Datasets:

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ftopal

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huggingface-datasets-processed

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[ "# Dataset Card for Evaluation run of ewqr2130/mistral-moe-scratch\n\n\n\nDataset automatically created during the evaluation run of model ewqr2130/mistral-moe-scratch on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T05:44:38.553210(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of ewqr2130/mistral-moe-scratch\n\n\n\nDataset automatically created during the evaluation run of model ewqr2130/mistral-moe-scratch on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T05:44:38.553210(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 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 ewqr2130/mistral-moe-scratch\n\n\n\nDataset automatically created during the evaluation run of model ewqr2130/mistral-moe-scratch on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T05:44:38.553210(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information 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 UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T06:31:48.797611(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T06:31:48.797611(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 203, 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 UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/zephyr-7b-sft-full-SPIN-iter1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T06:31:48.797611(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of 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" ]

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[ "# Dataset Card for \"uf_no_to_questions_v2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"uf_no_to_questions_v2\"\n\nMore Information needed" ]

[ 6, 21 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"uf_no_to_questions_v2\"\n\nMore Information needed" ]

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

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

[ 15 ]

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

[ "region:us" ]

[]

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

[ 6 ]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of Weyaxi/Nous-Hermes-2-SUS-Chat-2x34B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Nous-Hermes-2-SUS-Chat-2x34B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T07:39:39.668066(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Weyaxi/Nous-Hermes-2-SUS-Chat-2x34B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Nous-Hermes-2-SUS-Chat-2x34B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T07:39:39.668066(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 197, 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 Weyaxi/Nous-Hermes-2-SUS-Chat-2x34B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Nous-Hermes-2-SUS-Chat-2x34B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T07:39:39.668066(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]

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

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[ "# \nE-mail: tomoki.fujihara.p3@URL" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# \nE-mail: tomoki.fujihara.p3@URL" ]

[ 14, 16 ]

[ "passage: TAGS\n#license-apache-2.0 #region-us \n# \nE-mail: tomoki.fujihara.p3@URL" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of decem/Dionysus-Mistral-m3-v5\n\n\n\nDataset automatically created during the evaluation run of model decem/Dionysus-Mistral-m3-v5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T07:41:42.571559(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 decem/Dionysus-Mistral-m3-v5\n\n\n\nDataset automatically created during the evaluation run of model decem/Dionysus-Mistral-m3-v5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T07:41:42.571559(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 decem/Dionysus-Mistral-m3-v5\n\n\n\nDataset automatically created during the evaluation run of model decem/Dionysus-Mistral-m3-v5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T07:41:42.571559(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "region:us" ]

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[]

[ "# Dataset Card for Evaluation run of qblocks/gpt2_137m_DolphinCoder\n\n\n\nDataset automatically created during the evaluation run of model qblocks/gpt2_137m_DolphinCoder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T07:48:29.644069(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 qblocks/gpt2_137m_DolphinCoder\n\n\n\nDataset automatically created during the evaluation run of model qblocks/gpt2_137m_DolphinCoder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T07:48:29.644069(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 qblocks/gpt2_137m_DolphinCoder\n\n\n\nDataset automatically created during the evaluation run of model qblocks/gpt2_137m_DolphinCoder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T07:48:29.644069(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information 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 qblocks/codellama_7b_DolphinCoder\n\n\n\nDataset automatically created during the evaluation run of model qblocks/codellama_7b_DolphinCoder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T08:13:34.391359(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 qblocks/codellama_7b_DolphinCoder\n\n\n\nDataset automatically created during the evaluation run of model qblocks/codellama_7b_DolphinCoder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T08:13:34.391359(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 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 qblocks/codellama_7b_DolphinCoder\n\n\n\nDataset automatically created during the evaluation run of model qblocks/codellama_7b_DolphinCoder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T08:13:34.391359(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information 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 qblocks/falcon_7b_DolphinCoder\n\n\n\nDataset automatically created during the evaluation run of model qblocks/falcon_7b_DolphinCoder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T08:20:23.826354(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 qblocks/falcon_7b_DolphinCoder\n\n\n\nDataset automatically created during the evaluation run of model qblocks/falcon_7b_DolphinCoder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T08:20:23.826354(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 qblocks/falcon_7b_DolphinCoder\n\n\n\nDataset automatically created during the evaluation run of model qblocks/falcon_7b_DolphinCoder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T08:20:23.826354(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information 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 qblocks/mistral_7b_DolphinCoder\n\n\n\nDataset automatically created during the evaluation run of model qblocks/mistral_7b_DolphinCoder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T08:38:41.844099(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 qblocks/mistral_7b_DolphinCoder\n\n\n\nDataset automatically created during the evaluation run of model qblocks/mistral_7b_DolphinCoder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T08:38:41.844099(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 qblocks/mistral_7b_DolphinCoder\n\n\n\nDataset automatically created during the evaluation run of model qblocks/mistral_7b_DolphinCoder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T08:38:41.844099(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information 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 vihangd/smartsolmix-4x10.7b-v1\n\n\n\nDataset automatically created during the evaluation run of model vihangd/smartsolmix-4x10.7b-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T08:52:41.511718(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 vihangd/smartsolmix-4x10.7b-v1\n\n\n\nDataset automatically created during the evaluation run of model vihangd/smartsolmix-4x10.7b-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T08:52:41.511718(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 vihangd/smartsolmix-4x10.7b-v1\n\n\n\nDataset automatically created during the evaluation run of model vihangd/smartsolmix-4x10.7b-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T08:52:41.511718(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information 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 KnutJaegersberg/Deacon-20B\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deacon-20B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T09:05:17.184238(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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/Deacon-20B\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deacon-20B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T09:05:17.184238(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 KnutJaegersberg/Deacon-20B\n\n\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/Deacon-20B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T09:05:17.184238(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"full_sft_chat_data_filtered_final\"\n\nMore Information needed" ]

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

[ 6, 23 ]

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

[ "region:us" ]

[]

[]

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

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

[ 6, 16 ]

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

[ "region:us" ]

[]

[]

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

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

[ 6, 17 ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of moreh/MoMo-72B-LoRA-V1.4\n\n\n\nDataset automatically created during the evaluation run of model moreh/MoMo-72B-LoRA-V1.4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T09:27:55.373220(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 moreh/MoMo-72B-LoRA-V1.4\n\n\n\nDataset automatically created during the evaluation run of model moreh/MoMo-72B-LoRA-V1.4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T09:27:55.373220(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 moreh/MoMo-72B-LoRA-V1.4\n\n\n\nDataset automatically created during the evaluation run of model moreh/MoMo-72B-LoRA-V1.4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T09:27:55.373220(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]" ]

[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:ru", "license:mit", "psyhology", "text classification", "suicide", "region:us" ]

[]

[ "ru" ]

[ "# Dataset Card for Dataset for presuicidal signal detection\n\n\n\nThis dataset dedicated to find texts that contain information that helps to diagnosis person's suicide rating.", "## Dataset Details", "### Dataset Description\n\n\n\n- Curated by: Igor Buyanov (URL.o@URL)\n- Language(s) (NLP): Russian\n- License: MIT", "### Dataset Sources\n\n\n\n- Repository: link\n- Paper: link", "## Uses\n\n\n\nThe dataset is intended to use to train the model that can help the psychologists to analyze the potential suicidal person accounts faster in order to find clues and facts that helps them in threatment.", "## Dataset Structure\n\nThe dataset has two categories: the normal text (0) and text with potential useful information about person's suicide signals (1). These signals are:\n* Texts describing negative events that occurred with the subject in the past or in the present - messages that are factual, describing negative moments that can happen to a person, such as attempts and facts of rape, problems with parents, the fact of being in a psychiatric hospital, facts of self-harm, etc.\n* Current negative emotional state - messages containing a display of subjective negative attitude towards oneself and others, including a desire to die, a feeling of pressure from the past, self-hatred, aggressiveness, rage directed at oneself or others.\n\nNote that source dataset that was pointed in Repository contains five categories. Due to unrepresentation of some categories and extremeimbalance, the dataset were transformed to have only two categories. See the paper for more details.\n\nThe dataset is splitted to train and test parts. Current count distribution is as follows:", "## Dataset Creation", "### Source Data\n\nAccounts of Russian persons on Twitter that were marked as having tendency to suicide.", "### Annotations\n\n\n\nSee the paper.", "#### Personal and Sensitive Information\n\n\n\nThe dataset may contain some personal information that was shared by Twitter users themselves.\n\nBibTeX:", "## Dataset Card Authors\n\nIgor Buyanov", "## Dataset Card Contact\n\nURL.o@URL" ]

[ "TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Russian #license-mit #psyhology #text classification #suicide #region-us \n", "# Dataset Card for Dataset for presuicidal signal detection\n\n\n\nThis dataset dedicated to find texts that contain information that helps to diagnosis person's suicide rating.", "## Dataset Details", "### Dataset Description\n\n\n\n- Curated by: Igor Buyanov (URL.o@URL)\n- Language(s) (NLP): Russian\n- License: MIT", "### Dataset Sources\n\n\n\n- Repository: link\n- Paper: link", "## Uses\n\n\n\nThe dataset is intended to use to train the model that can help the psychologists to analyze the potential suicidal person accounts faster in order to find clues and facts that helps them in threatment.", "## Dataset Structure\n\nThe dataset has two categories: the normal text (0) and text with potential useful information about person's suicide signals (1). These signals are:\n* Texts describing negative events that occurred with the subject in the past or in the present - messages that are factual, describing negative moments that can happen to a person, such as attempts and facts of rape, problems with parents, the fact of being in a psychiatric hospital, facts of self-harm, etc.\n* Current negative emotional state - messages containing a display of subjective negative attitude towards oneself and others, including a desire to die, a feeling of pressure from the past, self-hatred, aggressiveness, rage directed at oneself or others.\n\nNote that source dataset that was pointed in Repository contains five categories. Due to unrepresentation of some categories and extremeimbalance, the dataset were transformed to have only two categories. See the paper for more details.\n\nThe dataset is splitted to train and test parts. Current count distribution is as follows:", "## Dataset Creation", "### Source Data\n\nAccounts of Russian persons on Twitter that were marked as having tendency to suicide.", "### Annotations\n\n\n\nSee the paper.", "#### Personal and Sensitive Information\n\n\n\nThe dataset may contain some personal information that was shared by Twitter users themselves.\n\nBibTeX:", "## Dataset Card Authors\n\nIgor Buyanov", "## Dataset Card Contact\n\nURL.o@URL" ]

[ 51, 37, 4, 35, 16, 50, 240, 5, 22, 9, 29, 10, 10 ]

[ "passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Russian #license-mit #psyhology #text classification #suicide #region-us \n# Dataset Card for Dataset for presuicidal signal detection\n\n\n\nThis dataset dedicated to find texts that contain information that helps to diagnosis person's suicide rating.## Dataset Details### Dataset Description\n\n\n\n- Curated by: Igor Buyanov (URL.o@URL)\n- Language(s) (NLP): Russian\n- License: MIT### Dataset Sources\n\n\n\n- Repository: link\n- Paper: link## Uses\n\n\n\nThe dataset is intended to use to train the model that can help the psychologists to analyze the potential suicidal person accounts faster in order to find clues and facts that helps them in threatment.## Dataset Structure\n\nThe dataset has two categories: the normal text (0) and text with potential useful information about person's suicide signals (1). These signals are:\n* Texts describing negative events that occurred with the subject in the past or in the present - messages that are factual, describing negative moments that can happen to a person, such as attempts and facts of rape, problems with parents, the fact of being in a psychiatric hospital, facts of self-harm, etc.\n* Current negative emotional state - messages containing a display of subjective negative attitude towards oneself and others, including a desire to die, a feeling of pressure from the past, self-hatred, aggressiveness, rage directed at oneself or others.\n\nNote that source dataset that was pointed in Repository contains five categories. Due to unrepresentation of some categories and extremeimbalance, the dataset were transformed to have only two categories. See the paper for more details.\n\nThe dataset is splitted to train and test parts. Current count distribution is as follows:## Dataset Creation### Source Data\n\nAccounts of Russian persons on Twitter that were marked as having tendency to suicide.### Annotations\n\n\n\nSee the paper.#### Personal and Sensitive Information\n\n\n\nThe dataset may contain some personal information that was shared by Twitter users themselves.\n\nBibTeX:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of ed001/datascience-coder-6.7b\n\n\n\nDataset automatically created during the evaluation run of model ed001/datascience-coder-6.7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T09:33:35.006022(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ed001/datascience-coder-6.7b\n\n\n\nDataset automatically created during the evaluation run of model ed001/datascience-coder-6.7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T09:33:35.006022(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ed001/datascience-coder-6.7b\n\n\n\nDataset automatically created during the evaluation run of model ed001/datascience-coder-6.7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T09:33:35.006022(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 malhajar/Mistral-7B-v0.2-meditron-turkish\n\n\n\nDataset automatically created during the evaluation run of model malhajar/Mistral-7B-v0.2-meditron-turkish on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T09:37:57.221599(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 malhajar/Mistral-7B-v0.2-meditron-turkish\n\n\n\nDataset automatically created during the evaluation run of model malhajar/Mistral-7B-v0.2-meditron-turkish on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T09:37:57.221599(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 195, 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 malhajar/Mistral-7B-v0.2-meditron-turkish\n\n\n\nDataset automatically created during the evaluation run of model malhajar/Mistral-7B-v0.2-meditron-turkish on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T09:37:57.221599(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of 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 deepseek-ai/deepseek-llm-7b-chat\n\n\n\nDataset automatically created during the evaluation run of model deepseek-ai/deepseek-llm-7b-chat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T10:38:25.592014(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 deepseek-ai/deepseek-llm-7b-chat\n\n\n\nDataset automatically created during the evaluation run of model deepseek-ai/deepseek-llm-7b-chat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T10:38:25.592014(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 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 deepseek-ai/deepseek-llm-7b-chat\n\n\n\nDataset automatically created during the evaluation run of model deepseek-ai/deepseek-llm-7b-chat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T10:38:25.592014(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information 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 deepseek-ai/deepseek-coder-6.7b-instruct\n\n\n\nDataset automatically created during the evaluation run of model deepseek-ai/deepseek-coder-6.7b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T09:40:26.509293(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 deepseek-ai/deepseek-coder-6.7b-instruct\n\n\n\nDataset automatically created during the evaluation run of model deepseek-ai/deepseek-coder-6.7b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T09:40:26.509293(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 197, 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 deepseek-ai/deepseek-coder-6.7b-instruct\n\n\n\nDataset automatically created during the evaluation run of model deepseek-ai/deepseek-coder-6.7b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T09:40:26.509293(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of 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" ]

