the formation of sequences. This configuration is best suited for those with experience in time series analysis and deep learning.\n\nand Acknowledgments:\n\nThis dataset is made available for public use by the cryptocurrency research community. While there is no specific citation for this dataset, users are encouraged to reference the dataset's URL and the corresponding author's contributions.\n\nHomepage: CryptoData Dataset on Hugging Face\n\nFor any questions or issues with the dataset, please raise an issue on the repository hosting the dataset." ]

[ 115, 104, 171, 54, 177, 196 ]

[ "passage: TAGS\n#task_categories-time-series-forecasting #multilinguality-monolingual #language-English #license-apache-2.0 #finance #crypto #economics #trading #blockchain #quantitative-analysis #machine-learning #deep-learning #time-series #sequence-modeling #price-prediction #market-analysis #investment-strategies #technical-indicators #historical-data-analysis #region-us \n# CryptoData Dataset\n\nThe CryptoData dataset is a comprehensive collection of cryptocurrency market data, designed to support various analyses, including price prediction, market trend analysis, and the study of the impact of various indicators on cryptocurrency prices.\n\nThis dataset has been configured to provide flexibility in selecting specific types of market data through the use of different dataset configurations. Depending on the analysis needs, users can select one of the available configurations to load data tailored to their requirements.## Available Configurations:\n\n1. Default: Includes open, high, low, close, and volume for each cryptocurrency market and date.\n\n2. Close: Focuses on the close price and volume of each cryptocurrency market and date, optimized for simplicity and analyses centered on closing prices.\n\n3. Indicators: Expands upon the default configuration by including technical indicators such as RSI (Relative Strength Index), SMA (Simple Moving Average), and EMA (Exponential Moving Average), aimed at more advanced technical analyses.\n\n4. Sequences: Specifically designed for sequence prediction tasks, this configuration provides sequences of market data alongside the corresponding prediction targets, facilitating the development of models for future price prediction.## How to Use:\n\nBelow are Python code snippets demonstrating how to load the CryptoData dataset with each configuration. Before running the snippets, ensure you have the 'datasets' library from Hugging Face installed." ]

[ "task_categories:text-classification", "task_categories:question-answering", "task_categories:summarization", "task_categories:feature-extraction", "task_categories:conversational", "task_categories:sentence-similarity", "size_categories:10K<n<100K", "language:en", "Customer Reviews", "Second-Hand", "Product Reviews", "Comments", "Text Data", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #task_categories-question-answering #task_categories-summarization #task_categories-feature-extraction #task_categories-conversational #task_categories-sentence-similarity #size_categories-10K<n<100K #language-English #Customer Reviews #Second-Hand #Product Reviews #Comments #Text Data #region-us \n" ]

[ 112 ]

[ "passage: TAGS\n#task_categories-text-classification #task_categories-question-answering #task_categories-summarization #task_categories-feature-extraction #task_categories-conversational #task_categories-sentence-similarity #size_categories-10K<n<100K #language-English #Customer Reviews #Second-Hand #Product Reviews #Comments #Text Data #region-us \n" ]

[ "task_categories:question-answering", "language:ms", "region:us" ]

[]

[ "ms" ]

[ "# Filtered CohereForAI/aya_dataset on zsm language\n\nOriginally from URL filter rows on 'zsm' language only." ]

[ "TAGS\n#task_categories-question-answering #language-Malay (macrolanguage) #region-us \n", "# Filtered CohereForAI/aya_dataset on zsm language\n\nOriginally from URL filter rows on 'zsm' language only." ]

[ 28, 34 ]

[ "passage: TAGS\n#task_categories-question-answering #language-Malay (macrolanguage) #region-us \n# Filtered CohereForAI/aya_dataset on zsm language\n\nOriginally from URL filter rows on 'zsm' language only." ]

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

[]

[]

[]

[ "TAGS\n#license-cc-by-4.0 #region-us \n" ]

[ 15 ]

[ "passage: TAGS\n#license-cc-by-4.0 #region-us \n" ]

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

[]

[ "ms" ]

[ "# DPO Binarized filtered-aya_dataset-zsm\n\nDPO binarized style using filtered URL on 'zsm' language only, after that we use URL to generate the outputs and the generated outputs use 'rejected' column.\n\nRead more about DPO binarized style dataset at URL" ]

[ "TAGS\n#language-Malay (macrolanguage) #region-us \n", "# DPO Binarized filtered-aya_dataset-zsm\n\nDPO binarized style using filtered URL on 'zsm' language only, after that we use URL to generate the outputs and the generated outputs use 'rejected' column.\n\nRead more about DPO binarized style dataset at URL" ]

[ 16, 74 ]

[ "passage: TAGS\n#language-Malay (macrolanguage) #region-us \n# DPO Binarized filtered-aya_dataset-zsm\n\nDPO binarized style using filtered URL on 'zsm' language only, after that we use URL to generate the outputs and the generated outputs use 'rejected' column.\n\nRead more about DPO binarized style dataset at URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Himitsui/Kaiju-11B\n\n\n\nDataset automatically created during the evaluation run of model Himitsui/Kaiju-11B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T14:10:39.178663(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 Himitsui/Kaiju-11B\n\n\n\nDataset automatically created during the evaluation run of model Himitsui/Kaiju-11B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T14:10:39.178663(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" ]

[ 6, 177, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Himitsui/Kaiju-11B\n\n\n\nDataset automatically created during the evaluation run of model Himitsui/Kaiju-11B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T14:10:39.178663(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 s3nh/poorx32124\n\n\n\nDataset automatically created during the evaluation run of model s3nh/poorx32124 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T14:37:56.380929(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 s3nh/poorx32124\n\n\n\nDataset automatically created during the evaluation run of model s3nh/poorx32124 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T14:37:56.380929(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" ]

[ 6, 179, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of s3nh/poorx32124\n\n\n\nDataset automatically created during the evaluation run of model s3nh/poorx32124 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T14:37:56.380929(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 TW3PartnersLLM/TW3-v1-AlpacaSmaug-30B\n\n\n\nDataset automatically created during the evaluation run of model TW3PartnersLLM/TW3-v1-AlpacaSmaug-30B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T14:34:36.455085(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 TW3PartnersLLM/TW3-v1-AlpacaSmaug-30B\n\n\n\nDataset automatically created during the evaluation run of model TW3PartnersLLM/TW3-v1-AlpacaSmaug-30B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T14:34:36.455085(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" ]

[ 6, 201, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of TW3PartnersLLM/TW3-v1-AlpacaSmaug-30B\n\n\n\nDataset automatically created during the evaluation run of model TW3PartnersLLM/TW3-v1-AlpacaSmaug-30B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T14:34:36.455085(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]" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of shuvom/yuj-v1\n\n\n\nDataset automatically created during the evaluation run of model shuvom/yuj-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 2024-02-13T14:45:38.399135(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 shuvom/yuj-v1\n\n\n\nDataset automatically created during the evaluation run of model shuvom/yuj-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 2024-02-13T14:45:38.399135(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" ]

[ 6, 177, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of shuvom/yuj-v1\n\n\n\nDataset automatically created during the evaluation run of model shuvom/yuj-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 2024-02-13T14:45:38.399135(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "language:en", "license:cc-by-4.0", "chemistry", "molecule", "small molecule", "instructions", "arxiv:2402.09391", "region:us" ]

[ "2402.09391" ]

[ "en" ]

[ "## Overview\n\nThe following figure illustrates the tasks and corresponding examples.\n\n!Overview of the tasks.\n\nThe following table shows the tasks and statistics over the SMolInstruct dataset, where “Qry.” and “Resp.” are average lengths of queries and responses, respectively.\n\n!Statistics of the SMolInstruct dataset.\n\nAn example is shown below:", "## ️ Usage\nYou can use the following lines to load the dataset:\n\n\nYou can also specify what tasks to load:", "## ️ Data Construction\n\nThe construction of SMolInstruct goes through a four-step pipeline: \n\n- data collection: Collect data from various sources and organize it for the tasks.\n- quality control: Rigorous scrutiny is applied to remove samples with chemically invalid SMILES and wrong or inaccurate information, as well as duplicated samples.\n- data splitting: Samples are carefully splitted into train/validation/test set to avoid data leakage across tasks. Also, the splitting is compatible with previous work to faciliate fair comparison.\n- instruction construction: We create natural and diverse templates for creating instructions. Molecular SMILES representations are canonicalized to provide a standardized data format. In addition, we use special tags to encapsulate corresponding segments (e.g., <SMILES>...</SMILES>} for SMILES, etc.) to promote model learning during training and faciliate answer extraction during inference.", "## License\n\nThe SMolInstruct dataset is licensed under CC BY 4.0.\n\nWe emphatically urge all users to adhere to the highest ethical standards when using our dataset, including maintaining fairness, transparency, and responsibility in their research. Any usage of the dataset that may lead to harm or pose a detriment to society is strictly forbidden.", "## Citation\nIf our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries.\n\n\n\nThank you for your interest in our work." ]

[ "TAGS\n#language-English #license-cc-by-4.0 #chemistry #molecule #small molecule #instructions #arxiv-2402.09391 #region-us \n", "## Overview\n\nThe following figure illustrates the tasks and corresponding examples.\n\n!Overview of the tasks.\n\nThe following table shows the tasks and statistics over the SMolInstruct dataset, where “Qry.” and “Resp.” are average lengths of queries and responses, respectively.\n\n!Statistics of the SMolInstruct dataset.\n\nAn example is shown below:", "## ️ Usage\nYou can use the following lines to load the dataset:\n\n\nYou can also specify what tasks to load:", "## ️ Data Construction\n\nThe construction of SMolInstruct goes through a four-step pipeline: \n\n- data collection: Collect data from various sources and organize it for the tasks.\n- quality control: Rigorous scrutiny is applied to remove samples with chemically invalid SMILES and wrong or inaccurate information, as well as duplicated samples.\n- data splitting: Samples are carefully splitted into train/validation/test set to avoid data leakage across tasks. Also, the splitting is compatible with previous work to faciliate fair comparison.\n- instruction construction: We create natural and diverse templates for creating instructions. Molecular SMILES representations are canonicalized to provide a standardized data format. In addition, we use special tags to encapsulate corresponding segments (e.g., <SMILES>...</SMILES>} for SMILES, etc.) to promote model learning during training and faciliate answer extraction during inference.", "## License\n\nThe SMolInstruct dataset is licensed under CC BY 4.0.\n\nWe emphatically urge all users to adhere to the highest ethical standards when using our dataset, including maintaining fairness, transparency, and responsibility in their research. Any usage of the dataset that may lead to harm or pose a detriment to society is strictly forbidden.", "## Citation\nIf our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries.\n\n\n\nThank you for your interest in our work." ]

[ 43, 87, 28, 217, 81, 44 ]

[ "passage: TAGS\n#language-English #license-cc-by-4.0 #chemistry #molecule #small molecule #instructions #arxiv-2402.09391 #region-us \n## Overview\n\nThe following figure illustrates the tasks and corresponding examples.\n\n!Overview of the tasks.\n\nThe following table shows the tasks and statistics over the SMolInstruct dataset, where “Qry.” and “Resp.” are average lengths of queries and responses, respectively.\n\n!Statistics of the SMolInstruct dataset.\n\nAn example is shown below:## ️ Usage\nYou can use the following lines to load the dataset:\n\n\nYou can also specify what tasks to load:## ️ Data Construction\n\nThe construction of SMolInstruct goes through a four-step pipeline: \n\n- data collection: Collect data from various sources and organize it for the tasks.\n- quality control: Rigorous scrutiny is applied to remove samples with chemically invalid SMILES and wrong or inaccurate information, as well as duplicated samples.\n- data splitting: Samples are carefully splitted into train/validation/test set to avoid data leakage across tasks. Also, the splitting is compatible with previous work to faciliate fair comparison.\n- instruction construction: We create natural and diverse templates for creating instructions. Molecular SMILES representations are canonicalized to provide a standardized data format. In addition, we use special tags to encapsulate corresponding segments (e.g., <SMILES>...</SMILES>} for SMILES, etc.) to promote model learning during training and faciliate answer extraction during inference.## License\n\nThe SMolInstruct dataset is licensed under CC BY 4.0.\n\nWe emphatically urge all users to adhere to the highest ethical standards when using our dataset, including maintaining fairness, transparency, and responsibility in their research. Any usage of the dataset that may lead to harm or pose a detriment to society is strictly forbidden.## Citation\nIf our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries.\n\n\n\nThank you for your interest in our work." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of nlpguy/AlloyIngotNeo\n\n\n\nDataset automatically created during the evaluation run of model nlpguy/AlloyIngotNeo on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T14:49:47.237954(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 nlpguy/AlloyIngotNeo\n\n\n\nDataset automatically created during the evaluation run of model nlpguy/AlloyIngotNeo on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T14:49:47.237954(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" ]