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[ "# Dataset Card for Evaluation run of allbyai/ToRoLaMa-7b-v1.0\n\n\n\nDataset automatically created during the evaluation run of model allbyai/ToRoLaMa-7b-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T09:43:59.013115(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 allbyai/ToRoLaMa-7b-v1.0\n\n\n\nDataset automatically created during the evaluation run of model allbyai/ToRoLaMa-7b-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T09:43:59.013115(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 allbyai/ToRoLaMa-7b-v1.0\n\n\n\nDataset automatically created during the evaluation run of model allbyai/ToRoLaMa-7b-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T09:43:59.013115(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2\n\n\n\nDataset automatically created during the evaluation run of model NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T10:04:28.728094(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2\n\n\n\nDataset automatically created during the evaluation run of model NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T10:04:28.728094(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 217, 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 NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2\n\n\n\nDataset automatically created during the evaluation run of model NickyNicky/Mistral-7B-OpenOrca-oasst_top1_2023-08-25-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T10:04:28.728094(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of OpenBuddy/openbuddy-zephyr-7b-v14.1\n\n\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-zephyr-7b-v14.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T10:04:44.823232(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 OpenBuddy/openbuddy-zephyr-7b-v14.1\n\n\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-zephyr-7b-v14.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T10:04:44.823232(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 OpenBuddy/openbuddy-zephyr-7b-v14.1\n\n\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-zephyr-7b-v14.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T10:04:44.823232(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information 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 \"rmh_subset_medium2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"rmh_subset_medium2\"\n\nMore Information needed" ]

[ 6, 19 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"rmh_subset_medium2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of perlthoughts/Chupacabra-7B-v2.04\n\n\n\nDataset automatically created during the evaluation run of model perlthoughts/Chupacabra-7B-v2.04 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T10:12:55.038964(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 perlthoughts/Chupacabra-7B-v2.04\n\n\n\nDataset automatically created during the evaluation run of model perlthoughts/Chupacabra-7B-v2.04 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T10:12:55.038964(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 perlthoughts/Chupacabra-7B-v2.04\n\n\n\nDataset automatically created during the evaluation run of model perlthoughts/Chupacabra-7B-v2.04 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T10:12:55.038964(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information 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 \"cruciverba\"\n\nMore Information needed" ]

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

[ 6, 14 ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of whiterabbitneo/WhiteRabbitNeo-13B\n\n\n\nDataset automatically created during the evaluation run of model whiterabbitneo/WhiteRabbitNeo-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T10:34:55.691217(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 whiterabbitneo/WhiteRabbitNeo-13B\n\n\n\nDataset automatically created during the evaluation run of model whiterabbitneo/WhiteRabbitNeo-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T10:34:55.691217(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 whiterabbitneo/WhiteRabbitNeo-13B\n\n\n\nDataset automatically created during the evaluation run of model whiterabbitneo/WhiteRabbitNeo-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T10:34:55.691217(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "task_categories:text-generation", "task_categories:text-classification", "task_categories:conversational", "size_categories:1M<n<10M", "language:en", "language:es", "language:ru", "language:de", "language:pl", "language:th", "language:vi", "language:sv", "language:bn", "language:da", "language:he", "language:it", "language:fa", "language:sk", "language:id", "language:nb", "language:el", "language:nl", "language:hu", "language:eu", "language:zh", "language:eo", "language:ja", "language:ca", "language:cs", "language:bg", "language:fi", "language:pt", "language:tr", "language:ro", "language:ar", "language:uk", "language:gl", "language:fr", "language:ko", "code", "biology", "medical", "region:us" ]

[]

[ "en", "es", "ru", "de", "pl", "th", "vi", "sv", "bn", "da", "he", "it", "fa", "sk", "id", "nb", "el", "nl", "hu", "eu", "zh", "eo", "ja", "ca", "cs", "bg", "fi", "pt", "tr", "ro", "ar", "uk", "gl", "fr", "ko" ]

[ "# Dataset Card for \"UltraChat-Mixin\"", "# UltraChat-Mixin Dataset", "## Overview\nllama 2 prompted style frin", "### ChatMatic\nChatMatic Dataset is built with mix of 4 other dataset and which carefully chosing best one from each one of them with using GPT-4 and contains System messages Dialogs and conv_depth more than 5 with higher sequence lengths Used datasets are:\n\n\"oasst2\"\n\"ise-uiuc/Magicoder-Evol-Instruct-110K\"\n\"vicgalle/alpaca-gpt4\"\n\"LDJnr/Capybara\"", "## Dataset Configuration\nThe dataset is configured as follows:", "## Features\nThe UltraChat-Mixin dataset consists of the following features:\n\n- prompt: A sequence of strings representing the conversation dialog Llama2 Style prompts.", "## Splits\nThe dataset contains a single split:\n\n- train: This split is used for training conversational AI models. It consists of 70_000 examples and has a size of approximately 168,057,889 bytes.", "## Download Size\nThe download size of the UltraChat-Mixin dataset is approximately 88,116,993 bytes.", "## Dataset Size\nThe total size of the UltraChat-Mixin dataset is approximately 168,057,889 bytes.\n\nPlease note that the dataset configuration and statistics provided above are based on the information provided by Erfan zare chavoshi." ]

[ "TAGS\n#task_categories-text-generation #task_categories-text-classification #task_categories-conversational #size_categories-1M<n<10M #language-English #language-Spanish #language-Russian #language-German #language-Polish #language-Thai #language-Vietnamese #language-Swedish #language-Bengali #language-Danish #language-Hebrew #language-Italian #language-Persian #language-Slovak #language-Indonesian #language-Norwegian Bokmål #language-Modern Greek (1453-) #language-Dutch #language-Hungarian #language-Basque #language-Chinese #language-Esperanto #language-Japanese #language-Catalan #language-Czech #language-Bulgarian #language-Finnish #language-Portuguese #language-Turkish #language-Romanian #language-Arabic #language-Ukrainian #language-Galician #language-French #language-Korean #code #biology #medical #region-us \n", "# Dataset Card for \"UltraChat-Mixin\"", "# UltraChat-Mixin Dataset", "## Overview\nllama 2 prompted style frin", "### ChatMatic\nChatMatic Dataset is built with mix of 4 other dataset and which carefully chosing best one from each one of them with using GPT-4 and contains System messages Dialogs and conv_depth more than 5 with higher sequence lengths Used datasets are:\n\n\"oasst2\"\n\"ise-uiuc/Magicoder-Evol-Instruct-110K\"\n\"vicgalle/alpaca-gpt4\"\n\"LDJnr/Capybara\"", "## Dataset Configuration\nThe dataset is configured as follows:", "## Features\nThe UltraChat-Mixin dataset consists of the following features:\n\n- prompt: A sequence of strings representing the conversation dialog Llama2 Style prompts.", "## Splits\nThe dataset contains a single split:\n\n- train: This split is used for training conversational AI models. It consists of 70_000 examples and has a size of approximately 168,057,889 bytes.", "## Download Size\nThe download size of the UltraChat-Mixin dataset is approximately 88,116,993 bytes.", "## Dataset Size\nThe total size of the UltraChat-Mixin dataset is approximately 168,057,889 bytes.\n\nPlease note that the dataset configuration and statistics provided above are based on the information provided by Erfan zare chavoshi." ]

[ 253, 13, 8, 10, 111, 16, 40, 49, 25, 53 ]

[ "passage: TAGS\n#task_categories-text-generation #task_categories-text-classification #task_categories-conversational #size_categories-1M<n<10M #language-English #language-Spanish #language-Russian #language-German #language-Polish #language-Thai #language-Vietnamese #language-Swedish #language-Bengali #language-Danish #language-Hebrew #language-Italian #language-Persian #language-Slovak #language-Indonesian #language-Norwegian Bokmål #language-Modern Greek (1453-) #language-Dutch #language-Hungarian #language-Basque #language-Chinese #language-Esperanto #language-Japanese #language-Catalan #language-Czech #language-Bulgarian #language-Finnish #language-Portuguese #language-Turkish #language-Romanian #language-Arabic #language-Ukrainian #language-Galician #language-French #language-Korean #code #biology #medical #region-us \n# Dataset Card for \"UltraChat-Mixin\"# UltraChat-Mixin Dataset## Overview\nllama 2 prompted style frin### ChatMatic\nChatMatic Dataset is built with mix of 4 other dataset and which carefully chosing best one from each one of them with using GPT-4 and contains System messages Dialogs and conv_depth more than 5 with higher sequence lengths Used datasets are:\n\n\"oasst2\"\n\"ise-uiuc/Magicoder-Evol-Instruct-110K\"\n\"vicgalle/alpaca-gpt4\"\n\"LDJnr/Capybara\"## Dataset Configuration\nThe dataset is configured as follows:## Features\nThe UltraChat-Mixin dataset consists of the following features:\n\n- prompt: A sequence of strings representing the conversation dialog Llama2 Style prompts.## Splits\nThe dataset contains a single split:\n\n- train: This split is used for training conversational AI models. It consists of 70_000 examples and has a size of approximately 168,057,889 bytes." ]

[ "task_categories:text-generation", "task_categories:text-classification", "task_categories:conversational", "size_categories:1M<n<10M", "language:en", "language:es", "language:ru", "language:de", "language:pl", "language:th", "language:vi", "language:sv", "language:bn", "language:da", "language:he", "language:it", "language:fa", "language:sk", "language:id", "language:nb", "language:el", "language:nl", "language:hu", "language:eu", "language:zh", "language:eo", "language:ja", "language:ca", "language:cs", "language:bg", "language:fi", "language:pt", "language:tr", "language:ro", "language:ar", "language:uk", "language:gl", "language:fr", "language:ko", "code", "biology", "medical", "region:us" ]

[]

[ "en", "es", "ru", "de", "pl", "th", "vi", "sv", "bn", "da", "he", "it", "fa", "sk", "id", "nb", "el", "nl", "hu", "eu", "zh", "eo", "ja", "ca", "cs", "bg", "fi", "pt", "tr", "ro", "ar", "uk", "gl", "fr", "ko" ]

[ "# ChatMatic", "## with Over 80,000 multi-turn examples.\n\nUltraChat-Matic Dataset is built with mix of 4 other dataset and which carefully chosing best one from each one of them with using 'GPT-4'\nand contains \nSystem messages Dialogs and conv_depth more than 5 with higher sequence lengths\nUsed datasets are:\n\n1. \"oasst2\"\n2. \"ise-uiuc/Magicoder-Evol-Instruct-110K\"\n3. \"vicgalle/alpaca-gpt4\"\n4. \"LDJnr/Capybara\"", "### From Capybara \n\n* Most tokens contained in this dataset are newly synthesized and did not exist prior online.\n\n* This leverages the Amplify-Instruct method(paper coming soon) to grow thousands of high-quality single-turn seeds into advanced and in-depth multi-turn conversations.\n\n* Average context length per conversation is over 1,000 tokens and 3 turns or more per example (most instruction/chat datasets on HF for fine-tuning are only 1 turn)\n\n* Each conversation is optimized to amplify the natural raw knowledge capabilities of the model, as well as delving deep into obscure and advanced topics.\n\n* Aggresively filtered to remove any and all possible examples of overt moralizing/alignment, and common undesirable behaviours such as \"as an AI language model\" and \"September 2021\" and \"I don't have personal beliefs\"\n\n* ### More than 60000 Datas generated or selected by GPT4" ]

[ "TAGS\n#task_categories-text-generation #task_categories-text-classification #task_categories-conversational #size_categories-1M<n<10M #language-English #language-Spanish #language-Russian #language-German #language-Polish #language-Thai #language-Vietnamese #language-Swedish #language-Bengali #language-Danish #language-Hebrew #language-Italian #language-Persian #language-Slovak #language-Indonesian #language-Norwegian Bokmål #language-Modern Greek (1453-) #language-Dutch #language-Hungarian #language-Basque #language-Chinese #language-Esperanto #language-Japanese #language-Catalan #language-Czech #language-Bulgarian #language-Finnish #language-Portuguese #language-Turkish #language-Romanian #language-Arabic #language-Ukrainian #language-Galician #language-French #language-Korean #code #biology #medical #region-us \n", "# ChatMatic", "## with Over 80,000 multi-turn examples.\n\nUltraChat-Matic Dataset is built with mix of 4 other dataset and which carefully chosing best one from each one of them with using 'GPT-4'\nand contains \nSystem messages Dialogs and conv_depth more than 5 with higher sequence lengths\nUsed datasets are:\n\n1. \"oasst2\"\n2. \"ise-uiuc/Magicoder-Evol-Instruct-110K\"\n3. \"vicgalle/alpaca-gpt4\"\n4. \"LDJnr/Capybara\"", "### From Capybara \n\n* Most tokens contained in this dataset are newly synthesized and did not exist prior online.\n\n* This leverages the Amplify-Instruct method(paper coming soon) to grow thousands of high-quality single-turn seeds into advanced and in-depth multi-turn conversations.\n\n* Average context length per conversation is over 1,000 tokens and 3 turns or more per example (most instruction/chat datasets on HF for fine-tuning are only 1 turn)\n\n* Each conversation is optimized to amplify the natural raw knowledge capabilities of the model, as well as delving deep into obscure and advanced topics.\n\n* Aggresively filtered to remove any and all possible examples of overt moralizing/alignment, and common undesirable behaviours such as \"as an AI language model\" and \"September 2021\" and \"I don't have personal beliefs\"\n\n* ### More than 60000 Datas generated or selected by GPT4" ]

[ 253, 4, 126, 223 ]

[ "passage: TAGS\n#task_categories-text-generation #task_categories-text-classification #task_categories-conversational #size_categories-1M<n<10M #language-English #language-Spanish #language-Russian #language-German #language-Polish #language-Thai #language-Vietnamese #language-Swedish #language-Bengali #language-Danish #language-Hebrew #language-Italian #language-Persian #language-Slovak #language-Indonesian #language-Norwegian Bokmål #language-Modern Greek (1453-) #language-Dutch #language-Hungarian #language-Basque #language-Chinese #language-Esperanto #language-Japanese #language-Catalan #language-Czech #language-Bulgarian #language-Finnish #language-Portuguese #language-Turkish #language-Romanian #language-Arabic #language-Ukrainian #language-Galician #language-French #language-Korean #code #biology #medical #region-us \n# ChatMatic## with Over 80,000 multi-turn examples.\n\nUltraChat-Matic Dataset is built with mix of 4 other dataset and which carefully chosing best one from each one of them with using 'GPT-4'\nand contains \nSystem messages Dialogs and conv_depth more than 5 with higher sequence lengths\nUsed datasets are:\n\n1. \"oasst2\"\n2. \"ise-uiuc/Magicoder-Evol-Instruct-110K\"\n3. \"vicgalle/alpaca-gpt4\"\n4. \"LDJnr/Capybara\"" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of ZoidBB/Jovian-10.7B-v1.0\n\n\n\nDataset automatically created during the evaluation run of model ZoidBB/Jovian-10.7B-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T22:02:39.167169(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ZoidBB/Jovian-10.7B-v1.0\n\n\n\nDataset automatically created during the evaluation run of model ZoidBB/Jovian-10.7B-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T22:02:39.167169(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ZoidBB/Jovian-10.7B-v1.0\n\n\n\nDataset automatically created during the evaluation run of model ZoidBB/Jovian-10.7B-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T22:02:39.167169(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of UCLA-AGI/test0\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/test0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T12:00:11.696869(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 UCLA-AGI/test0\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/test0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T12:00:11.696869(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 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 UCLA-AGI/test0\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/test0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T12:00:11.696869(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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-mistral-moloras-7b\n\n\n\nDataset automatically created during the evaluation run of model uukuguy/speechless-mistral-moloras-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T12:03:44.499020(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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-mistral-moloras-7b\n\n\n\nDataset automatically created during the evaluation run of model uukuguy/speechless-mistral-moloras-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T12:03:44.499020(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 195, 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-mistral-moloras-7b\n\n\n\nDataset automatically created during the evaluation run of model uukuguy/speechless-mistral-moloras-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T12:03:44.499020(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of 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 princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT\n\n\n\nDataset automatically created during the evaluation run of model princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T12:10:45.462405(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT\n\n\n\nDataset automatically created during the evaluation run of model princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T12:10:45.462405(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 203, 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 princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT\n\n\n\nDataset automatically created during the evaluation run of model princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T12:10:45.462405(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of 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" ]

[]

[]

[ "# Syntetic emails. Split by inquiry and answer email\n\nConsume and run into a notebook colab\nYou can switch the dataset there from 'mlabonne/guanaco-llama2-1k' to 'spielhoelle/film-equipment-order-emails'" ]

[ "TAGS\n#region-us \n", "# Syntetic emails. Split by inquiry and answer email\n\nConsume and run into a notebook colab\nYou can switch the dataset there from 'mlabonne/guanaco-llama2-1k' to 'spielhoelle/film-equipment-order-emails'" ]

[ 6, 61 ]

[ "passage: TAGS\n#region-us \n# Syntetic emails. Split by inquiry and answer email\n\nConsume and run into a notebook colab\nYou can switch the dataset there from 'mlabonne/guanaco-llama2-1k' to 'spielhoelle/film-equipment-order-emails'" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"myriade_noun_wsd_bis2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"myriade_noun_wsd_bis2\"\n\nMore Information needed" ]

[ 6, 22 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"myriade_noun_wsd_bis2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of NurtureAI/MistralLite-11B\n\n\n\nDataset automatically created during the evaluation run of model NurtureAI/MistralLite-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 2024-01-05T13:36:51.362118(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 NurtureAI/MistralLite-11B\n\n\n\nDataset automatically created during the evaluation run of model NurtureAI/MistralLite-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 2024-01-05T13:36:51.362118(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 NurtureAI/MistralLite-11B\n\n\n\nDataset automatically created during the evaluation run of model NurtureAI/MistralLite-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 2024-01-05T13:36:51.362118(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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" ]

[]

[]

[ "# Open Assistant 2 Top English Curated", "## Dataset Details", "### Dataset Description\n\nA filtered and curated dataset taken from the top scoring URL conversations. Saved in HF Chat format. The result is a high quality dataset for SFT.\n\n- Created by: dctanner and the team at Sablo AI\n- License: Apache 2.0", "## Dataset Structure\n\nWe structure the dataset using the format commonly used as input into Hugging Face Chat Templates:", "## Dataset Creation", "### Source Data\n\n- Source Dataset: URL", "#### Data Collection and Processing\n\nWe started with the top_k=1 English only conversations from URL\n\nFiltering and curation was done to remove conversations with:\n- Duplicate or very similar responses\n- Responses where the AI was actually responding like a person (present in this dataset as the responses are created by humans pretending to be an AI, and no everyone followed these instructions closely)\n- Profanity or inappropriate responses for an AI\n- Very short response lengths (often below 50 or 200 characters)\n- URLs", "# License\n\n- License: Apache 2.0\n\nThis dataset is usable for commercial purposes.", "# Contact\n\nCreated by dctanner and the team at Sablo AI" ]

[ "TAGS\n#region-us \n", "# Open Assistant 2 Top English Curated", "## Dataset Details", "### Dataset Description\n\nA filtered and curated dataset taken from the top scoring URL conversations. Saved in HF Chat format. The result is a high quality dataset for SFT.\n\n- Created by: dctanner and the team at Sablo AI\n- License: Apache 2.0", "## Dataset Structure\n\nWe structure the dataset using the format commonly used as input into Hugging Face Chat Templates:", "## Dataset Creation", "### Source Data\n\n- Source Dataset: URL", "#### Data Collection and Processing\n\nWe started with the top_k=1 English only conversations from URL\n\nFiltering and curation was done to remove conversations with:\n- Duplicate or very similar responses\n- Responses where the AI was actually responding like a person (present in this dataset as the responses are created by humans pretending to be an AI, and no everyone followed these instructions closely)\n- Profanity or inappropriate responses for an AI\n- Very short response lengths (often below 50 or 200 characters)\n- URLs", "# License\n\n- License: Apache 2.0\n\nThis dataset is usable for commercial purposes.", "# Contact\n\nCreated by dctanner and the team at Sablo AI" ]

[ 6, 8, 4, 64, 27, 5, 10, 117, 19, 15 ]

[ "passage: TAGS\n#region-us \n# Open Assistant 2 Top English Curated## Dataset Details### Dataset Description\n\nA filtered and curated dataset taken from the top scoring URL conversations. Saved in HF Chat format. The result is a high quality dataset for SFT.\n\n- Created by: dctanner and the team at Sablo AI\n- License: Apache 2.0## Dataset Structure\n\nWe structure the dataset using the format commonly used as input into Hugging Face Chat Templates:## Dataset Creation### Source Data\n\n- Source Dataset: URL#### Data Collection and Processing\n\nWe started with the top_k=1 English only conversations from URL\n\nFiltering and curation was done to remove conversations with:\n- Duplicate or very similar responses\n- Responses where the AI was actually responding like a person (present in this dataset as the responses are created by humans pretending to be an AI, and no everyone followed these instructions closely)\n- Profanity or inappropriate responses for an AI\n- Very short response lengths (often below 50 or 200 characters)\n- URLs# License\n\n- License: Apache 2.0\n\nThis dataset is usable for commercial purposes.# Contact\n\nCreated by dctanner and the team at Sablo AI" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"finetune-test-eiffel_dataset_20\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"finetune-test-eiffel_dataset_20\"\n\nMore Information needed" ]

[ 6, 22 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"finetune-test-eiffel_dataset_20\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"araproje_hellaswag_tr_conf1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"araproje_hellaswag_tr_conf1\"\n\nMore Information needed" ]

[ 6, 22 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_hellaswag_tr_conf1\"\n\nMore Information needed" ]

[ "language:en", "license:cc-by-sa-3.0", "Wikipedia", "Text", "Article", "finance", "article", "region:us" ]

[]

[ "en" ]

[ "# Wikipedia Crypto Articles ₿\n\nThis dataset is a collection of articles obtained from Wikipedia on January 5ᵗʰ, 2024. It contains two columns, title and article, containing the article's title as it is on the Wikipedia website and the article's content.\n\nThe articles vary from specific cryptocurrencies—such as Bitcoin or Ethereum—to historical facts, companies, exchanges, entities, and relevant people in the history of cryptocurrencies.\n\nThis dataset can be used to train machine learning models for question-answering tasks, summarization, conversation, named entity recognition, etc." ]

[ "TAGS\n#language-English #license-cc-by-sa-3.0 #Wikipedia #Text #Article #finance #article #region-us \n", "# Wikipedia Crypto Articles ₿\n\nThis dataset is a collection of articles obtained from Wikipedia on January 5ᵗʰ, 2024. It contains two columns, title and article, containing the article's title as it is on the Wikipedia website and the article's content.\n\nThe articles vary from specific cryptocurrencies—such as Bitcoin or Ethereum—to historical facts, companies, exchanges, entities, and relevant people in the history of cryptocurrencies.\n\nThis dataset can be used to train machine learning models for question-answering tasks, summarization, conversation, named entity recognition, etc." ]

[ 33, 135 ]

[ "passage: TAGS\n#language-English #license-cc-by-sa-3.0 #Wikipedia #Text #Article #finance #article #region-us \n# Wikipedia Crypto Articles ₿\n\nThis dataset is a collection of articles obtained from Wikipedia on January 5ᵗʰ, 2024. It contains two columns, title and article, containing the article's title as it is on the Wikipedia website and the article's content.\n\nThe articles vary from specific cryptocurrencies—such as Bitcoin or Ethereum—to historical facts, companies, exchanges, entities, and relevant people in the history of cryptocurrencies.\n\nThis dataset can be used to train machine learning models for question-answering tasks, summarization, conversation, named entity recognition, etc." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of nlpguy/Hermes-low-tune-2\n\n\n\nDataset automatically created during the evaluation run of model nlpguy/Hermes-low-tune-2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T13:59:33.272174(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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/Hermes-low-tune-2\n\n\n\nDataset automatically created during the evaluation run of model nlpguy/Hermes-low-tune-2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T13:59:33.272174(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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/Hermes-low-tune-2\n\n\n\nDataset automatically created during the evaluation run of model nlpguy/Hermes-low-tune-2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T13:59:33.272174(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 chargoddard/mixtralmerge-8x7B-rebalanced-test\n\n\n\nDataset automatically created during the evaluation run of model chargoddard/mixtralmerge-8x7B-rebalanced-test on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T14:01:45.060324(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 chargoddard/mixtralmerge-8x7B-rebalanced-test\n\n\n\nDataset automatically created during the evaluation run of model chargoddard/mixtralmerge-8x7B-rebalanced-test on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T14:01:45.060324(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 chargoddard/mixtralmerge-8x7B-rebalanced-test\n\n\n\nDataset automatically created during the evaluation run of model chargoddard/mixtralmerge-8x7B-rebalanced-test on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T14:01:45.060324(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of 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 Aabbhishekk/TinyLlama-1.1B-miniguanaco\n\n\n\nDataset automatically created during the evaluation run of model Aabbhishekk/TinyLlama-1.1B-miniguanaco on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T14:07:19.265466(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Aabbhishekk/TinyLlama-1.1B-miniguanaco\n\n\n\nDataset automatically created during the evaluation run of model Aabbhishekk/TinyLlama-1.1B-miniguanaco on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T14:07:19.265466(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 197, 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 Aabbhishekk/TinyLlama-1.1B-miniguanaco\n\n\n\nDataset automatically created during the evaluation run of model Aabbhishekk/TinyLlama-1.1B-miniguanaco on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T14:07:19.265466(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of 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 Deathsquad10/TinyLlama-1.1B-Remix-V.2\n\n\n\nDataset automatically created during the evaluation run of model Deathsquad10/TinyLlama-1.1B-Remix-V.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T14:09:04.143664(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Deathsquad10/TinyLlama-1.1B-Remix-V.2\n\n\n\nDataset automatically created during the evaluation run of model Deathsquad10/TinyLlama-1.1B-Remix-V.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T14:09:04.143664(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 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 Deathsquad10/TinyLlama-1.1B-Remix-V.2\n\n\n\nDataset automatically created during the evaluation run of model Deathsquad10/TinyLlama-1.1B-Remix-V.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T14:09:04.143664(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]

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

[]

[]