[ 6, 185, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of nlpguy/AlloyIngotNeo\n\n\n\nDataset automatically created during the evaluation run of model nlpguy/AlloyIngotNeo on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T14:49:47.237954(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 \"5k-govreport-4096\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"5k-govreport-4096\"\n\nMore Information needed" ]

[ 6, 17 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"5k-govreport-4096\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Twitter Neighbours dataset\n\nThis repository contains the dataset assembled as part of a APPRAISE project (H2020-SU-SEC-2020H2020-SU-SEC-2020)", "# Description\nThe dataset contains a sampled graph, extracted from Twitter, starting from a list of seed users.\nEach user is initially represented by a semantic embedding (vector) computed as the average text embedding of a sample of its tweets, while users are connected with each other through the 'following/follower' property on Twitter.", "# Statistics\n- number of users: 36122\n- number of edges: 84026\n- user initial embeddings size: 768", "# Files:\ngraph_train_and_test.pygeodata: is a compact representation of the graph for PyG usage.\n\ntwitter_neighs_graph.json: a dictionary containing:\n - 'adj_sparse': adjacency matrix in sparse representation\n - 'user_init_embs': initial user embedding, computed as average of text embedding of user tweets\n - 'train_test_split': 0/1 1D list, where 0 represents the user is in teh training set and 1 the user is in the test set" ]

[ "TAGS\n#region-us \n", "# Twitter Neighbours dataset\n\nThis repository contains the dataset assembled as part of a APPRAISE project (H2020-SU-SEC-2020H2020-SU-SEC-2020)", "# Description\nThe dataset contains a sampled graph, extracted from Twitter, starting from a list of seed users.\nEach user is initially represented by a semantic embedding (vector) computed as the average text embedding of a sample of its tweets, while users are connected with each other through the 'following/follower' property on Twitter.", "# Statistics\n- number of users: 36122\n- number of edges: 84026\n- user initial embeddings size: 768", "# Files:\ngraph_train_and_test.pygeodata: is a compact representation of the graph for PyG usage.\n\ntwitter_neighs_graph.json: a dictionary containing:\n - 'adj_sparse': adjacency matrix in sparse representation\n - 'user_init_embs': initial user embedding, computed as average of text embedding of user tweets\n - 'train_test_split': 0/1 1D list, where 0 represents the user is in teh training set and 1 the user is in the test set" ]

[ 6, 46, 83, 30, 138 ]

[ "passage: TAGS\n#region-us \n# Twitter Neighbours dataset\n\nThis repository contains the dataset assembled as part of a APPRAISE project (H2020-SU-SEC-2020H2020-SU-SEC-2020)# Description\nThe dataset contains a sampled graph, extracted from Twitter, starting from a list of seed users.\nEach user is initially represented by a semantic embedding (vector) computed as the average text embedding of a sample of its tweets, while users are connected with each other through the 'following/follower' property on Twitter.# Statistics\n- number of users: 36122\n- number of edges: 84026\n- user initial embeddings size: 768# Files:\ngraph_train_and_test.pygeodata: is a compact representation of the graph for PyG usage.\n\ntwitter_neighs_graph.json: a dictionary containing:\n - 'adj_sparse': adjacency matrix in sparse representation\n - 'user_init_embs': initial user embedding, computed as average of text embedding of user tweets\n - 'train_test_split': 0/1 1D list, where 0 represents the user is in teh training set and 1 the user is in the test set" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Test_Dataset_0213_hf\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Test_Dataset_0213_hf\"\n\nMore Information needed" ]

[ 6, 20 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"Test_Dataset_0213_hf\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of nlpguy/AlloyIngot\n\n\n\nDataset automatically created during the evaluation run of model nlpguy/AlloyIngot on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T14:57:48.240090(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 nlpguy/AlloyIngot\n\n\n\nDataset automatically created during the evaluation run of model nlpguy/AlloyIngot on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T14:57:48.240090(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" ]

[ 6, 181, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of nlpguy/AlloyIngot\n\n\n\nDataset automatically created during the evaluation run of model nlpguy/AlloyIngot on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T14:57:48.240090(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

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

[]

[ "en" ]

[ "# Wikipedia Dump with Gold Documents from Natural Questions", "## Dataset Summary\nThis dataset combines the English Wikipedia dump from December 20, 2018, with gold passages from the Natural Questions (NQ) dataset, \nspecifically tailored for open-domain question answering tasks. By integrating gold documents corresponding to each query in the NQ-open\nversion of the dataset, this resource addresses potential mismatches between the Wikipedia dump and the question-answer pairs found in NQ-open. \nSuch mismatches can lead to scenarios where the dump does not contain the required answer. \nA thorough process of duplicate filtering was applied to ensure the precise identification of the gold document for each query, \nenhancing the reliability of the dataset for natural language processing tasks.\n\n\nTherefore, the dataset can be employed as a knowledge base for RAG systems.\nOne critical aspect of dataset preparation involved addressing the constraints posed by Large Language Models (LLMs) regarding input size. \nLLMs, particularly when processing multiple documents in a single prompt, face limitations on the length of input they can efficiently handle. \nTo accommodate this, gold documents exceeding 512 tokens (tokenized with Llama2)\nwere excluded from the dataset. This decision was guided by the objective of maximizing the number of documents that can be included in the LLM's prompt \nwithout compromising on the detail or context provided by each document. \nAs a result, the final dataset encompasses 21,035,236 documents (13.9 GB).", "## Dataset Sources\n\n- Original Wikipedia Dump: The corpus originates from the English Wikipedia dump, where articles are segmented into non-overlapping passages of 100 words.\nDownload link.\n\n- Gold Passages: Sourced from the Natural Questions dataset, these passages are integrated to provide a comprehensive resource for question answering.\nThe gold passages are accessible through the following URLs:\n - train\n - dev\n - test\n\nThe above data comes from the Dense Passage Retrieval (DPR) github repository.", "## Dataset Structure\n\nAn example of a Wikipedia passage is as follows:" ]

[ "TAGS\n#task_categories-question-answering #size_categories-10M<n<100M #language-English #region-us \n", "# Wikipedia Dump with Gold Documents from Natural Questions", "## Dataset Summary\nThis dataset combines the English Wikipedia dump from December 20, 2018, with gold passages from the Natural Questions (NQ) dataset, \nspecifically tailored for open-domain question answering tasks. By integrating gold documents corresponding to each query in the NQ-open\nversion of the dataset, this resource addresses potential mismatches between the Wikipedia dump and the question-answer pairs found in NQ-open. \nSuch mismatches can lead to scenarios where the dump does not contain the required answer. \nA thorough process of duplicate filtering was applied to ensure the precise identification of the gold document for each query, \nenhancing the reliability of the dataset for natural language processing tasks.\n\n\nTherefore, the dataset can be employed as a knowledge base for RAG systems.\nOne critical aspect of dataset preparation involved addressing the constraints posed by Large Language Models (LLMs) regarding input size. \nLLMs, particularly when processing multiple documents in a single prompt, face limitations on the length of input they can efficiently handle. \nTo accommodate this, gold documents exceeding 512 tokens (tokenized with Llama2)\nwere excluded from the dataset. This decision was guided by the objective of maximizing the number of documents that can be included in the LLM's prompt \nwithout compromising on the detail or context provided by each document. \nAs a result, the final dataset encompasses 21,035,236 documents (13.9 GB).", "## Dataset Sources\n\n- Original Wikipedia Dump: The corpus originates from the English Wikipedia dump, where articles are segmented into non-overlapping passages of 100 words.\nDownload link.\n\n- Gold Passages: Sourced from the Natural Questions dataset, these passages are integrated to provide a comprehensive resource for question answering.\nThe gold passages are accessible through the following URLs:\n - train\n - dev\n - test\n\nThe above data comes from the Dense Passage Retrieval (DPR) github repository.", "## Dataset Structure\n\nAn example of a Wikipedia passage is as follows:" ]

[ 34, 12, 333, 114, 17 ]

[ "passage: TAGS\n#task_categories-question-answering #size_categories-10M<n<100M #language-English #region-us \n# Wikipedia Dump with Gold Documents from Natural Questions## Dataset Summary\nThis dataset combines the English Wikipedia dump from December 20, 2018, with gold passages from the Natural Questions (NQ) dataset, \nspecifically tailored for open-domain question answering tasks. By integrating gold documents corresponding to each query in the NQ-open\nversion of the dataset, this resource addresses potential mismatches between the Wikipedia dump and the question-answer pairs found in NQ-open. \nSuch mismatches can lead to scenarios where the dump does not contain the required answer. \nA thorough process of duplicate filtering was applied to ensure the precise identification of the gold document for each query, \nenhancing the reliability of the dataset for natural language processing tasks.\n\n\nTherefore, the dataset can be employed as a knowledge base for RAG systems.\nOne critical aspect of dataset preparation involved addressing the constraints posed by Large Language Models (LLMs) regarding input size. \nLLMs, particularly when processing multiple documents in a single prompt, face limitations on the length of input they can efficiently handle. \nTo accommodate this, gold documents exceeding 512 tokens (tokenized with Llama2)\nwere excluded from the dataset. This decision was guided by the objective of maximizing the number of documents that can be included in the LLM's prompt \nwithout compromising on the detail or context provided by each document. \nAs a result, the final dataset encompasses 21,035,236 documents (13.9 GB).## Dataset Sources\n\n- Original Wikipedia Dump: The corpus originates from the English Wikipedia dump, where articles are segmented into non-overlapping passages of 100 words.\nDownload link.\n\n- Gold Passages: Sourced from the Natural Questions dataset, these passages are integrated to provide a comprehensive resource for question answering.\nThe gold passages are accessible through the following URLs:\n - train\n - dev\n - test\n\nThe above data comes from the Dense Passage Retrieval (DPR) github repository." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of bardsai/jaskier-7b-dpo-v3.3\n\n\n\nDataset automatically created during the evaluation run of model bardsai/jaskier-7b-dpo-v3.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T15:49:58.893408(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 bardsai/jaskier-7b-dpo-v3.3\n\n\n\nDataset automatically created during the evaluation run of model bardsai/jaskier-7b-dpo-v3.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T15:49:58.893408(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" ]

[ 6, 191, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of bardsai/jaskier-7b-dpo-v3.3\n\n\n\nDataset automatically created during the evaluation run of model bardsai/jaskier-7b-dpo-v3.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T15:49:58.893408(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]" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of DatPySci/pythia-1b-sft-full\n\n\n\nDataset automatically created during the evaluation run of model DatPySci/pythia-1b-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 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T16:10:25.536341(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 DatPySci/pythia-1b-sft-full\n\n\n\nDataset automatically created during the evaluation run of model DatPySci/pythia-1b-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 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T16:10:25.536341(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" ]

[ 6, 191, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of DatPySci/pythia-1b-sft-full\n\n\n\nDataset automatically created during the evaluation run of model DatPySci/pythia-1b-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 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T16:10:25.536341(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]" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "task_categories:text-generation", "task_ids:language-modeling", "source_datasets:original", "language:fa", "region:us" ]

[]

[ "fa" ]

[ "# Persian Blog", "# Dataset Summary\n\npersian_news_dataset is a collection of 400k blog posts. these posts have been gathered from more than 10 websites. This dataset can be used in different NLP tasks like language modeling and text generation tasks.\n\nThis effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: 'free' and 'easy-to-use'. Here is a quick HOW-TO for using this dataset in datasets library:Demo-datasets", "# Description\n\nAs discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset:" ]

[ "TAGS\n#task_categories-text-generation #task_ids-language-modeling #source_datasets-original #language-Persian #region-us \n", "# Persian Blog", "# Dataset Summary\n\npersian_news_dataset is a collection of 400k blog posts. these posts have been gathered from more than 10 websites. This dataset can be used in different NLP tasks like language modeling and text generation tasks.\n\nThis effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: 'free' and 'easy-to-use'. Here is a quick HOW-TO for using this dataset in datasets library:Demo-datasets", "# Description\n\nAs discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset:" ]

[ 40, 4, 123, 39 ]

[ "passage: TAGS\n#task_categories-text-generation #task_ids-language-modeling #source_datasets-original #language-Persian #region-us \n# Persian Blog# Dataset Summary\n\npersian_news_dataset is a collection of 400k blog posts. these posts have been gathered from more than 10 websites. This dataset can be used in different NLP tasks like language modeling and text generation tasks.\n\nThis effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: 'free' and 'easy-to-use'. Here is a quick HOW-TO for using this dataset in datasets library:Demo-datasets# Description\n\nAs discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset:" ]

[ "task_categories:text-generation", "size_categories:1K<n<10K", "license:afl-3.0", "medical", "region:us" ]

[]

[]

[ "# Dataset Card for Transcript to SOAP\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- Curated by: RevMaxx Team\n- Language(s) (NLP): English", "#### Personal and Sensitive Information\n\nThe dataset contains sensitive information of patient-doctor conversation.", "## Dataset Card Authors\n\n- Team RevMaxx" ]

[ "TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #license-afl-3.0 #medical #region-us \n", "# Dataset Card for Transcript to SOAP\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- Curated by: RevMaxx Team\n- Language(s) (NLP): English", "#### Personal and Sensitive Information\n\nThe dataset contains sensitive information of patient-doctor conversation.", "## Dataset Card Authors\n\n- Team RevMaxx" ]

[ 40, 36, 4, 24, 22, 11 ]

[ "passage: TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #license-afl-3.0 #medical #region-us \n# Dataset Card for Transcript to SOAP\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- Curated by: RevMaxx Team\n- Language(s) (NLP): English#### Personal and Sensitive Information\n\nThe dataset contains sensitive information of patient-doctor conversation.## Dataset Card Authors\n\n- Team RevMaxx" ]

[ "task_categories:graph-ml", "size_categories:1K<n<10K", "license:cc", "arxiv:2210.02016", "region:us" ]

[ "2210.02016" ]

[]

[]

[ "TAGS\n#task_categories-graph-ml #size_categories-1K<n<10K #license-cc #arxiv-2210.02016 #region-us \n" ]

[ 41 ]

[ "passage: TAGS\n#task_categories-graph-ml #size_categories-1K<n<10K #license-cc #arxiv-2210.02016 #region-us \n" ]

[ "task_categories:text-classification", "task_categories:translation", "task_categories:summarization", "size_categories:1M<n<10M", "language:ar", "license:apache-2.0", "arxiv:2402.06619", "region:us" ]

[ "2402.06619" ]

[ "ar" ]

[ "### Overview\n\n\n'Arabic Aya' is a meticulously curated dataset derived from the comprehensive Aya collection by CohereForAI, specifically focusing on Arabic text data. This dataset aggregates content from the CohereForAI/aya\\_collection, CohereForAI/aya\\_dataset, and CohereForAI/aya\\_evaluation\\_suite, filtering out all but the Arabic content, including both Modern Standard Arabic (MSA) and various regional dialects.", "### Purpose\n\n\nThe aim of 'Arabic Aya' is to provide researchers, technologists, and linguists with a ready-to-use Arabic text resource, significantly reducing the time and effort required for data preprocessing in NLP and AI projects focused on the Arabic language.\n\n\n* Use the Aya datasets out of the box for your Arabic applications and research", "### Dataset Sources & Infos\n\n\n* Data Origin: Derived from 69 subsets of the original Aya datasets : CohereForAI/aya\\_collection, CohereForAI/aya\\_dataset, and CohereForAI/aya\\_evaluation\\_suite.\n* Languages: Modern Standard Arabic (MSA) and a variety of Arabic dialects ( 'arb', 'arz', 'ary', 'ars', 'knc', 'acm', 'apc', 'aeb', 'ajp', 'acq' )\n* Applications: Ideal for tasks such as language modeling, text classification, sentiment analysis, dialect identification, and machine translation.\n* Paper: 2402.06619\n* Maintainer: Elfilali Ali\n* License: Apache-2.0", "### Usage\n\n\nThis dataset serves as a foundational tool for those embarking on Arabic language projects, from academic research to commercial applications. By providing a pre-filtered source of Arabic text, 'Arabic Aya' enables users to dive straight into model training, analysis, and application development without the preliminary hassle of data cleaning and language filtering.", "#### Use with HF Datasets library\n\n\nTo load this dataset with Datasets, you'll need to install Datasets as 'pip install datasets --upgrade' and then use a similar code to the following:\n\n\nIn the above code snippet, \"CohereForAI-aya\\_collection-templated\\_mintaka\" refers to the arabic version (100k rows) of the original \"templated\\_mintaka\" subset (780k rows) of the aya\\_collection. You can load other subsets by specifying its name at the time of loading the dataset.", "### Access and Contribution\n\n\nAvailable on the Hugging Face Hub under 2A2I/Arabic\\_Aya, 'Arabic Aya' invites contributions from the community. Users are encouraged to offer feedback, suggest improvements.", "### Support and Collaboration\n\n\nWe are committed to fostering an inclusive and supportive environment around Arabic AI and NLP research. For support, collaboration, or queries regarding the dataset, please reach out through the Hugging Face Hub's discussion section or reach out at 2A2I Contact Email.\n\n\nOriginal Dataset Card of Aya by CohereForAI\n===========================================\n\n\n!Aya Header\n\n\nDataset Summary\n===============\n\n\nThe Aya Collection is a massive multilingual collection consisting of 513 million instances of prompts and completions covering a wide range of tasks.\nThis collection incorporates instruction-style templates from fluent speakers and applies them to a curated list of datasets, as well as translations of instruction-style datasets into 101 languages. Aya Dataset, a human-curated multilingual instruction and response dataset, is also part of this collection. See our paper for more details regarding the collection.\n\n\n* Curated by: Contributors of Aya Open Science Intiative\n* Language(s): 115 languages\n* License: Apache 2.0\n* Aya Datasets Family:\n| Name | Explanation |\n|------|--------------|\n| aya\\_dataset | Human-annotated multilingual instruction finetuning dataset, comprising over 204K instances across 65 languages. |\n| aya\\_collection | Created by applying instruction-style templates from fluent speakers to 44 datasets, including translations of 19 instruction-style datasets into 101 languages.|\n| aya\\_evaluation\\_suite | A diverse evaluation set for multilingual open-ended generation, featuring 250 culturally grounded prompts in 7 languages, 200 translated prompts in 24 languages, and human-edited versions selected for cross-cultural relevance from English Dolly in 6 languages.|\n\n\nDataset\n=======\n\n\nThe 'Aya Collection' is a comprehensive, large corpus of datasets that can be used by researchers around the world to train multilingual models. Our goal is only to include datasets with permissive licensing for manipulation and redistribution.\n\n\nThe 'Aya Collection' consists of three different sources of data:\n\n\n1. Templated data: We collaborated with fluent speakers to create templates that allowed for the automatic expansion of existing datasets into various languages.\n2. Translated data: We translated a hand-selected subset of 19 datasets into 101 languages (114 dialects) using the NLLB 3.3B parameter machine translation model.\n3. Aya Dataset: We release the Aya Dataset as a subset of the overall collection. This is the only dataset in the collection that is human-annotated in its entirety.\n\n\nLoad with Datasets\n------------------\n\n\nTo load this dataset with Datasets, you'll need to install Datasets as 'pip install datasets --upgrade' and then use the following code:\n\n\nIn the above code snippet, \"templated\\_mintaka\" refers to a subset of the aya\\_collection. You can load other subsets by specifying its name at the time of loading the dataset.\n\n\nData Instances\n--------------\n\n\nAn example of a 'train' instance looks as follows:\n\n\nData Fields\n-----------\n\n\nThe data fields are the same among all splits:\n\n\n* 'id:' Unique id of the data point\n* 'inputs:' Prompt or input to the language model.\n* 'targets:' Completion or output of the language model.\n* 'dataset\\_name:' The name of the source dataset that the data point was taken from\n* 'sub\\_dataset\\_name:' If the source is a collection, this field indicates which part of that collection the data point was taken from. If it is not a collection, this field is left blank.\n* 'task\\_type:' The task type that this conversation belongs to.\n* 'template\\_id': The id of the template applied to this data point.\n* 'language:' The ISO code of the dialect of the conversation.\n* 'script:' The script of the language.\n* 'split:' Indicates whether the data point is part of the 'train' or the 'test' split.", "### Statistics\n\n\nThe total number of data points, including the Aya Dataset' is 513,758,189. To view the breakdown of dialect codes and the respective templated and translated data point counts in the Aya Collection , refer to the toggled table below.\n\n\n\n **Breakdown of Aya Collection data point counts grouped by dialects** \n\nPS: Templated data also includes Mozambican Portuguese, which doesn't have its own ISO language code.\n\n\n\n \n\nMotivations & Intentions\n========================\n\n\n* Curation Rationale: Automatic augmentation of existing datasets serves to enhance the available linguistic resources for multiple languages. The list of languages was initially established from mT5 and aligned with the annotators’ language list and NLLB translation model. The datasets were translated directly from English for all languages.\n\n\nAdditional Information\n======================\n\n\nProvenance\n----------\n\n\n* Methods Used: A combination of crowd-sourced templating and automatic translation was employed to source this dataset.\n* Methodology Details:\n\t+ *Source:* Existing NLP datasets\n\t+ *Dates of Collection:* May 2023 - Dec 2023\n\n\nDataset Version and Maintenance\n-------------------------------\n\n\n* Maintenance Status: Actively Maintained\n* Version Details:\n\t+ *Current version:* 1.0\n\t+ *Last Update:* 02/2024\n\t+ *First Release:* 02/2024\n\n\nAuthorship\n----------\n\n\n* Publishing Organization: Cohere For AI\n* Industry Type: Not-for-profit - Tech\n* Contact Details: URL\n\n\nLicensing Information\n---------------------\n\n\nThis dataset can be used for any purpose, whether academic or commercial, under the terms of the Apache 2.0 License." ]