[ "# Dataset Card for Midjourney Art Prompts Dataset", "## Dataset Details", "### Dataset Description\n\nThe Midjourney Art Prompts Dataset is a meticulously curated collection of prompts designed for training or fine-tuning Language Model Prompt Generators. Each prompt is crafted to evoke specific artistic styles, themes, and details, providing a diverse and comprehensive set for creative language model outputs.\n\n- Curated by: Mohammed Shojaei \n- License: Apache-2.0", "### Dataset Sources\n\n- Repository: [URL", "## Uses", "### Direct Use\n\nThe dataset is intended for use in training or fine-tuning Language Models, particularly those focused on generating artistic prompts. It provides a rich set of subjects and styles to enhance the creative capabilities of prompt-based language models.", "### Out-of-Scope Use\n\nMisuse, malicious use, and uses not suitable for generating creative prompts are considered out-of-scope for this dataset.", "## Dataset Structure\n\nThe dataset is provided in CSV format with two main columns:\n\n- subject: Describes the subject matter of the prompt.\n- prompt: Provides detailed information about the artistic style, elements, and specifications for the prompt.", "## Dataset Creation", "### Curation Rationale\n\nThe dataset is created to support the development and enhancement of Language Model Prompt Generators, specifically in the domain of artistic prompts.", "### Source Data", "#### Data Collection and Processing\n\nThe prompts are curated with a focus on diverse artistic styles and themes. Data collection involves selecting prompts that showcase varied subjects and detailed specifications.", "#### Who are the source data producers?\n\nThe prompts are sourced from various artistic contexts and contributors mentioned in the dataset.", "### Annotations [optional]\n\nThis dataset does not involve additional annotations beyond the provided prompts.", "#### Personal and Sensitive Information\n\nThe dataset does not contain personal, sensitive, or private information.", "## Bias, Risks, and Limitations\n\nThe prompts are curated to be diverse, but biases may exist in the selection process. The dataset might not cover every possible artistic style, and users should be aware of potential limitations in representation.", "### Recommendations\n\nUsers should consider the dataset's limitations and supplement it with additional sources for a more comprehensive understanding of artistic prompts.\n\n[optional]\n\nBibTeX:\n\n[Provide BibTeX Information]\n\nAPA:\n\n[Provide APA Information]", "## Glossary [optional]\n\n[No specific terms or calculations in this context]", "## More Information [optional]\n\n[Any additional information you'd like to provide]", "## Dataset Card Authors [optional]\n\n[Your Name or Organization]", "## Dataset Card Contact\n\n[Your Contact Information]" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Dataset Card for Midjourney Art Prompts Dataset", "## Dataset Details", "### Dataset Description\n\nThe Midjourney Art Prompts Dataset is a meticulously curated collection of prompts designed for training or fine-tuning Language Model Prompt Generators. Each prompt is crafted to evoke specific artistic styles, themes, and details, providing a diverse and comprehensive set for creative language model outputs.\n\n- Curated by: Mohammed Shojaei \n- License: Apache-2.0", "### Dataset Sources\n\n- Repository: [URL", "## Uses", "### Direct Use\n\nThe dataset is intended for use in training or fine-tuning Language Models, particularly those focused on generating artistic prompts. It provides a rich set of subjects and styles to enhance the creative capabilities of prompt-based language models.", "### Out-of-Scope Use\n\nMisuse, malicious use, and uses not suitable for generating creative prompts are considered out-of-scope for this dataset.", "## Dataset Structure\n\nThe dataset is provided in CSV format with two main columns:\n\n- subject: Describes the subject matter of the prompt.\n- prompt: Provides detailed information about the artistic style, elements, and specifications for the prompt.", "## Dataset Creation", "### Curation Rationale\n\nThe dataset is created to support the development and enhancement of Language Model Prompt Generators, specifically in the domain of artistic prompts.", "### Source Data", "#### Data Collection and Processing\n\nThe prompts are curated with a focus on diverse artistic styles and themes. Data collection involves selecting prompts that showcase varied subjects and detailed specifications.", "#### Who are the source data producers?\n\nThe prompts are sourced from various artistic contexts and contributors mentioned in the dataset.", "### Annotations [optional]\n\nThis dataset does not involve additional annotations beyond the provided prompts.", "#### Personal and Sensitive Information\n\nThe dataset does not contain personal, sensitive, or private information.", "## Bias, Risks, and Limitations\n\nThe prompts are curated to be diverse, but biases may exist in the selection process. The dataset might not cover every possible artistic style, and users should be aware of potential limitations in representation.", "### Recommendations\n\nUsers should consider the dataset's limitations and supplement it with additional sources for a more comprehensive understanding of artistic prompts.\n\n[optional]\n\nBibTeX:\n\n[Provide BibTeX Information]\n\nAPA:\n\n[Provide APA Information]", "## Glossary [optional]\n\n[No specific terms or calculations in this context]", "## More Information [optional]\n\n[Any additional information you'd like to provide]", "## Dataset Card Authors [optional]\n\n[Your Name or Organization]", "## Dataset Card Contact\n\n[Your Contact Information]" ]

[ 14, 14, 4, 90, 13, 3, 56, 39, 56, 5, 37, 4, 45, 30, 25, 22, 55, 59, 19, 19, 17, 11 ]

[ "passage: TAGS\n#license-apache-2.0 #region-us \n# Dataset Card for Midjourney Art Prompts Dataset## Dataset Details### Dataset Description\n\nThe Midjourney Art Prompts Dataset is a meticulously curated collection of prompts designed for training or fine-tuning Language Model Prompt Generators. Each prompt is crafted to evoke specific artistic styles, themes, and details, providing a diverse and comprehensive set for creative language model outputs.\n\n- Curated by: Mohammed Shojaei \n- License: Apache-2.0### Dataset Sources\n\n- Repository: [URL## Uses### Direct Use\n\nThe dataset is intended for use in training or fine-tuning Language Models, particularly those focused on generating artistic prompts. It provides a rich set of subjects and styles to enhance the creative capabilities of prompt-based language models.### Out-of-Scope Use\n\nMisuse, malicious use, and uses not suitable for generating creative prompts are considered out-of-scope for this dataset.## Dataset Structure\n\nThe dataset is provided in CSV format with two main columns:\n\n- subject: Describes the subject matter of the prompt.\n- prompt: Provides detailed information about the artistic style, elements, and specifications for the prompt.## Dataset Creation### Curation Rationale\n\nThe dataset is created to support the development and enhancement of Language Model Prompt Generators, specifically in the domain of artistic prompts.### Source Data#### Data Collection and Processing\n\nThe prompts are curated with a focus on diverse artistic styles and themes. Data collection involves selecting prompts that showcase varied subjects and detailed specifications.#### Who are the source data producers?\n\nThe prompts are sourced from various artistic contexts and contributors mentioned in the dataset.### Annotations [optional]\n\nThis dataset does not involve additional annotations beyond the provided prompts.#### Personal and Sensitive Information\n\nThe dataset does not contain personal, sensitive, or private information." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"araproje_hellaswag_tr_conf2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"araproje_hellaswag_tr_conf2\"\n\nMore Information needed" ]

[ 6, 22 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_hellaswag_tr_conf2\"\n\nMore Information needed" ]

[ "region:us" ]

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[]

[ "# Dataset Card for Evaluation run of Weyaxi/MetaMath-Tulpar-7b-v2-Slerp\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/MetaMath-Tulpar-7b-v2-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T14:51:30.669474(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Weyaxi/MetaMath-Tulpar-7b-v2-Slerp\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/MetaMath-Tulpar-7b-v2-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T14:51:30.669474(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Weyaxi/MetaMath-Tulpar-7b-v2-Slerp\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/MetaMath-Tulpar-7b-v2-Slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T14:51:30.669474(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of 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" ]

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[]

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

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

[ 6, 15 ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"pairwise_classification_synthetic_gpt4_10k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"pairwise_classification_synthetic_gpt4_10k\"\n\nMore Information needed" ]

[ 6, 26 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"pairwise_classification_synthetic_gpt4_10k\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of TencentARC/LLaMA-Pro-8B\n\n\n\nDataset automatically created during the evaluation run of model TencentARC/LLaMA-Pro-8B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T15:06:36.564331(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 TencentARC/LLaMA-Pro-8B\n\n\n\nDataset automatically created during the evaluation run of model TencentARC/LLaMA-Pro-8B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T15:06:36.564331(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 TencentARC/LLaMA-Pro-8B\n\n\n\nDataset automatically created during the evaluation run of model TencentARC/LLaMA-Pro-8B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T15:06:36.564331(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

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

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[ "# Natural Instructions v2 Winogrande Tasks\n\n- Project: URL\n- Data source: DataProvenanceInitiative/niv2_submix_original", "## Details\n\nThis dataset contains all Winogrande examples that were included in the Flan 2022 collection which were orignally published in Super-Natural-Instructions.\n\nThe data is copied from the preprocessed Natural Instructions v2 dataset at DataProvenanceInitiative/niv2_submix_original.\n\nThese tasks are:\n1. 'task029_winogrande_full_object': Creating a pair of fill in the blank question-answer pairs on objects.\t\n2. 'task030_winogrande_full_person': Creating a pair of fill in the blank questions on persons.\t\n3. 'task031_winogrande_question_generation_object': Writing a fill in the blank question on objects.\t\n4. 'task032_winogrande_question_generation_person': Writing a fill in the blank question on persons.\t\n5. 'task033_winogrande_answer_generation': Answering a fill in the blank question on objects.\t\n6. 'task034_winogrande_question_modification_object': Modifying a fill in the blank question on objects.\t\n7. 'task035_winogrande_question_modification_person': Modifying a fill in the blank question on persons.\t\n8. 'task1391_winogrande_easy_answer_generation': Answering a fill in the blank question on objects.", "### Fields\n\n- 'inputs': a 'string' feature.\n- 'targets': a 'string' feature.\n- 'task_source': a 'string' feature.\n- 'task_name': a 'string' feature.\n- 'template_type': a 'string' feature." ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Natural Instructions v2 Winogrande Tasks\n\n- Project: URL\n- Data source: DataProvenanceInitiative/niv2_submix_original", "## Details\n\nThis dataset contains all Winogrande examples that were included in the Flan 2022 collection which were orignally published in Super-Natural-Instructions.\n\nThe data is copied from the preprocessed Natural Instructions v2 dataset at DataProvenanceInitiative/niv2_submix_original.\n\nThese tasks are:\n1. 'task029_winogrande_full_object': Creating a pair of fill in the blank question-answer pairs on objects.\t\n2. 'task030_winogrande_full_person': Creating a pair of fill in the blank questions on persons.\t\n3. 'task031_winogrande_question_generation_object': Writing a fill in the blank question on objects.\t\n4. 'task032_winogrande_question_generation_person': Writing a fill in the blank question on persons.\t\n5. 'task033_winogrande_answer_generation': Answering a fill in the blank question on objects.\t\n6. 'task034_winogrande_question_modification_object': Modifying a fill in the blank question on objects.\t\n7. 'task035_winogrande_question_modification_person': Modifying a fill in the blank question on persons.\t\n8. 'task1391_winogrande_easy_answer_generation': Answering a fill in the blank question on objects.", "### Fields\n\n- 'inputs': a 'string' feature.\n- 'targets': a 'string' feature.\n- 'task_source': a 'string' feature.\n- 'task_name': a 'string' feature.\n- 'template_type': a 'string' feature." ]

[ 14, 36, 333, 72 ]

[ "passage: TAGS\n#license-apache-2.0 #region-us \n# Natural Instructions v2 Winogrande Tasks\n\n- Project: URL\n- Data source: DataProvenanceInitiative/niv2_submix_original## Details\n\nThis dataset contains all Winogrande examples that were included in the Flan 2022 collection which were orignally published in Super-Natural-Instructions.\n\nThe data is copied from the preprocessed Natural Instructions v2 dataset at DataProvenanceInitiative/niv2_submix_original.\n\nThese tasks are:\n1. 'task029_winogrande_full_object': Creating a pair of fill in the blank question-answer pairs on objects.\t\n2. 'task030_winogrande_full_person': Creating a pair of fill in the blank questions on persons.\t\n3. 'task031_winogrande_question_generation_object': Writing a fill in the blank question on objects.\t\n4. 'task032_winogrande_question_generation_person': Writing a fill in the blank question on persons.\t\n5. 'task033_winogrande_answer_generation': Answering a fill in the blank question on objects.\t\n6. 'task034_winogrande_question_modification_object': Modifying a fill in the blank question on objects.\t\n7. 'task035_winogrande_question_modification_person': Modifying a fill in the blank question on persons.\t\n8. 'task1391_winogrande_easy_answer_generation': Answering a fill in the blank question on objects.### Fields\n\n- 'inputs': a 'string' feature.\n- 'targets': a 'string' feature.\n- 'task_source': a 'string' feature.\n- 'task_name': a 'string' feature.\n- 'template_type': a 'string' feature." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of Reverb/Mistral-7B-LoreWeaver\n\n\n\nDataset automatically created during the evaluation run of model Reverb/Mistral-7B-LoreWeaver on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T15:58:22.377519(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Reverb/Mistral-7B-LoreWeaver\n\n\n\nDataset automatically created during the evaluation run of model Reverb/Mistral-7B-LoreWeaver on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T15:58:22.377519(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Reverb/Mistral-7B-LoreWeaver\n\n\n\nDataset automatically created during the evaluation run of model Reverb/Mistral-7B-LoreWeaver on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T15:58:22.377519(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

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

[]

[]

[ "### Dataset Summary\n\n\nWinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'sentence': a 'string' feature.\n* 'option1': a 'string' feature.\n* 'option2': a 'string' feature.\n* 'answer': a 'string' feature.", "### Data Splits", "### Contributions\n\n\nThanks to @thomwolf, @TevenLeScao, @patrickvonplaten, @lewtun for adding this dataset." ]

[ "TAGS\n#license-cc-by-4.0 #region-us \n", "### Dataset Summary\n\n\nWinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.", "### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'sentence': a 'string' feature.\n* 'option1': a 'string' feature.\n* 'option2': a 'string' feature.\n* 'answer': a 'string' feature.", "### Data Splits", "### Contributions\n\n\nThanks to @thomwolf, @TevenLeScao, @patrickvonplaten, @lewtun for adding this dataset." ]