[ "TAGS\n#task_categories-text-classification #task_categories-translation #task_categories-summarization #size_categories-1M<n<10M #language-Arabic #license-apache-2.0 #arxiv-2402.06619 #region-us \n", "### Overview\n\n\n'Arabic Aya' is a meticulously curated dataset derived from the comprehensive Aya collection by CohereForAI, specifically focusing on Arabic text data. This dataset aggregates content from the CohereForAI/aya\\_collection, CohereForAI/aya\\_dataset, and CohereForAI/aya\\_evaluation\\_suite, filtering out all but the Arabic content, including both Modern Standard Arabic (MSA) and various regional dialects.", "### Purpose\n\n\nThe aim of 'Arabic Aya' is to provide researchers, technologists, and linguists with a ready-to-use Arabic text resource, significantly reducing the time and effort required for data preprocessing in NLP and AI projects focused on the Arabic language.\n\n\n* Use the Aya datasets out of the box for your Arabic applications and research", "### Dataset Sources & Infos\n\n\n* Data Origin: Derived from 69 subsets of the original Aya datasets : CohereForAI/aya\\_collection, CohereForAI/aya\\_dataset, and CohereForAI/aya\\_evaluation\\_suite.\n* Languages: Modern Standard Arabic (MSA) and a variety of Arabic dialects ( 'arb', 'arz', 'ary', 'ars', 'knc', 'acm', 'apc', 'aeb', 'ajp', 'acq' )\n* Applications: Ideal for tasks such as language modeling, text classification, sentiment analysis, dialect identification, and machine translation.\n* Paper: 2402.06619\n* Maintainer: Elfilali Ali\n* License: Apache-2.0", "### Usage\n\n\nThis dataset serves as a foundational tool for those embarking on Arabic language projects, from academic research to commercial applications. By providing a pre-filtered source of Arabic text, 'Arabic Aya' enables users to dive straight into model training, analysis, and application development without the preliminary hassle of data cleaning and language filtering.", "#### Use with HF Datasets library\n\n\nTo load this dataset with Datasets, you'll need to install Datasets as 'pip install datasets --upgrade' and then use a similar code to the following:\n\n\nIn the above code snippet, \"CohereForAI-aya\\_collection-templated\\_mintaka\" refers to the arabic version (100k rows) of the original \"templated\\_mintaka\" subset (780k rows) of the aya\\_collection. You can load other subsets by specifying its name at the time of loading the dataset.", "### Access and Contribution\n\n\nAvailable on the Hugging Face Hub under 2A2I/Arabic\\_Aya, 'Arabic Aya' invites contributions from the community. Users are encouraged to offer feedback, suggest improvements.", "### Support and Collaboration\n\n\nWe are committed to fostering an inclusive and supportive environment around Arabic AI and NLP research. For support, collaboration, or queries regarding the dataset, please reach out through the Hugging Face Hub's discussion section or reach out at 2A2I Contact Email.\n\n\nOriginal Dataset Card of Aya by CohereForAI\n===========================================\n\n\n!Aya Header\n\n\nDataset Summary\n===============\n\n\nThe Aya Collection is a massive multilingual collection consisting of 513 million instances of prompts and completions covering a wide range of tasks.\nThis collection incorporates instruction-style templates from fluent speakers and applies them to a curated list of datasets, as well as translations of instruction-style datasets into 101 languages. Aya Dataset, a human-curated multilingual instruction and response dataset, is also part of this collection. See our paper for more details regarding the collection.\n\n\n* Curated by: Contributors of Aya Open Science Intiative\n* Language(s): 115 languages\n* License: Apache 2.0\n* Aya Datasets Family:\n| Name | Explanation |\n|------|--------------|\n| aya\\_dataset | Human-annotated multilingual instruction finetuning dataset, comprising over 204K instances across 65 languages. |\n| aya\\_collection | Created by applying instruction-style templates from fluent speakers to 44 datasets, including translations of 19 instruction-style datasets into 101 languages.|\n| aya\\_evaluation\\_suite | A diverse evaluation set for multilingual open-ended generation, featuring 250 culturally grounded prompts in 7 languages, 200 translated prompts in 24 languages, and human-edited versions selected for cross-cultural relevance from English Dolly in 6 languages.|\n\n\nDataset\n=======\n\n\nThe 'Aya Collection' is a comprehensive, large corpus of datasets that can be used by researchers around the world to train multilingual models. Our goal is only to include datasets with permissive licensing for manipulation and redistribution.\n\n\nThe 'Aya Collection' consists of three different sources of data:\n\n\n1. Templated data: We collaborated with fluent speakers to create templates that allowed for the automatic expansion of existing datasets into various languages.\n2. Translated data: We translated a hand-selected subset of 19 datasets into 101 languages (114 dialects) using the NLLB 3.3B parameter machine translation model.\n3. Aya Dataset: We release the Aya Dataset as a subset of the overall collection. This is the only dataset in the collection that is human-annotated in its entirety.\n\n\nLoad with Datasets\n------------------\n\n\nTo load this dataset with Datasets, you'll need to install Datasets as 'pip install datasets --upgrade' and then use the following code:\n\n\nIn the above code snippet, \"templated\\_mintaka\" refers to a subset of the aya\\_collection. You can load other subsets by specifying its name at the time of loading the dataset.\n\n\nData Instances\n--------------\n\n\nAn example of a 'train' instance looks as follows:\n\n\nData Fields\n-----------\n\n\nThe data fields are the same among all splits:\n\n\n* 'id:' Unique id of the data point\n* 'inputs:' Prompt or input to the language model.\n* 'targets:' Completion or output of the language model.\n* 'dataset\\_name:' The name of the source dataset that the data point was taken from\n* 'sub\\_dataset\\_name:' If the source is a collection, this field indicates which part of that collection the data point was taken from. If it is not a collection, this field is left blank.\n* 'task\\_type:' The task type that this conversation belongs to.\n* 'template\\_id': The id of the template applied to this data point.\n* 'language:' The ISO code of the dialect of the conversation.\n* 'script:' The script of the language.\n* 'split:' Indicates whether the data point is part of the 'train' or the 'test' split.", "### Statistics\n\n\nThe total number of data points, including the Aya Dataset' is 513,758,189. To view the breakdown of dialect codes and the respective templated and translated data point counts in the Aya Collection , refer to the toggled table below.\n\n\n\n **Breakdown of Aya Collection data point counts grouped by dialects** \n\nPS: Templated data also includes Mozambican Portuguese, which doesn't have its own ISO language code.\n\n\n\n \n\nMotivations & Intentions\n========================\n\n\n* Curation Rationale: Automatic augmentation of existing datasets serves to enhance the available linguistic resources for multiple languages. The list of languages was initially established from mT5 and aligned with the annotators’ language list and NLLB translation model. The datasets were translated directly from English for all languages.\n\n\nAdditional Information\n======================\n\n\nProvenance\n----------\n\n\n* Methods Used: A combination of crowd-sourced templating and automatic translation was employed to source this dataset.\n* Methodology Details:\n\t+ *Source:* Existing NLP datasets\n\t+ *Dates of Collection:* May 2023 - Dec 2023\n\n\nDataset Version and Maintenance\n-------------------------------\n\n\n* Maintenance Status: Actively Maintained\n* Version Details:\n\t+ *Current version:* 1.0\n\t+ *Last Update:* 02/2024\n\t+ *First Release:* 02/2024\n\n\nAuthorship\n----------\n\n\n* Publishing Organization: Cohere For AI\n* Industry Type: Not-for-profit - Tech\n* Contact Details: URL\n\n\nLicensing Information\n---------------------\n\n\nThis dataset can be used for any purpose, whether academic or commercial, under the terms of the Apache 2.0 License." ]

[ 70, 112, 80, 185, 80, 144, 53, 977, 379 ]

[ "passage: TAGS\n#task_categories-text-classification #task_categories-translation #task_categories-summarization #size_categories-1M<n<10M #language-Arabic #license-apache-2.0 #arxiv-2402.06619 #region-us \n### Overview\n\n\n'Arabic Aya' is a meticulously curated dataset derived from the comprehensive Aya collection by CohereForAI, specifically focusing on Arabic text data. This dataset aggregates content from the CohereForAI/aya\\_collection, CohereForAI/aya\\_dataset, and CohereForAI/aya\\_evaluation\\_suite, filtering out all but the Arabic content, including both Modern Standard Arabic (MSA) and various regional dialects.### Purpose\n\n\nThe aim of 'Arabic Aya' is to provide researchers, technologists, and linguists with a ready-to-use Arabic text resource, significantly reducing the time and effort required for data preprocessing in NLP and AI projects focused on the Arabic language.\n\n\n* Use the Aya datasets out of the box for your Arabic applications and research### Dataset Sources & Infos\n\n\n* Data Origin: Derived from 69 subsets of the original Aya datasets : CohereForAI/aya\\_collection, CohereForAI/aya\\_dataset, and CohereForAI/aya\\_evaluation\\_suite.\n* Languages: Modern Standard Arabic (MSA) and a variety of Arabic dialects ( 'arb', 'arz', 'ary', 'ars', 'knc', 'acm', 'apc', 'aeb', 'ajp', 'acq' )\n* Applications: Ideal for tasks such as language modeling, text classification, sentiment analysis, dialect identification, and machine translation.\n* Paper: 2402.06619\n* Maintainer: Elfilali Ali\n* License: Apache-2.0", "passage: ### Usage\n\n\nThis dataset serves as a foundational tool for those embarking on Arabic language projects, from academic research to commercial applications. By providing a pre-filtered source of Arabic text, 'Arabic Aya' enables users to dive straight into model training, analysis, and application development without the preliminary hassle of data cleaning and language filtering.#### Use with HF Datasets library\n\n\nTo load this dataset with Datasets, you'll need to install Datasets as 'pip install datasets --upgrade' and then use a similar code to the following:\n\n\nIn the above code snippet, \"CohereForAI-aya\\_collection-templated\\_mintaka\" refers to the arabic version (100k rows) of the original \"templated\\_mintaka\" subset (780k rows) of the aya\\_collection. You can load other subsets by specifying its name at the time of loading the dataset.### Access and Contribution\n\n\nAvailable on the Hugging Face Hub under 2A2I/Arabic\\_Aya, 'Arabic Aya' invites contributions from the community. Users are encouraged to offer feedback, suggest improvements." ]

[ "task_categories:text-classification", "task_categories:text-generation", "task_ids:language-modeling", "task_ids:multi-class-classification", "source_datasets:original", "language:fa", "region:us" ]

[]

[ "fa" ]

[ "# Persian_News_Dataset", "# Dataset Summary\n\npersian_news_dataset is a collection of 5 million news articles. News articles have been gathered from more than 10 news agencies for the last 12 years. This dataset can be used in different NLP tasks like language modeling, classification, supervised topic modeling,...\n\nThis effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: 'free' and 'easy-to-use'. Here is a quick HOW-TO for using this dataset in datasets library:Demo-datasets", "# Description\n\nAs discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset:" ]

[ "TAGS\n#task_categories-text-classification #task_categories-text-generation #task_ids-language-modeling #task_ids-multi-class-classification #source_datasets-original #language-Persian #region-us \n", "# Persian_News_Dataset", "# Dataset Summary\n\npersian_news_dataset is a collection of 5 million news articles. News articles have been gathered from more than 10 news agencies for the last 12 years. This dataset can be used in different NLP tasks like language modeling, classification, supervised topic modeling,...\n\nThis effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: 'free' and 'easy-to-use'. Here is a quick HOW-TO for using this dataset in datasets library:Demo-datasets", "# Description\n\nAs discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset:" ]

[ 63, 8, 135, 39 ]

[ "passage: TAGS\n#task_categories-text-classification #task_categories-text-generation #task_ids-language-modeling #task_ids-multi-class-classification #source_datasets-original #language-Persian #region-us \n# Persian_News_Dataset# Dataset Summary\n\npersian_news_dataset is a collection of 5 million news articles. News articles have been gathered from more than 10 news agencies for the last 12 years. This dataset can be used in different NLP tasks like language modeling, classification, supervised topic modeling,...\n\nThis effort is part of a bigger perspective to have several datasets in Persian language for different tasks that have two important factors: 'free' and 'easy-to-use'. Here is a quick HOW-TO for using this dataset in datasets library:Demo-datasets# Description\n\nAs discussed before, this dataset contains 5M news articles. Each article has these three attributes: text, title, category. Here is a sample of dataset:" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 16 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"PocketMarkedDataset\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 16 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"CASFPocketMarked\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 15 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"FSPocketMarked\"\n\nMore Information needed" ]

[ "task_categories:token-classification", "language:fr", "license:cc-by-nc-4.0", "spacy", "region:us" ]

[]

[ "fr" ]

[ "### Dataset Summary\n\n\nThis dataset contains semantic annotations (at the token and span levels) for named entities (such as Spatial, Person, and MISC), nominal entities, as well as nested named entities, spatial relations, and other relevant information within French encyclopedic entries.\n\n\nThe span tagset is as follows:\n\n\n* NC-Spatial: a common noun that identifies a spatial entity (nominal spatial entity) including natural features, e.g. 'ville', 'la rivière', 'royaume'.\n* NP-Spatial: a proper noun identifying the name of a place (spatial named entities), e.g. 'France', 'Paris', 'la Chine'.\n* ENE-Spatial: nested spatial entity , e.g. 'ville de France', 'royaume de Naples', 'la mer Baltique'.\n* Relation: spatial relation, e.g. 'dans', 'sur', 'à 10 lieues de'.\n* Latlong: geographic coordinates, e.g. 'Long. 19. 49. lat. 43. 55. 44.'\n* NC-Person: a common noun that identifies a person (nominal spatial entity), e.g. 'roi', 'l'empereur', 'les auteurs'.\n* NP-Person: a proper noun identifying the name of a person (person named entities), e.g. 'Louis XIV', 'Pline', 'les Romains'.\n* ENE-Person: nested people entity, e.g. 'le czar Pierre', 'roi de Macédoine'\n* NP-Misc: a proper noun identifying entities not classified as spatial or person, e.g. 'l'Eglise', '1702', 'Pélasgique'.\n* ENE-Misc: nested named entity not classified as spatial or person, e.g. 'l'ordre de S. Jacques', 'la déclaration du 21 Mars 1671'.\n* Head: entry name\n* Domain-Mark: words indicating the knowledge domain (usually after the head and between parenthesis), e.g. 'Géographie', 'Geog.', 'en Anatomie'.", "### Supported Tasks\n\n\n* 'token-classification' or 'span-classification': The dataset can be used to train a model for 'token-classification' or 'span-classification'.\nIt is more specifically designed for spatial role labelling. A spacy custom spancat model is available at: URL\n\n\nDataset Structure\n-----------------\n\n\nThe dataset is provided as JSONL files[^1] where each row follows the following structure:\n\n\nEach data contains four main fields:\n\n\n* 'text': plain text of a paragraph.\n* 'meta': metadata from the ARTFL Encyclopédie about the paragraph such volume, article, paragraph id, headword, etc.\n* 'tokens': list of tokens, with their text, id, start and end position at the character level.\n* 'spans': list of spans (i.e., annotations), with their text, label, start and end position at the character level.\n\n\n[^1]:spaCy binary files are also available on the Github and Zenodo repositories.", "### Data Splits\n\n\nThe dataset consists of 2200 paragraphs randomly selected out of 2001 Encyclopédie's entries.\nAll paragraphs were written in French and are distributed as follows among the Encyclopédie knowledge domains:\n\n\n\nThe spans/entities were labeled by the project team along with using pre-labelling with early models to speed up the labelling process.\nA train/val/test split was used.\nValidation and test sets are composed of 200 paragraphs each: 100 classified as 'Géographie' and 100 from another knowledge domain.\nThe datasets have the following breakdown of tokens and spans/entities.\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nList of people involved in annotating the dataset:\n\n\n* Ludovic Moncla (@ludovicmoncla), INSA Lyon, CNRS, LIRIS UMR 5205\n* Katherine McDonough (@kmcdono2, Lancaster University & The Alan Turing Institute", "### Acknowledgement\n\n\nThe authors are grateful to the ASLAN project (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program \"Investments for the Future\" operated by the National Research Agency (ANR).\nData courtesy the ARTFL Encyclopédie Project, University of Chicago." ]

[ "TAGS\n#task_categories-token-classification #language-French #license-cc-by-nc-4.0 #spacy #region-us \n", "### Dataset Summary\n\n\nThis dataset contains semantic annotations (at the token and span levels) for named entities (such as Spatial, Person, and MISC), nominal entities, as well as nested named entities, spatial relations, and other relevant information within French encyclopedic entries.\n\n\nThe span tagset is as follows:\n\n\n* NC-Spatial: a common noun that identifies a spatial entity (nominal spatial entity) including natural features, e.g. 'ville', 'la rivière', 'royaume'.\n* NP-Spatial: a proper noun identifying the name of a place (spatial named entities), e.g. 'France', 'Paris', 'la Chine'.\n* ENE-Spatial: nested spatial entity , e.g. 'ville de France', 'royaume de Naples', 'la mer Baltique'.\n* Relation: spatial relation, e.g. 'dans', 'sur', 'à 10 lieues de'.\n* Latlong: geographic coordinates, e.g. 'Long. 19. 49. lat. 43. 55. 44.'\n* NC-Person: a common noun that identifies a person (nominal spatial entity), e.g. 'roi', 'l'empereur', 'les auteurs'.\n* NP-Person: a proper noun identifying the name of a person (person named entities), e.g. 'Louis XIV', 'Pline', 'les Romains'.\n* ENE-Person: nested people entity, e.g. 'le czar Pierre', 'roi de Macédoine'\n* NP-Misc: a proper noun identifying entities not classified as spatial or person, e.g. 'l'Eglise', '1702', 'Pélasgique'.\n* ENE-Misc: nested named entity not classified as spatial or person, e.g. 'l'ordre de S. Jacques', 'la déclaration du 21 Mars 1671'.\n* Head: entry name\n* Domain-Mark: words indicating the knowledge domain (usually after the head and between parenthesis), e.g. 'Géographie', 'Geog.', 'en Anatomie'.", "### Supported Tasks\n\n\n* 'token-classification' or 'span-classification': The dataset can be used to train a model for 'token-classification' or 'span-classification'.\nIt is more specifically designed for spatial role labelling. A spacy custom spancat model is available at: URL\n\n\nDataset Structure\n-----------------\n\n\nThe dataset is provided as JSONL files[^1] where each row follows the following structure:\n\n\nEach data contains four main fields:\n\n\n* 'text': plain text of a paragraph.\n* 'meta': metadata from the ARTFL Encyclopédie about the paragraph such volume, article, paragraph id, headword, etc.\n* 'tokens': list of tokens, with their text, id, start and end position at the character level.\n* 'spans': list of spans (i.e., annotations), with their text, label, start and end position at the character level.\n\n\n[^1]:spaCy binary files are also available on the Github and Zenodo repositories.", "### Data Splits\n\n\nThe dataset consists of 2200 paragraphs randomly selected out of 2001 Encyclopédie's entries.\nAll paragraphs were written in French and are distributed as follows among the Encyclopédie knowledge domains:\n\n\n\nThe spans/entities were labeled by the project team along with using pre-labelling with early models to speed up the labelling process.\nA train/val/test split was used.\nValidation and test sets are composed of 200 paragraphs each: 100 classified as 'Géographie' and 100 from another knowledge domain.\nThe datasets have the following breakdown of tokens and spans/entities.\n\n\n\nAdditional Information\n----------------------", "### Dataset Curators\n\n\nList of people involved in annotating the dataset:\n\n\n* Ludovic Moncla (@ludovicmoncla), INSA Lyon, CNRS, LIRIS UMR 5205\n* Katherine McDonough (@kmcdono2, Lancaster University & The Alan Turing Institute", "### Acknowledgement\n\n\nThe authors are grateful to the ASLAN project (ANR-10-LABX-0081) of the Université de Lyon, for its financial support within the French program \"Investments for the Future\" operated by the National Research Agency (ANR).\nData courtesy the ARTFL Encyclopédie Project, University of Chicago." ]

[ 38, 553, 240, 152, 62, 75 ]

[ "passage: TAGS\n#task_categories-token-classification #language-French #license-cc-by-nc-4.0 #spacy #region-us \n", "passage: ### Dataset Summary\n\n\nThis dataset contains semantic annotations (at the token and span levels) for named entities (such as Spatial, Person, and MISC), nominal entities, as well as nested named entities, spatial relations, and other relevant information within French encyclopedic entries.\n\n\nThe span tagset is as follows:\n\n\n* NC-Spatial: a common noun that identifies a spatial entity (nominal spatial entity) including natural features, e.g. 'ville', 'la rivière', 'royaume'.\n* NP-Spatial: a proper noun identifying the name of a place (spatial named entities), e.g. 'France', 'Paris', 'la Chine'.\n* ENE-Spatial: nested spatial entity , e.g. 'ville de France', 'royaume de Naples', 'la mer Baltique'.\n* Relation: spatial relation, e.g. 'dans', 'sur', 'à 10 lieues de'.\n* Latlong: geographic coordinates, e.g. 'Long. 19. 49. lat. 43. 55. 44.'\n* NC-Person: a common noun that identifies a person (nominal spatial entity), e.g. 'roi', 'l'empereur', 'les auteurs'.\n* NP-Person: a proper noun identifying the name of a person (person named entities), e.g. 'Louis XIV', 'Pline', 'les Romains'.\n* ENE-Person: nested people entity, e.g. 'le czar Pierre', 'roi de Macédoine'\n* NP-Misc: a proper noun identifying entities not classified as spatial or person, e.g. 'l'Eglise', '1702', 'Pélasgique'.\n* ENE-Misc: nested named entity not classified as spatial or person, e.g. 'l'ordre de S. Jacques', 'la déclaration du 21 Mars 1671'.\n* Head: entry name\n* Domain-Mark: words indicating the knowledge domain (usually after the head and between parenthesis), e.g. 'Géographie', 'Geog.', 'en Anatomie'.### Supported Tasks\n\n\n* 'token-classification' or 'span-classification': The dataset can be used to train a model for 'token-classification' or 'span-classification'.\nIt is more specifically designed for spatial role labelling. A spacy custom spancat model is available at: URL\n\n\nDataset Structure\n-----------------\n\n\nThe dataset is provided as JSONL files[^1] where each row follows the following structure:\n\n\nEach data contains four main fields:\n\n\n* 'text': plain text of a paragraph.\n* 'meta': metadata from the ARTFL Encyclopédie about the paragraph such volume, article, paragraph id, headword, etc.\n* 'tokens': list of tokens, with their text, id, start and end position at the character level.\n* 'spans': list of spans (i.e., annotations), with their text, label, start and end position at the character level.\n\n\n[^1]:spaCy binary files are also available on the Github and Zenodo repositories.### Data Splits\n\n\nThe dataset consists of 2200 paragraphs randomly selected out of 2001 Encyclopédie's entries.\nAll paragraphs were written in French and are distributed as follows among the Encyclopédie knowledge domains:\n\n\n\nThe spans/entities were labeled by the project team along with using pre-labelling with early models to speed up the labelling process.\nA train/val/test split was used.\nValidation and test sets are composed of 200 paragraphs each: 100 classified as 'Géographie' and 100 from another knowledge domain.\nThe datasets have the following breakdown of tokens and spans/entities.\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\nList of people involved in annotating the dataset:\n\n\n* Ludovic Moncla (@ludovicmoncla), INSA Lyon, CNRS, LIRIS UMR 5205\n* Katherine McDonough (@kmcdono2, Lancaster University & The Alan Turing Institute" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of DreadPoor/ToppyLake-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model DreadPoor/ToppyLake-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T17:37:50.313114(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 DreadPoor/ToppyLake-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model DreadPoor/ToppyLake-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T17:37:50.313114(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" ]

[ 6, 189, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of DreadPoor/ToppyLake-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model DreadPoor/ToppyLake-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T17:37:50.313114(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]" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Big-Dataset-0214\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Big-Dataset-0214\"\n\nMore Information needed" ]

[ 6, 16 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"Big-Dataset-0214\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5\n\n\n\nDataset automatically created during the evaluation run of model arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T17:48:37.130833(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 arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5\n\n\n\nDataset automatically created during the evaluation run of model arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T17:48:37.130833(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" ]

[ 6, 205, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5\n\n\n\nDataset automatically created during the evaluation run of model arlineka/Brunhilde-2x7b-MOE-DPO-v.01.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T17:48:37.130833(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]" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"stenotype-eval-type-stripped\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"stenotype-eval-type-stripped\"\n\nMore Information needed" ]

[ 6, 21 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"stenotype-eval-type-stripped\"\n\nMore Information needed" ]