[ 15, 94, 65, 5, 35 ]

[ "passage: TAGS\n#license-cc-by-4.0 #region-us \n### Dataset Summary\n\n\nWinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern\n2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a\nfill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires\ncommonsense reasoning.### Data Fields\n\n\nThe data fields are the same among all splits.\n\n\n* 'sentence': a 'string' feature.\n* 'option1': a 'string' feature.\n* 'option2': a 'string' feature.\n* 'answer': a 'string' feature.### Data Splits### Contributions\n\n\nThanks to @thomwolf, @TevenLeScao, @patrickvonplaten, @lewtun for adding this dataset." ]

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

[]

[ "en" ]

[ "## Data Introduction\n\nOver 1.5 Million synthetically generated ground-truth/OCR pairs for post correction tasks from our paper \"Large Synthetic Data from the ar𝜒iv for OCR Post Correction of Historic Scientific Articles\".\n\nSynthetic ground truth (SGT) sentences have been mined from the ar𝜒iv Bulk Downloads source documents, \nand Optical Character Recognition (OCR) \nsentences have been generated with the Tesseract OCR engine on the PDF pages generated from compiled source documents.\n\nSGT/OCR pairs come from astronomy articles in the years 1991-2011.\n\nNo page augmentation has been applied to any of the PDF documents (i.e. these are \"clean\" pages without warping, dust, etc.)", "## Resources", "### Dataset Versions\n\n* V0 (original released with original paper) is available here\n\nPlease reference the following if you make use of this dataset:" ]

[ "TAGS\n#task_categories-text-classification #task_categories-translation #size_categories-1M<n<10M #language-English #license-apache-2.0 #region-us \n", "## Data Introduction\n\nOver 1.5 Million synthetically generated ground-truth/OCR pairs for post correction tasks from our paper \"Large Synthetic Data from the ar𝜒iv for OCR Post Correction of Historic Scientific Articles\".\n\nSynthetic ground truth (SGT) sentences have been mined from the ar𝜒iv Bulk Downloads source documents, \nand Optical Character Recognition (OCR) \nsentences have been generated with the Tesseract OCR engine on the PDF pages generated from compiled source documents.\n\nSGT/OCR pairs come from astronomy articles in the years 1991-2011.\n\nNo page augmentation has been applied to any of the PDF documents (i.e. these are \"clean\" pages without warping, dust, etc.)", "## Resources", "### Dataset Versions\n\n* V0 (original released with original paper) is available here\n\nPlease reference the following if you make use of this dataset:" ]

[ 50, 174, 3, 32 ]

[ "passage: TAGS\n#task_categories-text-classification #task_categories-translation #size_categories-1M<n<10M #language-English #license-apache-2.0 #region-us \n## Data Introduction\n\nOver 1.5 Million synthetically generated ground-truth/OCR pairs for post correction tasks from our paper \"Large Synthetic Data from the ar𝜒iv for OCR Post Correction of Historic Scientific Articles\".\n\nSynthetic ground truth (SGT) sentences have been mined from the ar𝜒iv Bulk Downloads source documents, \nand Optical Character Recognition (OCR) \nsentences have been generated with the Tesseract OCR engine on the PDF pages generated from compiled source documents.\n\nSGT/OCR pairs come from astronomy articles in the years 1991-2011.\n\nNo page augmentation has been applied to any of the PDF documents (i.e. these are \"clean\" pages without warping, dust, etc.)## Resources### Dataset Versions\n\n* V0 (original released with original paper) is available here\n\nPlease reference the following if you make use of this dataset:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984\n\n\n\nDataset automatically created during the evaluation run of model EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T15:51:45.493023(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984\n\n\n\nDataset automatically created during the evaluation run of model EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T15:51:45.493023(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 209, 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 EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984\n\n\n\nDataset automatically created during the evaluation run of model EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T15:51:45.493023(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases 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" ]

[]

[]

[ "# Dataset Card for Evaluation run of EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-15936\n\n\n\nDataset automatically created during the evaluation run of model EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-15936 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T15:58:44.045673(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-15936\n\n\n\nDataset automatically created during the evaluation run of model EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-15936 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T15:58:44.045673(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 209, 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 EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-15936\n\n\n\nDataset automatically created during the evaluation run of model EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-15936 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T15:58:44.045673(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases 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" ]

[]

[]

[ "# Dataset Card for \"araproje_hellaswag_en_conf1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"araproje_hellaswag_en_conf1\"\n\nMore Information needed" ]

[ 6, 22 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_hellaswag_en_conf1\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"araproje_hellaswag_en_conf2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"araproje_hellaswag_en_conf2\"\n\nMore Information needed" ]

[ 6, 22 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_hellaswag_en_conf2\"\n\nMore Information needed" ]

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

[]

[]

[ "# The original Winograd Schema Challenge (WSC) as hosted by Ernest Davis", "## Dataset Description\n\n- Homepage: URL\n- Paper: URL", "### Dataset Summary\n\nThe original Winograd Schema Challenge (WSC) consisted of 136 schemas resulting in 273 problems. This was later expanded to 150 schemas resulting in 285 problems.\n\nA Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is\nresolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its\nresolution. The schema takes its name from a well-known example by Terry Winograd:\n\n> The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.\n\nIf the word is \"feared\", then \"they\" presumably refers to the city council; if it is \"advocated\" then \"they\"\npresumably refers to the demonstrators.", "## Dataset Structure", "### Data Instances\n\nEach instance contains a text passage with a designated pronoun and two possible answers indicating which entity in\nthe passage the pronoun represents. An example instance looks like the following:", "### Data Fields\n\n- 'text' (str): The text sequence\n- 'options' (list[str]): The two entity options that the pronoun may be referring to\n- 'label' (int): The index of the correct option in the 'options' field\n- 'pronoun' (str): The pronoun in the sequence to be resolved\n- 'pronoun_loc' (int): The starting position of the pronoun in the sequence\n- 'quote' (str): The substr with the key action or context surrounding the pronoun\n- 'quote_loc' (int): The starting position of the quote in the sequence\n- 'source' (str): A description of the source who contributed the example", "### Licensing Information\n\nThis work is licensed under a Creative Commons Attribution 4.0 International\nLicense.\n\n\n\nThe Winograd Schema Challenge including many of the examples here was proposed by\nLevesque et al 2012:", "### Contributions\n\nModified from loading script of: @joeddav." ]

[ "TAGS\n#license-cc-by-4.0 #region-us \n", "# The original Winograd Schema Challenge (WSC) as hosted by Ernest Davis", "## Dataset Description\n\n- Homepage: URL\n- Paper: URL", "### Dataset Summary\n\nThe original Winograd Schema Challenge (WSC) consisted of 136 schemas resulting in 273 problems. This was later expanded to 150 schemas resulting in 285 problems.\n\nA Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is\nresolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its\nresolution. The schema takes its name from a well-known example by Terry Winograd:\n\n> The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.\n\nIf the word is \"feared\", then \"they\" presumably refers to the city council; if it is \"advocated\" then \"they\"\npresumably refers to the demonstrators.", "## Dataset Structure", "### Data Instances\n\nEach instance contains a text passage with a designated pronoun and two possible answers indicating which entity in\nthe passage the pronoun represents. An example instance looks like the following:", "### Data Fields\n\n- 'text' (str): The text sequence\n- 'options' (list[str]): The two entity options that the pronoun may be referring to\n- 'label' (int): The index of the correct option in the 'options' field\n- 'pronoun' (str): The pronoun in the sequence to be resolved\n- 'pronoun_loc' (int): The starting position of the pronoun in the sequence\n- 'quote' (str): The substr with the key action or context surrounding the pronoun\n- 'quote_loc' (int): The starting position of the quote in the sequence\n- 'source' (str): A description of the source who contributed the example", "### Licensing Information\n\nThis work is licensed under a Creative Commons Attribution 4.0 International\nLicense.\n\n\n\nThe Winograd Schema Challenge including many of the examples here was proposed by\nLevesque et al 2012:", "### Contributions\n\nModified from loading script of: @joeddav." ]

[ 15, 19, 12, 187, 6, 46, 163, 44, 18 ]

[ "passage: TAGS\n#license-cc-by-4.0 #region-us \n# The original Winograd Schema Challenge (WSC) as hosted by Ernest Davis## Dataset Description\n\n- Homepage: URL\n- Paper: URL### Dataset Summary\n\nThe original Winograd Schema Challenge (WSC) consisted of 136 schemas resulting in 273 problems. This was later expanded to 150 schemas resulting in 285 problems.\n\nA Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is\nresolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its\nresolution. The schema takes its name from a well-known example by Terry Winograd:\n\n> The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.\n\nIf the word is \"feared\", then \"they\" presumably refers to the city council; if it is \"advocated\" then \"they\"\npresumably refers to the demonstrators.## Dataset Structure### Data Instances\n\nEach instance contains a text passage with a designated pronoun and two possible answers indicating which entity in\nthe passage the pronoun represents. An example instance looks like the following:### Data Fields\n\n- 'text' (str): The text sequence\n- 'options' (list[str]): The two entity options that the pronoun may be referring to\n- 'label' (int): The index of the correct option in the 'options' field\n- 'pronoun' (str): The pronoun in the sequence to be resolved\n- 'pronoun_loc' (int): The starting position of the pronoun in the sequence\n- 'quote' (str): The substr with the key action or context surrounding the pronoun\n- 'quote_loc' (int): The starting position of the quote in the sequence\n- 'source' (str): A description of the source who contributed the example### Licensing Information\n\nThis work is licensed under a Creative Commons Attribution 4.0 International\nLicense.\n\n\n\nThe Winograd Schema Challenge including many of the examples here was proposed by\nLevesque et al 2012:" ]

[ "license:cc-by-4.0", "arxiv:1806.08730", "region:us" ]

[ "1806.08730" ]

[]

[ "### Dataset Summary\n\n\nExamples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.\nThis Modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### default\n\n\n* Size of downloaded dataset files: 0.02 MB\n* Size of the generated dataset: 0.04 MB\n* Total amount of disk used: 0.06 MB\n\n\nAn example looks as follows:", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### default\n\n\n* 'sentence': a 'string' feature.\n* 'question': a 'string' feature.\n* 'options': a 'list' of 'string' features.\n* 'answer': a 'string' feature.", "### Data Splits", "### Licensing Information\n\n\nOur code for running decaNLP has been open sourced under BSD-3-Clause.\n\n\nWe chose to restrict decaNLP to datasets that were free and publicly accessible for research, but you should check their individual terms if you deviate from this use case.\n\n\nFrom the Winograd Schema Challenge:\n\n\n\n> \n> Both versions of the collections are licenced under a Creative Commons Attribution 4.0 International License.\n> \n> \n> \n\n\nIf you use this in your work, please cite:", "### Contributions\n\n\nThanks to @thomwolf, @lewtun, @ghomasHudson, @lhoestq for adding this dataset." ]

[ "TAGS\n#license-cc-by-4.0 #arxiv-1806.08730 #region-us \n", "### Dataset Summary\n\n\nExamples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.\nThis Modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.\n\n\nDataset Structure\n-----------------", "### Data Instances", "#### default\n\n\n* Size of downloaded dataset files: 0.02 MB\n* Size of the generated dataset: 0.04 MB\n* Total amount of disk used: 0.06 MB\n\n\nAn example looks as follows:", "### Data Fields\n\n\nThe data fields are the same among all splits.", "#### default\n\n\n* 'sentence': a 'string' feature.\n* 'question': a 'string' feature.\n* 'options': a 'list' of 'string' features.\n* 'answer': a 'string' feature.", "### Data Splits", "### Licensing Information\n\n\nOur code for running decaNLP has been open sourced under BSD-3-Clause.\n\n\nWe chose to restrict decaNLP to datasets that were free and publicly accessible for research, but you should check their individual terms if you deviate from this use case.\n\n\nFrom the Winograd Schema Challenge:\n\n\n\n> \n> Both versions of the collections are licenced under a Creative Commons Attribution 4.0 International License.\n> \n> \n> \n\n\nIf you use this in your work, please cite:", "### Contributions\n\n\nThanks to @thomwolf, @lewtun, @ghomasHudson, @lhoestq for adding this dataset." ]

[ 24, 74, 6, 44, 17, 55, 5, 110, 34 ]

[ "passage: TAGS\n#license-cc-by-4.0 #arxiv-1806.08730 #region-us \n### Dataset Summary\n\n\nExamples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.\nThis Modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.\n\n\nDataset Structure\n-----------------### Data Instances#### default\n\n\n* Size of downloaded dataset files: 0.02 MB\n* Size of the generated dataset: 0.04 MB\n* Total amount of disk used: 0.06 MB\n\n\nAn example looks as follows:### Data Fields\n\n\nThe data fields are the same among all splits.#### default\n\n\n* 'sentence': a 'string' feature.\n* 'question': a 'string' feature.\n* 'options': a 'list' of 'string' features.\n* 'answer': a 'string' feature.### Data Splits### Licensing Information\n\n\nOur code for running decaNLP has been open sourced under BSD-3-Clause.\n\n\nWe chose to restrict decaNLP to datasets that were free and publicly accessible for research, but you should check their individual terms if you deviate from this use case.\n\n\nFrom the Winograd Schema Challenge:\n\n\n\n> \n> Both versions of the collections are licenced under a Creative Commons Attribution 4.0 International License.\n> \n> \n> \n\n\nIf you use this in your work, please cite:### Contributions\n\n\nThanks to @thomwolf, @lewtun, @ghomasHudson, @lhoestq for adding this dataset." ]

[ "region:us" ]

[]

[]