[ "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "# Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI)", "## Project Overview\n\nThe Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI) is committed to utilizing the Trees & Planting Sites dataset for a comprehensive geospatial analysis of Durham's urban tree canopy. Through Python within Google Colab, our aim is to identify key locations for canopy expansion, evaluate the impact of urban development on green spaces, and deliver informed recommendations for the sustainable growth of urban tree coverage.", "## Background and Rationale\n\nDurham's urban tree canopy is a crucial component that contributes to environmental quality, public health, and overall city aesthetics. This canopy is under threat due to ongoing urban development and natural wear. A systematic, data-driven approach is critical for strategic planning and conservation of the urban forest to ensure its vitality for generations to come.", "## Data Sources and Methodology", "### Data Sources\n\nWe will leverage the following files from the Durham Trees & Planting Sites Dataset, as found on the Durham Open Data portal:\n\n- 'GS_TreeInventory.shp'\n- 'Trees_&_Planting_Sites.csv'\n- 'Trees_%26_Planting_Sites.geojson'", "# Dataset Card for Urban Tree Inventory", "## Dataset Description\n\nThis dataset provides comprehensive information about urban trees within a specified area, including their physical characteristics, environmental benefits, and the economic value they add in terms of ecosystem services.", "### Spatial Data (GeoJSON)\n\nFormat: GeoJSON\n\nContent:\n\n- Type: 'FeatureCollection' - A collection of feature objects.\n- Features: Each feature object represents a tree and contains:\n - Type: 'Feature'\n - Geometry: 'Point' (includes longitude and latitude of the tree location).\n - Properties: Detailed information about the tree (some fields may overlap with the CSV structure below).", "### Tabular Data (CSV)\n\nFormat: CSV\n\nColumns:\n\n- X, Y: Coordinates of the tree location.\n- OBJECTID: Unique identifier for the tree.\n- streetaddress: Street address nearest to the tree.\n- city: City where the tree is located.\n- zipcode: Zip code for the location of the tree.\n- facilityid: Identifier for the facility associated with the tree, if any.\n- present: Indication of whether the tree is currently present.\n- genus, species, commonname: Botanical and common names of the tree.\n- plantingdate: Date when the tree was planted.\n- diameterin: Diameter of the tree trunk in inches.\n- heightft: Height of the tree in feet.\n- condition: Health condition of the tree.\n- contractwork: Indicates if the tree has had any contract work done.\n- neighborhood: Neighborhood where the tree is located.\n- program: The program under which the tree was planted.\n- plantingw: Width of the planting site.\n- plantingcond: Condition of the planting site.\n- underpwerlins: Whether the tree is under power lines.\n- matureheight: The mature height of the tree.\n- GlobalID: A global unique identifier for the tree.\n- created_user: The user who created the record.\n- created_date: The date the record was created.\n- last_edited_user: The user who last edited the record.\n- last_edited_date: The date the record was last edited.", "#### Environmental and Economic Data:\n\n- isoprene, monoterpene, vocs: Emissions and absorption data for various compounds.\n- coremoved_ozperyr, o3removed_ozperyr, etc.: Annual pollutant removal metrics.\n- o2production_lbperyr: Annual oxygen production.\n- carbonstorage_lb, carbonstorage_dol: Carbon storage metrics.\n- grosscarseq_lbperyr, grosscarseq_dolperyr: Gross carbon sequestration.\n- avoidrunoff_ft2peryr, avoidrunoff_dol2peryr: Metrics related to stormwater runoff avoidance.\n- totannbenefits_dolperyr: Total annual dollar benefits from the tree.\n- leafarea_sqft, potevapotran_cuftperyr, etc.: Metrics related to the water cycle.\n- heating_mbtuperyr, cooling_kwhperyr, etc.: Energy savings related to the tree's impact on building energy use.", "### Example Record\n\nGeoJSON Feature:\n\nThe 'GS_TreeInventory.shp' file encompasses a range of attributes for each record:\n\n- OBJECTID: Unique identifier for each record.\n- streetaddr: Street address where the tree or planting site is located.\n- city: The city name, which is Durham.\n- zipcode: Postal code for the location.\n- facilityid: Identifier possibly linked to a facility or area associated with the tree.\n- present: Type of feature present, such as a tree or a planting site.\n- genus: Genus of the tree.\n- species: Species of the tree.\n- commonname: Common name of the tree.\n- plantingda: Date or year range when the tree was planted or the planting site was established.\n- ...", "### Objectives\n\n1. Combine Shapefile and CSV data into a comprehensive geospatial dataset using Python.\n2. Apply Python libraries to uncover relationships between tree canopy data and urban development.\n3. Provide practical insights and strategies for the expansion of Durham's urban tree canopy.\n4. Produce analyses and visualizations with the GeoJSON file.", "### Methodology\n\nOur analytical process within Google Colab will encompass:\n\n- Data Preparation and Integration: Using tools like Geopandas, Pandas, and PyShp to organize and combine spatial and tabular data.\n- Geospatial Analysis: Applying Shapely and Rtree for spatial analysis, and using SciPy or Statsmodels for statistical correlations.\n- Visualization and Optimization: Generating maps and graphs with Matplotlib, Seaborn, or Plotly, and utilizing optimization algorithms to suggest optimal planting locations.", "## Deliverables\n\n1. A collection of Google Colab Python notebooks that outline our analytical processes.\n2. Interactive maps and visualizations that connect tree canopy coverage with urban development metrics.\n3. An exhaustive report that contains our findings and recommendations for enhancing the urban canopy.", "## Limitations\n\n- Computational Resources: The limited computational offerings of Google Colab may pose a challenge to the size of the datasets or the complexity of models we can employ.\n- Data Quality: The accuracy and currency of the data ultimately affect the precision of our recommendations.\n- Sociopolitical Considerations: Implementation of our data-driven suggestions must be reviewed within the context of local policy and community input.", "## Conclusion\n\nDUCAEI aims to create a more verdant and livable urban landscape in Durham through this Python-based analytical project. By laying a strong foundation for data-informed decision-making, we hope to cultivate a thriving, green, and sustainable urban environment." ]

[ "TAGS\n#size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n", "# Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI)", "## Project Overview\n\nThe Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI) is committed to utilizing the Trees & Planting Sites dataset for a comprehensive geospatial analysis of Durham's urban tree canopy. Through Python within Google Colab, our aim is to identify key locations for canopy expansion, evaluate the impact of urban development on green spaces, and deliver informed recommendations for the sustainable growth of urban tree coverage.", "## Background and Rationale\n\nDurham's urban tree canopy is a crucial component that contributes to environmental quality, public health, and overall city aesthetics. This canopy is under threat due to ongoing urban development and natural wear. A systematic, data-driven approach is critical for strategic planning and conservation of the urban forest to ensure its vitality for generations to come.", "## Data Sources and Methodology", "### Data Sources\n\nWe will leverage the following files from the Durham Trees & Planting Sites Dataset, as found on the Durham Open Data portal:\n\n- 'GS_TreeInventory.shp'\n- 'Trees_&_Planting_Sites.csv'\n- 'Trees_%26_Planting_Sites.geojson'", "# Dataset Card for Urban Tree Inventory", "## Dataset Description\n\nThis dataset provides comprehensive information about urban trees within a specified area, including their physical characteristics, environmental benefits, and the economic value they add in terms of ecosystem services.", "### Spatial Data (GeoJSON)\n\nFormat: GeoJSON\n\nContent:\n\n- Type: 'FeatureCollection' - A collection of feature objects.\n- Features: Each feature object represents a tree and contains:\n - Type: 'Feature'\n - Geometry: 'Point' (includes longitude and latitude of the tree location).\n - Properties: Detailed information about the tree (some fields may overlap with the CSV structure below).", "### Tabular Data (CSV)\n\nFormat: CSV\n\nColumns:\n\n- X, Y: Coordinates of the tree location.\n- OBJECTID: Unique identifier for the tree.\n- streetaddress: Street address nearest to the tree.\n- city: City where the tree is located.\n- zipcode: Zip code for the location of the tree.\n- facilityid: Identifier for the facility associated with the tree, if any.\n- present: Indication of whether the tree is currently present.\n- genus, species, commonname: Botanical and common names of the tree.\n- plantingdate: Date when the tree was planted.\n- diameterin: Diameter of the tree trunk in inches.\n- heightft: Height of the tree in feet.\n- condition: Health condition of the tree.\n- contractwork: Indicates if the tree has had any contract work done.\n- neighborhood: Neighborhood where the tree is located.\n- program: The program under which the tree was planted.\n- plantingw: Width of the planting site.\n- plantingcond: Condition of the planting site.\n- underpwerlins: Whether the tree is under power lines.\n- matureheight: The mature height of the tree.\n- GlobalID: A global unique identifier for the tree.\n- created_user: The user who created the record.\n- created_date: The date the record was created.\n- last_edited_user: The user who last edited the record.\n- last_edited_date: The date the record was last edited.", "#### Environmental and Economic Data:\n\n- isoprene, monoterpene, vocs: Emissions and absorption data for various compounds.\n- coremoved_ozperyr, o3removed_ozperyr, etc.: Annual pollutant removal metrics.\n- o2production_lbperyr: Annual oxygen production.\n- carbonstorage_lb, carbonstorage_dol: Carbon storage metrics.\n- grosscarseq_lbperyr, grosscarseq_dolperyr: Gross carbon sequestration.\n- avoidrunoff_ft2peryr, avoidrunoff_dol2peryr: Metrics related to stormwater runoff avoidance.\n- totannbenefits_dolperyr: Total annual dollar benefits from the tree.\n- leafarea_sqft, potevapotran_cuftperyr, etc.: Metrics related to the water cycle.\n- heating_mbtuperyr, cooling_kwhperyr, etc.: Energy savings related to the tree's impact on building energy use.", "### Example Record\n\nGeoJSON Feature:\n\nThe 'GS_TreeInventory.shp' file encompasses a range of attributes for each record:\n\n- OBJECTID: Unique identifier for each record.\n- streetaddr: Street address where the tree or planting site is located.\n- city: The city name, which is Durham.\n- zipcode: Postal code for the location.\n- facilityid: Identifier possibly linked to a facility or area associated with the tree.\n- present: Type of feature present, such as a tree or a planting site.\n- genus: Genus of the tree.\n- species: Species of the tree.\n- commonname: Common name of the tree.\n- plantingda: Date or year range when the tree was planted or the planting site was established.\n- ...", "### Objectives\n\n1. Combine Shapefile and CSV data into a comprehensive geospatial dataset using Python.\n2. Apply Python libraries to uncover relationships between tree canopy data and urban development.\n3. Provide practical insights and strategies for the expansion of Durham's urban tree canopy.\n4. Produce analyses and visualizations with the GeoJSON file.", "### Methodology\n\nOur analytical process within Google Colab will encompass:\n\n- Data Preparation and Integration: Using tools like Geopandas, Pandas, and PyShp to organize and combine spatial and tabular data.\n- Geospatial Analysis: Applying Shapely and Rtree for spatial analysis, and using SciPy or Statsmodels for statistical correlations.\n- Visualization and Optimization: Generating maps and graphs with Matplotlib, Seaborn, or Plotly, and utilizing optimization algorithms to suggest optimal planting locations.", "## Deliverables\n\n1. A collection of Google Colab Python notebooks that outline our analytical processes.\n2. Interactive maps and visualizations that connect tree canopy coverage with urban development metrics.\n3. An exhaustive report that contains our findings and recommendations for enhancing the urban canopy.", "## Limitations\n\n- Computational Resources: The limited computational offerings of Google Colab may pose a challenge to the size of the datasets or the complexity of models we can employ.\n- Data Quality: The accuracy and currency of the data ultimately affect the precision of our recommendations.\n- Sociopolitical Considerations: Implementation of our data-driven suggestions must be reviewed within the context of local policy and community input.", "## Conclusion\n\nDUCAEI aims to create a more verdant and livable urban landscape in Durham through this Python-based analytical project. By laying a strong foundation for data-informed decision-making, we hope to cultivate a thriving, green, and sustainable urban environment." ]

[ 30, 20, 108, 86, 7, 83, 10, 43, 105, 343, 235, 182, 86, 135, 69, 96, 63 ]