[ "# Dolly 15k Curated", "## Dataset Details", "### Dataset Description\n\nA filtered and curated dataset version of URL Saved in HF Chat format. The result is a high quality dataset for SFT.\n\n- Created by: dctanner and the team at Sablo AI\n- License: CC BY-SA 3.0", "## Dataset Structure\n\nWe structure the dataset using the format commonly used as input into Hugging Face Chat Templates. Where present, the context field text has been appending to the instruction in OpenAI style 'Text: \"\"\"...\"\"\"' format.", "## Dataset Creation", "### Source Data\n\n- Source Dataset: URL and URL", "#### Data Collection and Processing\n\nWe started with URL (en split only) which is a manually curated version of URL\n\nAs well as formatting to fit HF Chat style, we removed many duplicates based on the instruction text. This ensures the dataset is diverse and not repetitive.", "# License\n\n- License: CC BY-SA 3.0\n\nThis dataset is usable for commercial purposes. Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license:\n- Wikipedia (various pages) - URL - Copyright © Wikipedia editors and contributors.\n- Databricks (URL) - Copyright © Databricks", "# Contact\n\nCreated by dctanner and the team at Sablo AI" ]

[ "TAGS\n#region-us \n", "# Dolly 15k Curated", "## Dataset Details", "### Dataset Description\n\nA filtered and curated dataset version of URL Saved in HF Chat format. The result is a high quality dataset for SFT.\n\n- Created by: dctanner and the team at Sablo AI\n- License: CC BY-SA 3.0", "## Dataset Structure\n\nWe structure the dataset using the format commonly used as input into Hugging Face Chat Templates. Where present, the context field text has been appending to the instruction in OpenAI style 'Text: \"\"\"...\"\"\"' format.", "## Dataset Creation", "### Source Data\n\n- Source Dataset: URL and URL", "#### Data Collection and Processing\n\nWe started with URL (en split only) which is a manually curated version of URL\n\nAs well as formatting to fit HF Chat style, we removed many duplicates based on the instruction text. This ensures the dataset is diverse and not repetitive.", "# License\n\n- License: CC BY-SA 3.0\n\nThis dataset is usable for commercial purposes. Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license:\n- Wikipedia (various pages) - URL - Copyright © Wikipedia editors and contributors.\n- Databricks (URL) - Copyright © Databricks", "# Contact\n\nCreated by dctanner and the team at Sablo AI" ]

[ 6, 7, 4, 59, 58, 5, 12, 63, 81, 15 ]

[ "passage: TAGS\n#region-us \n# Dolly 15k Curated## Dataset Details### Dataset Description\n\nA filtered and curated dataset version of URL Saved in HF Chat format. The result is a high quality dataset for SFT.\n\n- Created by: dctanner and the team at Sablo AI\n- License: CC BY-SA 3.0## Dataset Structure\n\nWe structure the dataset using the format commonly used as input into Hugging Face Chat Templates. Where present, the context field text has been appending to the instruction in OpenAI style 'Text: \"\"\"...\"\"\"' format.## Dataset Creation### Source Data\n\n- Source Dataset: URL and URL#### Data Collection and Processing\n\nWe started with URL (en split only) which is a manually curated version of URL\n\nAs well as formatting to fit HF Chat style, we removed many duplicates based on the instruction text. This ensures the dataset is diverse and not repetitive.# License\n\n- License: CC BY-SA 3.0\n\nThis dataset is usable for commercial purposes. Certain categories of material in the dataset include materials from the following sources, licensed under the CC BY-SA 3.0 license:\n- Wikipedia (various pages) - URL - Copyright © Wikipedia editors and contributors.\n- Databricks (URL) - Copyright © Databricks# Contact\n\nCreated by dctanner and the team at Sablo AI" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"nft_prediction_1_with_dates\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"nft_prediction_1_with_dates\"\n\nMore Information needed" ]

[ 6, 22 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"nft_prediction_1_with_dates\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of jondurbin/cinematika-7b-v0.1\n\n\n\nDataset automatically created during the evaluation run of model jondurbin/cinematika-7b-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T16:28:44.189724(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 jondurbin/cinematika-7b-v0.1\n\n\n\nDataset automatically created during the evaluation run of model jondurbin/cinematika-7b-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T16:28:44.189724(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 jondurbin/cinematika-7b-v0.1\n\n\n\nDataset automatically created during the evaluation run of model jondurbin/cinematika-7b-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T16:28:44.189724(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"araproje_hellaswag_en_conf3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"araproje_hellaswag_en_conf3\"\n\nMore Information needed" ]

[ 6, 22 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_hellaswag_en_conf3\"\n\nMore Information needed" ]

[ "task_categories:sentence-similarity", "size_categories:100K<n<1M", "source_datasets:BeIR", "language:en", "license:apache-2.0", "text", "region:us" ]

[]

[ "en" ]

[ "# BEIR/MTEB hard negatives dataset\n\nA dataset for quick evaluation of embedding models during their training.\n\nThe problem: running a full MTEB evaluation on a single GPU may take 10-20 hours. Most of this time is spent on embedding all 30M docs in all 10+ corpora. This dataset solves this problem by unwrapping a \"retrieval\" style benchmark into the \"reranking\" style:\n\n* We compute embeddings for all documents in the corpora with the intfloat/e5-base-v2 model.\n* For each corpus in BEIR/MTEB benchmark we build a Lucene index with text documents and their embeddings.\n* For each eval query we do a hybrid RRF-based retrieval for top-32 negatives\n\nAs BEIR testset is size-unbalanced (TREC-COVID is 42 queries, and MS MARCO is ~4000) we sample top-300 random queries from each dataset.\n\nIt takes around 30-60 seconds to perform eval using Nixietune on a single RTX 4090.\n\nA dataset in a nixietune compatible format:", "## Usage\n\nTo use with HF datasets:", "## License\n\nApache 2.0" ]

[ "TAGS\n#task_categories-sentence-similarity #size_categories-100K<n<1M #source_datasets-BeIR #language-English #license-apache-2.0 #text #region-us \n", "# BEIR/MTEB hard negatives dataset\n\nA dataset for quick evaluation of embedding models during their training.\n\nThe problem: running a full MTEB evaluation on a single GPU may take 10-20 hours. Most of this time is spent on embedding all 30M docs in all 10+ corpora. This dataset solves this problem by unwrapping a \"retrieval\" style benchmark into the \"reranking\" style:\n\n* We compute embeddings for all documents in the corpora with the intfloat/e5-base-v2 model.\n* For each corpus in BEIR/MTEB benchmark we build a Lucene index with text documents and their embeddings.\n* For each eval query we do a hybrid RRF-based retrieval for top-32 negatives\n\nAs BEIR testset is size-unbalanced (TREC-COVID is 42 queries, and MS MARCO is ~4000) we sample top-300 random queries from each dataset.\n\nIt takes around 30-60 seconds to perform eval using Nixietune on a single RTX 4090.\n\nA dataset in a nixietune compatible format:", "## Usage\n\nTo use with HF datasets:", "## License\n\nApache 2.0" ]

[ 54, 248, 12, 5 ]

[ "passage: TAGS\n#task_categories-sentence-similarity #size_categories-100K<n<1M #source_datasets-BeIR #language-English #license-apache-2.0 #text #region-us \n# BEIR/MTEB hard negatives dataset\n\nA dataset for quick evaluation of embedding models during their training.\n\nThe problem: running a full MTEB evaluation on a single GPU may take 10-20 hours. Most of this time is spent on embedding all 30M docs in all 10+ corpora. This dataset solves this problem by unwrapping a \"retrieval\" style benchmark into the \"reranking\" style:\n\n* We compute embeddings for all documents in the corpora with the intfloat/e5-base-v2 model.\n* For each corpus in BEIR/MTEB benchmark we build a Lucene index with text documents and their embeddings.\n* For each eval query we do a hybrid RRF-based retrieval for top-32 negatives\n\nAs BEIR testset is size-unbalanced (TREC-COVID is 42 queries, and MS MARCO is ~4000) we sample top-300 random queries from each dataset.\n\nIt takes around 30-60 seconds to perform eval using Nixietune on a single RTX 4090.\n\nA dataset in a nixietune compatible format:## Usage\n\nTo use with HF datasets:## License\n\nApache 2.0" ]

[ "task_categories:text-generation", "region:us" ]

[]

[]

[]

[ "TAGS\n#task_categories-text-generation #region-us \n" ]

[ 17 ]

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

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

[]

[]

[ "### Raw Text Version", "### Table Version" ]

[ "TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n", "### Raw Text Version", "### Table Version" ]

[ 44, 5, 4 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n### Raw Text Version### Table Version" ]

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

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-mit #medical #code #region-us \n" ]

[ 44 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-mit #medical #code #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of maywell/TinyLlama-MoE-Chat\n\n\n\nDataset automatically created during the evaluation run of model maywell/TinyLlama-MoE-Chat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T20:27:04.395344(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 maywell/TinyLlama-MoE-Chat\n\n\n\nDataset automatically created during the evaluation run of model maywell/TinyLlama-MoE-Chat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-05T20:27:04.395344(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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, 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 maywell/TinyLlama-MoE-Chat\n\n\n\nDataset automatically created during the evaluation run of model maywell/TinyLlama-MoE-Chat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-05T20:27:04.395344(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "task_categories:sentence-similarity", "size_categories:100K<n<1M", "source_datasets:BeIR", "language:en", "license:apache-2.0", "text", "region:us" ]

[]

[ "en" ]

[ "# Big Hard Negatives Dataset\n\nA dataset for training embedding models for semantic search.\n\nTODO: add desc\n\nA dataset in a nixietune compatible format:", "## Usage\n\nTo use with HF datasets:", "## License\n\nApache 2.0" ]

[ "TAGS\n#task_categories-sentence-similarity #size_categories-100K<n<1M #source_datasets-BeIR #language-English #license-apache-2.0 #text #region-us \n", "# Big Hard Negatives Dataset\n\nA dataset for training embedding models for semantic search.\n\nTODO: add desc\n\nA dataset in a nixietune compatible format:", "## Usage\n\nTo use with HF datasets:", "## License\n\nApache 2.0" ]

[ 54, 38, 12, 5 ]

[ "passage: TAGS\n#task_categories-sentence-similarity #size_categories-100K<n<1M #source_datasets-BeIR #language-English #license-apache-2.0 #text #region-us \n# Big Hard Negatives Dataset\n\nA dataset for training embedding models for semantic search.\n\nTODO: add desc\n\nA dataset in a nixietune compatible format:## Usage\n\nTo use with HF datasets:## License\n\nApache 2.0" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"training_v0.0.5-public\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"training_v0.0.5-public\"\n\nMore Information needed" ]

[ 6, 17 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"training_v0.0.5-public\"\n\nMore Information needed" ]

[ "task_categories:question-answering", "size_categories:n<1K", "language:cs", "license:cc", "doi:10.57967/hf/1609", "region:us" ]

[]

[ "cs" ]

[]

[ "TAGS\n#task_categories-question-answering #size_categories-n<1K #language-Czech #license-cc #doi-10.57967/hf/1609 #region-us \n" ]

[ 51 ]

[ "passage: TAGS\n#task_categories-question-answering #size_categories-n<1K #language-Czech #license-cc #doi-10.57967/hf/1609 #region-us \n" ]

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

[]

[ "en" ]

[ "# TinyLlama-1.1B\n</div>\n\nURL\n\nThe TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of \"just\" 90 days using 16 A100-40G GPUs . The training has started on 2023-09-01. \n\n\nWe adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.", "#### This Model\nThis is the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T. We follow HF's Zephyr's training recipe. The model was \" initially fine-tuned on a variant of the 'UltraChat' dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. \nWe then further aligned the model with TRL's 'DPOTrainer' on the openbmb/UltraFeedback dataset, which contain 64k prompts and model completions that are ranked by GPT-4.\"", "#### How to use\nYou will need the transformers>=4.34\nDo check the TinyLlama github page for more information." ]

[ "TAGS\n#language-English #license-apache-2.0 #region-us \n", "# TinyLlama-1.1B\n</div>\n\nURL\n\nThe TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of \"just\" 90 days using 16 A100-40G GPUs . The training has started on 2023-09-01. \n\n\nWe adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.", "#### This Model\nThis is the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T. We follow HF's Zephyr's training recipe. The model was \" initially fine-tuned on a variant of the 'UltraChat' dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. \nWe then further aligned the model with TRL's 'DPOTrainer' on the openbmb/UltraFeedback dataset, which contain 64k prompts and model completions that are ranked by GPT-4.\"", "#### How to use\nYou will need the transformers>=4.34\nDo check the TinyLlama github page for more information." ]

[ 18, 155, 146, 29 ]

[ "passage: TAGS\n#language-English #license-apache-2.0 #region-us \n# TinyLlama-1.1B\n</div>\n\nURL\n\nThe TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of \"just\" 90 days using 16 A100-40G GPUs . The training has started on 2023-09-01. \n\n\nWe adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.#### This Model\nThis is the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T. We follow HF's Zephyr's training recipe. The model was \" initially fine-tuned on a variant of the 'UltraChat' dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. \nWe then further aligned the model with TRL's 'DPOTrainer' on the openbmb/UltraFeedback dataset, which contain 64k prompts and model completions that are ranked by GPT-4.\"#### How to use\nYou will need the transformers>=4.34\nDo check the TinyLlama github page for more information." ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "language:en", "text-to-image", "stable-diffusion", "arxiv:2308.06721", "region:us" ]

[ "2308.06721" ]

[ "en" ]

[ "# IP-Adapter-FaceID Model Card\n\n\n<div align=\"center\">\n\nProject Page | Paper (ArXiv) | Code\n</div>\n\n---", "## Introduction\n\nAn experimental version of IP-Adapter-FaceID: we use face ID embedding from a face recognition model instead of CLIP image embedding, additionally, we use LoRA to improve ID consistency. IP-Adapter-FaceID can generate various style images conditioned on a face with only text prompts. \n\n!results\n\n\nUpdate 2023/12/27: \n\nIP-Adapter-FaceID-Plus: face ID embedding (for face ID) + CLIP image embedding (for face structure)\n\n<div align=\"center\"> \n\n!results\n</div>\n\nUpdate 2023/12/28: \n\nIP-Adapter-FaceID-PlusV2: face ID embedding (for face ID) + controllable CLIP image embedding (for face structure)\n\nYou can adjust the weight of the face structure to get different generation!\n\n<div align=\"center\"> \n\n!results\n</div>\n\nUpdate 2024/01/04: \n\nIP-Adapter-FaceID-SDXL: An experimental SDXL version of IP-Adapter-FaceID\n\n<div align=\"center\"> \n\n!results\n</div>", "## Usage", "### IP-Adapter-FaceID\n\nFirstly, you should use insightface to extract face ID embedding:\n\n\n\nThen, you can generate images conditioned on the face embeddings:", "### IP-Adapter-FaceID-SDXL\n\nFirstly, you should use insightface to extract face ID embedding:\n\n\n\nThen, you can generate images conditioned on the face embeddings:", "### IP-Adapter-FaceID-Plus\n\nFirstly, you should use insightface to extract face ID embedding and face image:\n\n\n\nThen, you can generate images conditioned on the face embeddings:", "## Limitations and Bias\n- The model does not achieve perfect photorealism and ID consistency.\n- The generalization of the model is limited due to limitations of the training data, base model and face recognition model.", "## Non-commercial use\nThis model is released exclusively for research purposes and is not intended for commercial use." ]

[ "TAGS\n#language-English #text-to-image #stable-diffusion #arxiv-2308.06721 #region-us \n", "# IP-Adapter-FaceID Model Card\n\n\n<div align=\"center\">\n\nProject Page | Paper (ArXiv) | Code\n</div>\n\n---", "## Introduction\n\nAn experimental version of IP-Adapter-FaceID: we use face ID embedding from a face recognition model instead of CLIP image embedding, additionally, we use LoRA to improve ID consistency. IP-Adapter-FaceID can generate various style images conditioned on a face with only text prompts. \n\n!results\n\n\nUpdate 2023/12/27: \n\nIP-Adapter-FaceID-Plus: face ID embedding (for face ID) + CLIP image embedding (for face structure)\n\n<div align=\"center\"> \n\n!results\n</div>\n\nUpdate 2023/12/28: \n\nIP-Adapter-FaceID-PlusV2: face ID embedding (for face ID) + controllable CLIP image embedding (for face structure)\n\nYou can adjust the weight of the face structure to get different generation!\n\n<div align=\"center\"> \n\n!results\n</div>\n\nUpdate 2024/01/04: \n\nIP-Adapter-FaceID-SDXL: An experimental SDXL version of IP-Adapter-FaceID\n\n<div align=\"center\"> \n\n!results\n</div>", "## Usage", "### IP-Adapter-FaceID\n\nFirstly, you should use insightface to extract face ID embedding:\n\n\n\nThen, you can generate images conditioned on the face embeddings:", "### IP-Adapter-FaceID-SDXL\n\nFirstly, you should use insightface to extract face ID embedding:\n\n\n\nThen, you can generate images conditioned on the face embeddings:", "### IP-Adapter-FaceID-Plus\n\nFirstly, you should use insightface to extract face ID embedding and face image:\n\n\n\nThen, you can generate images conditioned on the face embeddings:", "## Limitations and Bias\n- The model does not achieve perfect photorealism and ID consistency.\n- The generalization of the model is limited due to limitations of the training data, base model and face recognition model.", "## Non-commercial use\nThis model is released exclusively for research purposes and is not intended for commercial use." ]

[ 32, 37, 255, 3, 43, 46, 48, 46, 25 ]

[ "passage: TAGS\n#language-English #text-to-image #stable-diffusion #arxiv-2308.06721 #region-us \n# IP-Adapter-FaceID Model Card\n\n\n<div align=\"center\">\n\nProject Page | Paper (ArXiv) | Code\n</div>\n\n---## Introduction\n\nAn experimental version of IP-Adapter-FaceID: we use face ID embedding from a face recognition model instead of CLIP image embedding, additionally, we use LoRA to improve ID consistency. IP-Adapter-FaceID can generate various style images conditioned on a face with only text prompts. \n\n!results\n\n\nUpdate 2023/12/27: \n\nIP-Adapter-FaceID-Plus: face ID embedding (for face ID) + CLIP image embedding (for face structure)\n\n<div align=\"center\"> \n\n!results\n</div>\n\nUpdate 2023/12/28: \n\nIP-Adapter-FaceID-PlusV2: face ID embedding (for face ID) + controllable CLIP image embedding (for face structure)\n\nYou can adjust the weight of the face structure to get different generation!\n\n<div align=\"center\"> \n\n!results\n</div>\n\nUpdate 2024/01/04: \n\nIP-Adapter-FaceID-SDXL: An experimental SDXL version of IP-Adapter-FaceID\n\n<div align=\"center\"> \n\n!results\n</div>## Usage### IP-Adapter-FaceID\n\nFirstly, you should use insightface to extract face ID embedding:\n\n\n\nThen, you can generate images conditioned on the face embeddings:### IP-Adapter-FaceID-SDXL\n\nFirstly, you should use insightface to extract face ID embedding:\n\n\n\nThen, you can generate images conditioned on the face embeddings:### IP-Adapter-FaceID-Plus\n\nFirstly, you should use insightface to extract face ID embedding and face image:\n\n\n\nThen, you can generate images conditioned on the face embeddings:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"araproje_mmlu_tr_s1_split\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"araproje_mmlu_tr_s1_split\"\n\nMore Information needed" ]

[ 6, 24 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_mmlu_tr_s1_split\"\n\nMore Information needed" ]

[ "task_categories:image-classification", "size_categories:100K<n<1M", "license:other", "webdataset", "arxiv:1409.0575", "region:us" ]

[ "1409.0575" ]

[]

[ "## Dataset Description\n\n- Homepage: URL\n- Paper: URL", "### Dataset Summary\n\nILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a \"synonym set\" or \"synset\". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.\n\n This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the patch which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](URL please check the download section of the main website.", "### Data Splits\n\nUnlike ImageNet-1k (ILSVRC 2012), the full ImageNet dataset has no defined splits. This subset includes a validation split consiting of 40 samples per 11821 classes.", "#### Train\n* 'imagenet1k-train-{0000..1023}.tar'\n* 1281167 samples over 1024 shards", "#### Validation\n* 'imagenet1k-validation-{0000..0063}.tar'\n* 50000 samples over 63 shards", "### Processing\n\nThe webdataset shards were converted from TFDS shards matching the splits in TFDS ImageNet-1k.", "## Additional Information", "### Dataset Curators\n\nAuthors of [[1]](URL and [[2]](URL\n\n- Olga Russakovsky\n- Jia Deng\n- Hao Su\n- Jonathan Krause\n- Sanjeev Satheesh\n- Wei Dong\n- Richard Socher\n- Li-Jia Li\n- Kai Li\n- Sean Ma\n- Zhiheng Huang\n- Andrej Karpathy\n- Aditya Khosla\n- Michael Bernstein\n- Alexander C Berg\n- Li Fei-Fei", "### Licensing Information\n\nIn exchange for permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:\n\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.\n1. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.\n1. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.\n1. The law of the State of New Jersey shall apply to all disputes under this agreement." ]

[ "TAGS\n#task_categories-image-classification #size_categories-100K<n<1M #license-other #webdataset #arxiv-1409.0575 #region-us \n", "## Dataset Description\n\n- Homepage: URL\n- Paper: URL", "### Dataset Summary\n\nILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a \"synonym set\" or \"synset\". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.\n\n This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the patch which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](URL please check the download section of the main website.", "### Data Splits\n\nUnlike ImageNet-1k (ILSVRC 2012), the full ImageNet dataset has no defined splits. This subset includes a validation split consiting of 40 samples per 11821 classes.", "#### Train\n* 'imagenet1k-train-{0000..1023}.tar'\n* 1281167 samples over 1024 shards", "#### Validation\n* 'imagenet1k-validation-{0000..0063}.tar'\n* 50000 samples over 63 shards", "### Processing\n\nThe webdataset shards were converted from TFDS shards matching the splits in TFDS ImageNet-1k.", "## Additional Information", "### Dataset Curators\n\nAuthors of [[1]](URL and [[2]](URL\n\n- Olga Russakovsky\n- Jia Deng\n- Hao Su\n- Jonathan Krause\n- Sanjeev Satheesh\n- Wei Dong\n- Richard Socher\n- Li-Jia Li\n- Kai Li\n- Sean Ma\n- Zhiheng Huang\n- Andrej Karpathy\n- Aditya Khosla\n- Michael Bernstein\n- Alexander C Berg\n- Li Fei-Fei", "### Licensing Information\n\nIn exchange for permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:\n\n1. Researcher shall use the Database only for non-commercial research and educational purposes.\n1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.\n1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.\n1. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.\n1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.\n1. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.\n1. The law of the State of New Jersey shall apply to all disputes under this agreement." ]

[ 46, 12, 222, 50, 33, 35, 33, 5, 96, 327 ]

[ "passage: TAGS\n#task_categories-image-classification #size_categories-100K<n<1M #license-other #webdataset #arxiv-1409.0575 #region-us \n## Dataset Description\n\n- Homepage: URL\n- Paper: URL### Dataset Summary\n\nILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a \"synonym set\" or \"synset\". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.\n\n This dataset provides access to ImageNet (ILSVRC) 2012 which is the most commonly used subset of ImageNet. This dataset spans 1000 object classes and contains 1,281,167 training images, 50,000 validation images and 100,000 test images. The version also has the patch which fixes some of the corrupted test set images already applied. For full ImageNet dataset presented in [[2]](URL please check the download section of the main website.### Data Splits\n\nUnlike ImageNet-1k (ILSVRC 2012), the full ImageNet dataset has no defined splits. This subset includes a validation split consiting of 40 samples per 11821 classes.#### Train\n* 'imagenet1k-train-{0000..1023}.tar'\n* 1281167 samples over 1024 shards#### Validation\n* 'imagenet1k-validation-{0000..0063}.tar'\n* 50000 samples over 63 shards### Processing\n\nThe webdataset shards were converted from TFDS shards matching the splits in TFDS ImageNet-1k.## Additional Information" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"kdd210_hourly_24\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"kdd210_hourly_24\"\n\nMore Information needed" ]

[ 6, 19 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"kdd210_hourly_24\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 17 ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"kdd210_hourly_48\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"kdd210_hourly_48\"\n\nMore Information needed" ]

[ 6, 19 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"kdd210_hourly_48\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"kdd210_hourly_96\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"kdd210_hourly_96\"\n\nMore Information needed" ]

[ 6, 19 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"kdd210_hourly_96\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"kdd210_hourly_168\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"kdd210_hourly_168\"\n\nMore Information needed" ]

[ 6, 19 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"kdd210_hourly_168\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"kdd210_hourly_336\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"kdd210_hourly_336\"\n\nMore Information needed" ]

[ 6, 20 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"kdd210_hourly_336\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"araproje_mmlu_tr_conf1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"araproje_mmlu_tr_conf1\"\n\nMore Information needed" ]

[ 6, 21 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_mmlu_tr_conf1\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"araproje_mmlu_tr_conf2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"araproje_mmlu_tr_conf2\"\n\nMore Information needed" ]

[ 6, 21 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_mmlu_tr_conf2\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of FinancialSupport/saiga-7b\n\n\n\nDataset automatically created during the evaluation run of model FinancialSupport/saiga-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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-06T09:45:13.910276(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 FinancialSupport/saiga-7b\n\n\n\nDataset automatically created during the evaluation run of model FinancialSupport/saiga-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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-06T09:45:13.910276(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 FinancialSupport/saiga-7b\n\n\n\nDataset automatically created during the evaluation run of model FinancialSupport/saiga-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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-06T09:45:13.910276(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:unknown", "region:us" ]

[]

[]

[ "# Winograd Schema Challenge examples included in the SuperGLUE Benchmark\n\nSpecifically: The wsc and URL datasets from the HuggingFace \"super_glue\" repository.", "### Data Fields\n\n- 'text' (*'str'*): The text of the schema.\n- 'span1_index' (*'int'*): Starting word index of first entity.\n- 'span2_index' (*'int'*): Starting word index of second entity.\n- 'span1_text' (*'str'*): Textual representation of first entity.\n- 'span2_text' (*'str'*): Textual representation of second entity.\n- 'idx' (*'int'*): Index of the example in the dataset.\n- 'label' (*'bool'*): True if the two spans corefer.\n\n\"\"\"The primary SuperGLUE tasks are built on and derived from existing datasets. We refer users to the original licenses accompanying each dataset, but it is our understanding that these licenses allow for their use and redistribution in a research context.\"\"\"", "### Contributions\n\nThanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset." ]

[ "TAGS\n#license-unknown #region-us \n", "# Winograd Schema Challenge examples included in the SuperGLUE Benchmark\n\nSpecifically: The wsc and URL datasets from the HuggingFace \"super_glue\" repository.", "### Data Fields\n\n- 'text' (*'str'*): The text of the schema.\n- 'span1_index' (*'int'*): Starting word index of first entity.\n- 'span2_index' (*'int'*): Starting word index of second entity.\n- 'span1_text' (*'str'*): Textual representation of first entity.\n- 'span2_text' (*'str'*): Textual representation of second entity.\n- 'idx' (*'int'*): Index of the example in the dataset.\n- 'label' (*'bool'*): True if the two spans corefer.\n\n\"\"\"The primary SuperGLUE tasks are built on and derived from existing datasets. We refer users to the original licenses accompanying each dataset, but it is our understanding that these licenses allow for their use and redistribution in a research context.\"\"\"", "### Contributions\n\nThanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset." ]