[ "passage: TAGS\n#size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n# Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI)## Project Overview\n\nThe Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI) is committed to utilizing the Trees & Planting Sites dataset for a comprehensive geospatial analysis of Durham's urban tree canopy. Through Python within Google Colab, our aim is to identify key locations for canopy expansion, evaluate the impact of urban development on green spaces, and deliver informed recommendations for the sustainable growth of urban tree coverage.## Background and Rationale\n\nDurham's urban tree canopy is a crucial component that contributes to environmental quality, public health, and overall city aesthetics. This canopy is under threat due to ongoing urban development and natural wear. A systematic, data-driven approach is critical for strategic planning and conservation of the urban forest to ensure its vitality for generations to come.## Data Sources and Methodology### Data Sources\n\nWe will leverage the following files from the Durham Trees & Planting Sites Dataset, as found on the Durham Open Data portal:\n\n- 'GS_TreeInventory.shp'\n- 'Trees_&_Planting_Sites.csv'\n- 'Trees_%26_Planting_Sites.geojson'# Dataset Card for Urban Tree Inventory## Dataset Description\n\nThis dataset provides comprehensive information about urban trees within a specified area, including their physical characteristics, environmental benefits, and the economic value they add in terms of ecosystem services.### Spatial Data (GeoJSON)\n\nFormat: GeoJSON\n\nContent:\n\n- Type: 'FeatureCollection' - A collection of feature objects.\n- Features: Each feature object represents a tree and contains:\n - Type: 'Feature'\n - Geometry: 'Point' (includes longitude and latitude of the tree location).\n - Properties: Detailed information about the tree (some fields may overlap with the CSV structure below).", "passage: ### Tabular Data (CSV)\n\nFormat: CSV\n\nColumns:\n\n- X, Y: Coordinates of the tree location.\n- OBJECTID: Unique identifier for the tree.\n- streetaddress: Street address nearest to the tree.\n- city: City where the tree is located.\n- zipcode: Zip code for the location of the tree.\n- facilityid: Identifier for the facility associated with the tree, if any.\n- present: Indication of whether the tree is currently present.\n- genus, species, commonname: Botanical and common names of the tree.\n- plantingdate: Date when the tree was planted.\n- diameterin: Diameter of the tree trunk in inches.\n- heightft: Height of the tree in feet.\n- condition: Health condition of the tree.\n- contractwork: Indicates if the tree has had any contract work done.\n- neighborhood: Neighborhood where the tree is located.\n- program: The program under which the tree was planted.\n- plantingw: Width of the planting site.\n- plantingcond: Condition of the planting site.\n- underpwerlins: Whether the tree is under power lines.\n- matureheight: The mature height of the tree.\n- GlobalID: A global unique identifier for the tree.\n- created_user: The user who created the record.\n- created_date: The date the record was created.\n- last_edited_user: The user who last edited the record.\n- last_edited_date: The date the record was last edited.#### Environmental and Economic Data:\n\n- isoprene, monoterpene, vocs: Emissions and absorption data for various compounds.\n- coremoved_ozperyr, o3removed_ozperyr, etc.: Annual pollutant removal metrics.\n- o2production_lbperyr: Annual oxygen production.\n- carbonstorage_lb, carbonstorage_dol: Carbon storage metrics.\n- grosscarseq_lbperyr, grosscarseq_dolperyr: Gross carbon sequestration.\n- avoidrunoff_ft2peryr, avoidrunoff_dol2peryr: Metrics related to stormwater runoff avoidance.\n- totannbenefits_dolperyr: Total annual dollar benefits from the tree.\n- leafarea_sqft, potevapotran_cuftperyr, etc.: Metrics related to the water cycle.\n- heating_mbtuperyr, cooling_kwhperyr, etc.: Energy savings related to the tree's impact on building energy use.### Example Record\n\nGeoJSON Feature:\n\nThe 'GS_TreeInventory.shp' file encompasses a range of attributes for each record:\n\n- OBJECTID: Unique identifier for each record.\n- streetaddr: Street address where the tree or planting site is located.\n- city: The city name, which is Durham.\n- zipcode: Postal code for the location.\n- facilityid: Identifier possibly linked to a facility or area associated with the tree.\n- present: Type of feature present, such as a tree or a planting site.\n- genus: Genus of the tree.\n- species: Species of the tree.\n- commonname: Common name of the tree.\n- plantingda: Date or year range when the tree was planted or the planting site was established.\n- ...### Objectives\n\n1. Combine Shapefile and CSV data into a comprehensive geospatial dataset using Python.\n2. Apply Python libraries to uncover relationships between tree canopy data and urban development.\n3. Provide practical insights and strategies for the expansion of Durham's urban tree canopy.\n4. Produce analyses and visualizations with the GeoJSON file." ]

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

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[]

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

[ 14 ]

[ "passage: TAGS\n#license-apache-2.0 #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of DreadPoor/BagelToppyLake-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model DreadPoor/BagelToppyLake-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T19:00:17.803854(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 DreadPoor/BagelToppyLake-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model DreadPoor/BagelToppyLake-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T19:00:17.803854(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" ]

[ 6, 193, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of DreadPoor/BagelToppyLake-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model DreadPoor/BagelToppyLake-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T19:00:17.803854(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]" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of andysalerno/rainbowfish-7B-v10\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/rainbowfish-7B-v10 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T19:38:34.787963(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 andysalerno/rainbowfish-7B-v10\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/rainbowfish-7B-v10 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T19:38:34.787963(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" ]

[ 6, 187, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of andysalerno/rainbowfish-7B-v10\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/rainbowfish-7B-v10 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T19:38:34.787963(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 \"samantar_merged_with_train_val\"\n\nMore Information needed" ]

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

[ 6, 22 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"samantar_merged_with_train_val\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of FelixChao/Cygnus-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/Cygnus-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T19:53:00.035091(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/Cygnus-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/Cygnus-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T19:53:00.035091(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" ]

[ 6, 179, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of FelixChao/Cygnus-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/Cygnus-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T19:53:00.035091(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:zero-shot-classification", "license:mit", "region:us" ]

[]

[]

[]

[ "TAGS\n#task_categories-zero-shot-classification #license-mit #region-us \n" ]

[ 24 ]

[ "passage: TAGS\n#task_categories-zero-shot-classification #license-mit #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Technoculture/mtor\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/mtor on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T20:28:47.252845(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/mtor\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/mtor on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T20:28:47.252845(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" ]

[ 6, 171, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Technoculture/mtor\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/mtor on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T20:28:47.252845(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 \"ShareMix-chatML\"\n\nMore Information needed" ]

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

[ 6, 16 ]

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

[ "arxiv:2402.10208", "region:us" ]

[ "2402.10208" ]

[]

[ "### Data Subsets\n\n\nThe table below describes the dataset subsets in detail:", "### Data Fields\n\n\nAs described above, each row of the dataset represents a single layer that should be recovered and contains the following fields:\n\n\n\n```\ntask_name - The name of the task the model was fine-tuned on (subset).\nlayer_model - In some cases a Pre-FT model has more than one model (e.g., Stable Diffusion fine-tuned both \n the UNet and the Text Encoder). This field specifies the model the layer belongs to.\nlayer_name - The name of the layer in the Pre-FT model as it appears in the model state_dict.\npre_ft_name - The name of the Pre-FT model (e.g., runwayml/stable-diffusion-v1-5).\npre_ft_weight - The weight matrix of the Pre-FT models layer. \nlora_{lora_idx}_name - The name of the LoRA fine-tuned model.\nlora_{lora_idx}_A_weight - The LoRA A weight matrix of the LoRA fine-tuned models layer.\nlora_{lora_idx}_B_weight - The LoRA B weight matrix of the LoRA fine-tuned models layer.\nlora_{lora_idx}_rank - The LoRA rank of the LoRA fine-tuned models layer.\nlora_{lora_idx}_alpha - The LoRA alpha of the LoRA fine-tuned models layer.\n\n```\n\nwhere '{lora\\_idx}' is the index of the LoRA fine-tuned model in the subset (there are 15 LoRA models per subset).", "### Layer Merging Example\n\n\nThe following code snippet demonstrates merging the LoRA fine-tuned weights with the Pre-FT weights.\n\n\nDataset Creation\n----------------", "### Source Data\n\n\n* The fine-tuning of the ViT models was performed using the PEFT library\non various datasets from the VTAB-1K benchmark.\n* The fine-tuned LoRA models for Stable Diffusion are taken from civitai and were fine-tuned by RalFinger.\n* The fine-tuning of Mistral was performed based on the Zephyr model as seen here.\n\n\nFor the full list of models and hyper-parameters see the appendix of the paper.\n\n\nRisks and Out-of-Scope Use\n--------------------------\n\n\nOur work uncovers a significant vulnerability in fine-tuned models, allowing attackers to\naccess pre-fine-tuning weights. While this discovery reveals potential security risks,\nour primary objective is to advance the field of Machine Learning and raise awareness within the\nresearch community about the existing vulnerabilities in current models.\n\n\nInstead of using the findings of this study to execute attacks, we advocate for their use by\nmodel creators to enhance the safety and security of their models. By acknowledging and\naddressing vulnerabilities, creators can proactively safeguard against potential threats.\n\n\nFollowing established practices in the cyber-security community, we emphasize the importance of open\ndiscussion and encourage the reporting of vulnerabilities. By fostering transparency and collaboration,\nwe can collectively create a safer environment for deploying machine learning models.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Licensing Information\n\n\nIf you use this dataset in your work please cite the following paper:\n\n\nBibTeX:" ]

[ "TAGS\n#arxiv-2402.10208 #region-us \n", "### Data Subsets\n\n\nThe table below describes the dataset subsets in detail:", "### Data Fields\n\n\nAs described above, each row of the dataset represents a single layer that should be recovered and contains the following fields:\n\n\n\n```\ntask_name - The name of the task the model was fine-tuned on (subset).\nlayer_model - In some cases a Pre-FT model has more than one model (e.g., Stable Diffusion fine-tuned both \n the UNet and the Text Encoder). This field specifies the model the layer belongs to.\nlayer_name - The name of the layer in the Pre-FT model as it appears in the model state_dict.\npre_ft_name - The name of the Pre-FT model (e.g., runwayml/stable-diffusion-v1-5).\npre_ft_weight - The weight matrix of the Pre-FT models layer. \nlora_{lora_idx}_name - The name of the LoRA fine-tuned model.\nlora_{lora_idx}_A_weight - The LoRA A weight matrix of the LoRA fine-tuned models layer.\nlora_{lora_idx}_B_weight - The LoRA B weight matrix of the LoRA fine-tuned models layer.\nlora_{lora_idx}_rank - The LoRA rank of the LoRA fine-tuned models layer.\nlora_{lora_idx}_alpha - The LoRA alpha of the LoRA fine-tuned models layer.\n\n```\n\nwhere '{lora\\_idx}' is the index of the LoRA fine-tuned model in the subset (there are 15 LoRA models per subset).", "### Layer Merging Example\n\n\nThe following code snippet demonstrates merging the LoRA fine-tuned weights with the Pre-FT weights.\n\n\nDataset Creation\n----------------", "### Source Data\n\n\n* The fine-tuning of the ViT models was performed using the PEFT library\non various datasets from the VTAB-1K benchmark.\n* The fine-tuned LoRA models for Stable Diffusion are taken from civitai and were fine-tuned by RalFinger.\n* The fine-tuning of Mistral was performed based on the Zephyr model as seen here.\n\n\nFor the full list of models and hyper-parameters see the appendix of the paper.\n\n\nRisks and Out-of-Scope Use\n--------------------------\n\n\nOur work uncovers a significant vulnerability in fine-tuned models, allowing attackers to\naccess pre-fine-tuning weights. While this discovery reveals potential security risks,\nour primary objective is to advance the field of Machine Learning and raise awareness within the\nresearch community about the existing vulnerabilities in current models.\n\n\nInstead of using the findings of this study to execute attacks, we advocate for their use by\nmodel creators to enhance the safety and security of their models. By acknowledging and\naddressing vulnerabilities, creators can proactively safeguard against potential threats.\n\n\nFollowing established practices in the cyber-security community, we emphasize the importance of open\ndiscussion and encourage the reporting of vulnerabilities. By fostering transparency and collaboration,\nwe can collectively create a safer environment for deploying machine learning models.\n\n\nConsiderations for Using the Data\n---------------------------------", "### Licensing Information\n\n\nIf you use this dataset in your work please cite the following paper:\n\n\nBibTeX:" ]

[ 14, 20, 381, 41, 321, 26 ]