[ 13, 45, 208, 28 ]

[ "passage: TAGS\n#license-unknown #region-us \n# Winograd Schema Challenge examples included in the SuperGLUE Benchmark\n\nSpecifically: The wsc and URL datasets from the HuggingFace \"super_glue\" repository.### Data Fields\n\n- 'text' (*'str'*): The text of the schema.\n- 'span1_index' (*'int'*): Starting word index of first entity.\n- 'span2_index' (*'int'*): Starting word index of second entity.\n- 'span1_text' (*'str'*): Textual representation of first entity.\n- 'span2_text' (*'str'*): Textual representation of second entity.\n- 'idx' (*'int'*): Index of the example in the dataset.\n- 'label' (*'bool'*): True if the two spans corefer.\n\n\"\"\"The primary SuperGLUE tasks are built on and derived from existing datasets. We refer users to the original licenses accompanying each dataset, but it is our understanding that these licenses allow for their use and redistribution in a research context.\"\"\"### Contributions\n\nThanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset." ]

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

[]

[]

[ "# Pronoun Disambiguation Problems (PDP) from the 2016 WSC as hosted by Ernest Davis\n\n\n60 pronoun disambiguation problems from URL", "### Data Fields\n\n- 'text' (str): The text sequence\n- 'options' (list[str]): The two entity options that the pronoun may be referring to\n- 'label' (int): The index of the correct option in the 'options' field\n- 'pronoun' (str): The pronoun in the sequence to be resolved\n- 'pronoun_loc' (int): The starting position of the pronoun in the sequence\n- 'quote' (str): The substr with the key action or context surrounding the pronoun\n- 'quote_loc' (int): The starting position of the quote in the sequence\n- 'source' (str): A description of the source who contributed the example" ]

[ "TAGS\n#license-cc-by-4.0 #region-us \n", "# Pronoun Disambiguation Problems (PDP) from the 2016 WSC as hosted by Ernest Davis\n\n\n60 pronoun disambiguation problems from URL", "### Data Fields\n\n- 'text' (str): The text sequence\n- 'options' (list[str]): The two entity options that the pronoun may be referring to\n- 'label' (int): The index of the correct option in the 'options' field\n- 'pronoun' (str): The pronoun in the sequence to be resolved\n- 'pronoun_loc' (int): The starting position of the pronoun in the sequence\n- 'quote' (str): The substr with the key action or context surrounding the pronoun\n- 'quote_loc' (int): The starting position of the quote in the sequence\n- 'source' (str): A description of the source who contributed the example" ]

[ 15, 35, 163 ]

[ "passage: TAGS\n#license-cc-by-4.0 #region-us \n# Pronoun Disambiguation Problems (PDP) from the 2016 WSC as hosted by Ernest Davis\n\n\n60 pronoun disambiguation problems from URL### Data Fields\n\n- 'text' (str): The text sequence\n- 'options' (list[str]): The two entity options that the pronoun may be referring to\n- 'label' (int): The index of the correct option in the 'options' field\n- 'pronoun' (str): The pronoun in the sequence to be resolved\n- 'pronoun_loc' (int): The starting position of the pronoun in the sequence\n- 'quote' (str): The substr with the key action or context surrounding the pronoun\n- 'quote_loc' (int): The starting position of the quote in the sequence\n- 'source' (str): A description of the source who contributed the example" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of UCLA-AGI/test-test\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/test-test on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-06T03:24:05.759125(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 UCLA-AGI/test-test\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/test-test on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-06T03:24:05.759125(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 UCLA-AGI/test-test\n\n\n\nDataset automatically created during the evaluation run of model UCLA-AGI/test-test on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-06T03:24:05.759125(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "task_categories:text-classification", "license:apache-2.0", "code", "arxiv:2102.07995", "region:us" ]

[ "2102.07995" ]

[]

[ "# D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis\nThis is an unofficial HuggingFace upload of the D2A dataset from \"D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis\". \"Test\" splits have all labels as -1 as they are not provided.\n\nUsage:\n\n*", "# D2A Leaderboard Data\n\nThis document describes D2A V1 Leaderboard data. You can download them from the Leaderboard section of the D2A Dataset page. To begin download directly you can click here.", "## Source files:\n\nThe files were created using the default security errors of datasets Libav, OpenSSL, Nginx, Httpd and Libtiff from D2A.\n\nThere are 4 directories corresponding to 4 tasks of the leaderboard. Each directory contains 3 csv files corresponding to the train (80%), dev (10%) and test (10%) split. \nThe columns in the split files are identical except the test split which does not contain the label column.", "## Columns:\n\n1. id: A unique id for every example in a task.\n2. label: Values are 0 or 1.\n\t1. Value 0: No vulnerability/defect in the example.\n\t2. Value 1: Example contains some vulnerability/defect.\n3. trace: Bug trace or bug report generated by Infer static analyzer. Infer predictions contain a lot of False positives which is why even 0 label examples have a bug report. \n4. bug_function/code: Full source code of the function where the vulnerability originates.\n5. bug_url: URL of the file which contains the bug_function.\n6. functions: Full source code of all the functions in the bug trace, with the duplicates removed. This will include the function in bug_function.", "## Default Security Errors:\n\nThese are security errors enabled by default by Infer.\n\n* BIABD_USE_AFTER_FREE\n* BUFFER_OVERRUN_L1\n* BUFFER_OVERRUN_L2\n* BUFFER_OVERRUN_L3\n* BUFFER_OVERRUN_R2\n* BUFFER_OVERRUN_S2\n* BUFFER_OVERRUN_T1\n* INTEGER_OVERFLOW_L1\n* INTEGER_OVERFLOW_L2\n* INTEGER_OVERFLOW_R2\n* MEMORY_LEAK\n* NULL_DEREFERENCE\n* RESOURCE_LEAK\n* LAB_RESOURCE_LEAK\n* UNINITIALIZED_VALUE\n* USE_AFTER_DELETE\n* USE_AFTER_FREE\n* USE_AFTER_LIFETIME", "## Data Examples:\n\n1. Trace:\n\n\n\n2. Bug URL:\n\n\n\n3. Bug Function:\n\n\n\n4. Functions:\n\n\n\nLeaderboard README || Leaderboard page" ]

[ "TAGS\n#task_categories-text-classification #license-apache-2.0 #code #arxiv-2102.07995 #region-us \n", "# D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis\nThis is an unofficial HuggingFace upload of the D2A dataset from \"D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis\". \"Test\" splits have all labels as -1 as they are not provided.\n\nUsage:\n\n*", "# D2A Leaderboard Data\n\nThis document describes D2A V1 Leaderboard data. You can download them from the Leaderboard section of the D2A Dataset page. To begin download directly you can click here.", "## Source files:\n\nThe files were created using the default security errors of datasets Libav, OpenSSL, Nginx, Httpd and Libtiff from D2A.\n\nThere are 4 directories corresponding to 4 tasks of the leaderboard. Each directory contains 3 csv files corresponding to the train (80%), dev (10%) and test (10%) split. \nThe columns in the split files are identical except the test split which does not contain the label column.", "## Columns:\n\n1. id: A unique id for every example in a task.\n2. label: Values are 0 or 1.\n\t1. Value 0: No vulnerability/defect in the example.\n\t2. Value 1: Example contains some vulnerability/defect.\n3. trace: Bug trace or bug report generated by Infer static analyzer. Infer predictions contain a lot of False positives which is why even 0 label examples have a bug report. \n4. bug_function/code: Full source code of the function where the vulnerability originates.\n5. bug_url: URL of the file which contains the bug_function.\n6. functions: Full source code of all the functions in the bug trace, with the duplicates removed. This will include the function in bug_function.", "## Default Security Errors:\n\nThese are security errors enabled by default by Infer.\n\n* BIABD_USE_AFTER_FREE\n* BUFFER_OVERRUN_L1\n* BUFFER_OVERRUN_L2\n* BUFFER_OVERRUN_L3\n* BUFFER_OVERRUN_R2\n* BUFFER_OVERRUN_S2\n* BUFFER_OVERRUN_T1\n* INTEGER_OVERFLOW_L1\n* INTEGER_OVERFLOW_L2\n* INTEGER_OVERFLOW_R2\n* MEMORY_LEAK\n* NULL_DEREFERENCE\n* RESOURCE_LEAK\n* LAB_RESOURCE_LEAK\n* UNINITIALIZED_VALUE\n* USE_AFTER_DELETE\n* USE_AFTER_FREE\n* USE_AFTER_LIFETIME", "## Data Examples:\n\n1. Trace:\n\n\n\n2. Bug URL:\n\n\n\n3. Bug Function:\n\n\n\n4. Functions:\n\n\n\nLeaderboard README || Leaderboard page" ]

[ 35, 96, 47, 109, 168, 191, 33 ]

[ "passage: TAGS\n#task_categories-text-classification #license-apache-2.0 #code #arxiv-2102.07995 #region-us \n# D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis\nThis is an unofficial HuggingFace upload of the D2A dataset from \"D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis\". \"Test\" splits have all labels as -1 as they are not provided.\n\nUsage:\n\n*# D2A Leaderboard Data\n\nThis document describes D2A V1 Leaderboard data. You can download them from the Leaderboard section of the D2A Dataset page. To begin download directly you can click here.## Source files:\n\nThe files were created using the default security errors of datasets Libav, OpenSSL, Nginx, Httpd and Libtiff from D2A.\n\nThere are 4 directories corresponding to 4 tasks of the leaderboard. Each directory contains 3 csv files corresponding to the train (80%), dev (10%) and test (10%) split. \nThe columns in the split files are identical except the test split which does not contain the label column.## Columns:\n\n1. id: A unique id for every example in a task.\n2. label: Values are 0 or 1.\n\t1. Value 0: No vulnerability/defect in the example.\n\t2. Value 1: Example contains some vulnerability/defect.\n3. trace: Bug trace or bug report generated by Infer static analyzer. Infer predictions contain a lot of False positives which is why even 0 label examples have a bug report. \n4. bug_function/code: Full source code of the function where the vulnerability originates.\n5. bug_url: URL of the file which contains the bug_function.\n6. functions: Full source code of all the functions in the bug trace, with the duplicates removed. This will include the function in bug_function." ]

[ "task_categories:text-classification", "code", "arxiv:1807.04320", "region:us" ]

[ "1807.04320" ]

[]

[]

[ "TAGS\n#task_categories-text-classification #code #arxiv-1807.04320 #region-us \n" ]

[ 27 ]

[ "passage: TAGS\n#task_categories-text-classification #code #arxiv-1807.04320 #region-us \n" ]

[ "task_categories:text-classification", "code", "arxiv:1801.01681", "region:us" ]

[ "1801.01681" ]

[]

[ "# Multiclass Vulnerability Dataset (MVD) \nMVD is a database for research on multiclass vulnerability detection with deep learning. The dataset is based on the NIST Software Assurance Reference Dataset (SARD) and National Vulnerability Database (NVD). Up to now, it has possessed 181641 code gadgets, covering 40 types of vulnerabilities. Each code gadget in MVD is composed of multiple program statements, which have direct or indirect data-dependence and control-dependence relationships with the library/API function calls. In total, the code gadgets in MVD are extracted from 33409 testcases of SARD and NVD, 138522 code gadgets of which are non-vulnerable and 43119 are vulnerable.\n\nIn this repository, the compressed file URL stores 181641 code gadgets and their corresponding labels. The file named URL records the mapping relationship between each label and the corresponding vulnerability. The folder source files contains 33,409 source files for extracting code gadgets." ]

[ "TAGS\n#task_categories-text-classification #code #arxiv-1801.01681 #region-us \n", "# Multiclass Vulnerability Dataset (MVD) \nMVD is a database for research on multiclass vulnerability detection with deep learning. The dataset is based on the NIST Software Assurance Reference Dataset (SARD) and National Vulnerability Database (NVD). Up to now, it has possessed 181641 code gadgets, covering 40 types of vulnerabilities. Each code gadget in MVD is composed of multiple program statements, which have direct or indirect data-dependence and control-dependence relationships with the library/API function calls. In total, the code gadgets in MVD are extracted from 33409 testcases of SARD and NVD, 138522 code gadgets of which are non-vulnerable and 43119 are vulnerable.\n\nIn this repository, the compressed file URL stores 181641 code gadgets and their corresponding labels. The file named URL records the mapping relationship between each label and the corresponding vulnerability. The folder source files contains 33,409 source files for extracting code gadgets." ]

[ 28, 232 ]

[ "passage: TAGS\n#task_categories-text-classification #code #arxiv-1801.01681 #region-us \n# Multiclass Vulnerability Dataset (MVD) \nMVD is a database for research on multiclass vulnerability detection with deep learning. The dataset is based on the NIST Software Assurance Reference Dataset (SARD) and National Vulnerability Database (NVD). Up to now, it has possessed 181641 code gadgets, covering 40 types of vulnerabilities. Each code gadget in MVD is composed of multiple program statements, which have direct or indirect data-dependence and control-dependence relationships with the library/API function calls. In total, the code gadgets in MVD are extracted from 33409 testcases of SARD and NVD, 138522 code gadgets of which are non-vulnerable and 43119 are vulnerable.\n\nIn this repository, the compressed file URL stores 181641 code gadgets and their corresponding labels. The file named URL records the mapping relationship between each label and the corresponding vulnerability. The folder source files contains 33,409 source files for extracting code gadgets." ]

[ "task_categories:text-classification", "code", "arxiv:2009.07235", "region:us" ]

[ "2009.07235" ]

[]

[]

[ "TAGS\n#task_categories-text-classification #code #arxiv-2009.07235 #region-us \n" ]

[ 27 ]

[ "passage: TAGS\n#task_categories-text-classification #code #arxiv-2009.07235 #region-us \n" ]