[ "passage: TAGS\n#arxiv-2402.10208 #region-us \n### Data Subsets\n\n\nThe table below describes the dataset subsets in detail:### Data Fields\n\n\nAs described above, each row of the dataset represents a single layer that should be recovered and contains the following fields:\n\n\n\n```\ntask_name - The name of the task the model was fine-tuned on (subset).\nlayer_model - In some cases a Pre-FT model has more than one model (e.g., Stable Diffusion fine-tuned both \n the UNet and the Text Encoder). This field specifies the model the layer belongs to.\nlayer_name - The name of the layer in the Pre-FT model as it appears in the model state_dict.\npre_ft_name - The name of the Pre-FT model (e.g., runwayml/stable-diffusion-v1-5).\npre_ft_weight - The weight matrix of the Pre-FT models layer. \nlora_{lora_idx}_name - The name of the LoRA fine-tuned model.\nlora_{lora_idx}_A_weight - The LoRA A weight matrix of the LoRA fine-tuned models layer.\nlora_{lora_idx}_B_weight - The LoRA B weight matrix of the LoRA fine-tuned models layer.\nlora_{lora_idx}_rank - The LoRA rank of the LoRA fine-tuned models layer.\nlora_{lora_idx}_alpha - The LoRA alpha of the LoRA fine-tuned models layer.\n\n```\n\nwhere '{lora\\_idx}' is the index of the LoRA fine-tuned model in the subset (there are 15 LoRA models per subset).### Layer Merging Example\n\n\nThe following code snippet demonstrates merging the LoRA fine-tuned weights with the Pre-FT weights.\n\n\nDataset Creation\n----------------" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Dataset Name\n\n\n\n\n Tokenizer: mdeberta\n\n Dataset: GZ-GOLD-NER-ALIGN_105\n\n Unshuffled ratio: 1\n\n Shuffled ratio: 0\n\n Drop duplicates: False\n\n Dataset path = /home/pgajo/working/food/data/GZ/GZ-GOLD/GZ-GOLD-NER-ALIGN_105.json", "## 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\n\n Tokenizer: mdeberta\n\n Dataset: GZ-GOLD-NER-ALIGN_105\n\n Unshuffled ratio: 1\n\n Shuffled ratio: 0\n\n Drop duplicates: False\n\n Dataset path = /home/pgajo/working/food/data/GZ/GZ-GOLD/GZ-GOLD-NER-ALIGN_105.json", "## 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" ]

[ 6, 91, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Dataset Name\n\n\n\n\n Tokenizer: mdeberta\n\n Dataset: GZ-GOLD-NER-ALIGN_105\n\n Unshuffled ratio: 1\n\n Shuffled ratio: 0\n\n Drop duplicates: False\n\n Dataset path = /home/pgajo/working/food/data/GZ/GZ-GOLD/GZ-GOLD-NER-ALIGN_105.json## 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" ]

[]

[]

[]

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

[ 14 ]

[ "passage: TAGS\n#license-apache-2.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of mlabonne/Monarch-7B\n\n\n\nDataset automatically created during the evaluation run of model mlabonne/Monarch-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T21:50:30.154125(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 mlabonne/Monarch-7B\n\n\n\nDataset automatically created during the evaluation run of model mlabonne/Monarch-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T21:50:30.154125(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" ]

[ 6, 177, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of mlabonne/Monarch-7B\n\n\n\nDataset automatically created during the evaluation run of model mlabonne/Monarch-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T21:50:30.154125(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 RaduGabriel/MUZ\n\n\n\nDataset automatically created during the evaluation run of model RaduGabriel/MUZ on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T22:13:46.441981(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 RaduGabriel/MUZ\n\n\n\nDataset automatically created during the evaluation run of model RaduGabriel/MUZ on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T22:13:46.441981(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" ]

[ 6, 175, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of RaduGabriel/MUZ\n\n\n\nDataset automatically created during the evaluation run of model RaduGabriel/MUZ on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T22:13:46.441981(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 \"URL\"\n\nMore Information needed" ]

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

[ 6, 11 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for Evaluation run of Xenon1/Zenith-7B\n\n\n\nDataset automatically created during the evaluation run of model Xenon1/Zenith-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T23:51:23.458204(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 Xenon1/Zenith-7B\n\n\n\nDataset automatically created during the evaluation run of model Xenon1/Zenith-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-13T23:51:23.458204(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" ]

[ 6, 177, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Xenon1/Zenith-7B\n\n\n\nDataset automatically created during the evaluation run of model Xenon1/Zenith-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-13T23:51:23.458204(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 Mihaiii/Bucharest-0.1\n\n\n\nDataset automatically created during the evaluation run of model Mihaiii/Bucharest-0.1 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 2024-02-14T00:16:59.594031(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 Mihaiii/Bucharest-0.1\n\n\n\nDataset automatically created during the evaluation run of model Mihaiii/Bucharest-0.1 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 2024-02-14T00:16:59.594031(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" ]

[ 6, 177, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Mihaiii/Bucharest-0.1\n\n\n\nDataset automatically created during the evaluation run of model Mihaiii/Bucharest-0.1 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 2024-02-14T00:16:59.594031(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 Test157t/Echidna-7b-128k\n\n\n\nDataset automatically created during the evaluation run of model Test157t/Echidna-7b-128k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-14T02:20:02.752572(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 Test157t/Echidna-7b-128k\n\n\n\nDataset automatically created during the evaluation run of model Test157t/Echidna-7b-128k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-14T02:20:02.752572(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" ]

[ 6, 185, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Test157t/Echidna-7b-128k\n\n\n\nDataset automatically created during the evaluation run of model Test157t/Echidna-7b-128k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-02-14T02:20:02.752572(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:multiple-choice", "task_ids:multiple-choice-qa", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:https://huggingface.co/datasets/race", "language:fr", "license:other", "arxiv:1704.04683", "region:us" ]

[ "1704.04683" ]

[ "fr" ]

[ "# Dataset Card for \"race_all_fr\"", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: Original Dataset Homepage\n- Repository: Translated Dataset on Hugging Face\n- Paper: Original Dataset Paper\n- Leaderboard: Original Dataset Leaderboard", "### Dataset Summary\n\n'race_all_fr' est la version française du dataset RACE, un large dataset de compréhension de lecture comprenant plus de 28 000 passages et près de 100 000 questions. Le dataset original, conçu pour les étudiants des écoles secondaires et des collèges en Chine, a été traduit en français pour étendre son accessibilité et permettre des recherches en compréhension de lecture dans d'autres langues.", "### Supported Tasks and Leaderboards\n\nLes tâches et classements supportés restent identiques à ceux du dataset original RACE, adaptés pour la langue française.", "### Languages\n\nLe dataset est entièrement en français.", "## Dataset Structure", "### Data Instances\n\nLes instances de données sont structurées de manière identique à celles du dataset original RACE, mais traduites en français.", "### Data Fields\n\n- 'example_id': un identifiant unique pour chaque exemple.\n- 'article': le texte de l'article sur lequel se base les questions.\n- 'question': la question posée.\n- 'options': les quatre options de réponse fournies, où seulement une est correcte.\n- 'answer': la lettre correspondant à la réponse correcte parmi les options.", "### Data Splits\n\nLa répartition des données (train/validation/test) est la même que celle du dataset RACE original.", "## Dataset Creation", "### Curation Rationale\n\nCe dataset a été créé pour étendre les ressources disponibles pour la recherche en traitement automatique des langues (TAL) en français, spécifiquement pour la compréhension de la lecture.", "### Source Data", "#### Initial Data Collection and Normalization\n\nLes données sources sont identiques à celles du dataset RACE, mais ont été traduites en français.", "### Annotations\n\nLes annotations restent inchangées par rapport à l'original, à l'exception de la langue.", "### Personal and Sensitive Information\n\nLes considérations sont les mêmes que pour le dataset RACE original.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nLa traduction de datasets en différentes langues est cruciale pour rendre la recherche en TAL accessible plus largement et pour permettre l'entraînement de modèles multilingues.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information\n\nDataset traduit à l'aide de Large_dataset_translator et l'API Google Translate.", "### Licensing Information\n\nLes mêmes licences que le dataset RACE original s'appliquent. Veuillez consulter le lien suivant pour plus d'informations.\n\n\n\nVeuillez citer le papier original du dataset RACE lors de l'utilisation de 'race_all_fr' :", "### Contributions\n\nLa traduction de ce dataset a été réalisée par @MangoHiller. Pour les contributions originales, veuillez vous référer au dépôt GitHub du dataset RACE : URL" ]

[ "TAGS\n#task_categories-multiple-choice #task_ids-multiple-choice-qa #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//huggingface.co/datasets/race #language-French #license-other #arxiv-1704.04683 #region-us \n", "# Dataset Card for \"race_all_fr\"", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: Original Dataset Homepage\n- Repository: Translated Dataset on Hugging Face\n- Paper: Original Dataset Paper\n- Leaderboard: Original Dataset Leaderboard", "### Dataset Summary\n\n'race_all_fr' est la version française du dataset RACE, un large dataset de compréhension de lecture comprenant plus de 28 000 passages et près de 100 000 questions. Le dataset original, conçu pour les étudiants des écoles secondaires et des collèges en Chine, a été traduit en français pour étendre son accessibilité et permettre des recherches en compréhension de lecture dans d'autres langues.", "### Supported Tasks and Leaderboards\n\nLes tâches et classements supportés restent identiques à ceux du dataset original RACE, adaptés pour la langue française.", "### Languages\n\nLe dataset est entièrement en français.", "## Dataset Structure", "### Data Instances\n\nLes instances de données sont structurées de manière identique à celles du dataset original RACE, mais traduites en français.", "### Data Fields\n\n- 'example_id': un identifiant unique pour chaque exemple.\n- 'article': le texte de l'article sur lequel se base les questions.\n- 'question': la question posée.\n- 'options': les quatre options de réponse fournies, où seulement une est correcte.\n- 'answer': la lettre correspondant à la réponse correcte parmi les options.", "### Data Splits\n\nLa répartition des données (train/validation/test) est la même que celle du dataset RACE original.", "## Dataset Creation", "### Curation Rationale\n\nCe dataset a été créé pour étendre les ressources disponibles pour la recherche en traitement automatique des langues (TAL) en français, spécifiquement pour la compréhension de la lecture.", "### Source Data", "#### Initial Data Collection and Normalization\n\nLes données sources sont identiques à celles du dataset RACE, mais ont été traduites en français.", "### Annotations\n\nLes annotations restent inchangées par rapport à l'original, à l'exception de la langue.", "### Personal and Sensitive Information\n\nLes considérations sont les mêmes que pour le dataset RACE original.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nLa traduction de datasets en différentes langues est cruciale pour rendre la recherche en TAL accessible plus largement et pour permettre l'entraînement de modèles multilingues.", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information\n\nDataset traduit à l'aide de Large_dataset_translator et l'API Google Translate.", "### Licensing Information\n\nLes mêmes licences que le dataset RACE original s'appliquent. Veuillez consulter le lien suivant pour plus d'informations.\n\n\n\nVeuillez citer le papier original du dataset RACE lors de l'utilisation de 'race_all_fr' :", "### Contributions\n\nLa traduction de ce dataset a été réalisée par @MangoHiller. Pour les contributions originales, veuillez vous référer au dépôt GitHub du dataset RACE : URL" ]

[ 91, 12, 120, 41, 96, 38, 12, 6, 36, 91, 33, 5, 44, 4, 33, 30, 26, 8, 42, 8, 7, 30, 63, 47 ]

[ "passage: TAGS\n#task_categories-multiple-choice #task_ids-multiple-choice-qa #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-https-//huggingface.co/datasets/race #language-French #license-other #arxiv-1704.04683 #region-us \n# Dataset Card for \"race_all_fr\"## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: Original Dataset Homepage\n- Repository: Translated Dataset on Hugging Face\n- Paper: Original Dataset Paper\n- Leaderboard: Original Dataset Leaderboard### Dataset Summary\n\n'race_all_fr' est la version française du dataset RACE, un large dataset de compréhension de lecture comprenant plus de 28 000 passages et près de 100 000 questions. Le dataset original, conçu pour les étudiants des écoles secondaires et des collèges en Chine, a été traduit en français pour étendre son accessibilité et permettre des recherches en compréhension de lecture dans d'autres langues.### Supported Tasks and Leaderboards\n\nLes tâches et classements supportés restent identiques à ceux du dataset original RACE, adaptés pour la langue française.### Languages\n\nLe dataset est entièrement en français.## Dataset Structure### Data Instances\n\nLes instances de données sont structurées de manière identique à celles du dataset original RACE, mais traduites en français." ]

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

[]

[ "uk" ]

[]

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

[ 13 ]

[ "passage: TAGS\n#language-Ukrainian #region-us \n" ]

[ "language:en", "license:cc-by-nc-4.0", "news", "region:us" ]

[]

[ "en" ]

[]

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

[ 23 ]

[ "passage: TAGS\n#language-English #license-cc-by-nc-4.0 #news #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"evesix-level0\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"evesix-level0\"\n\nMore Information needed" ]

[ 6, 16 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"evesix-level0\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 16 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"filtered-ultrachat\"\n\nMore Information needed" ]