Datasets:

---

ftopal

/

huggingface-datasets

like 0

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of abhinand/malayalam-llama-7b-instruct-v0.1\n\n\n\nDataset automatically created during the evaluation run of model abhinand/malayalam-llama-7b-instruct-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T18:17:56.469419(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of abhinand/malayalam-llama-7b-instruct-v0.1\n\n\n\nDataset automatically created during the evaluation run of model abhinand/malayalam-llama-7b-instruct-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T18:17:56.469419(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Cartinoe5930/DARE-Merging\n\n\n\nDataset automatically created during the evaluation run of model Cartinoe5930/DARE-Merging on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T15:09:50.545148(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Cartinoe5930/DARE-Merging\n\n\n\nDataset automatically created during the evaluation run of model Cartinoe5930/DARE-Merging on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T15:09:50.545148(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 abhinand/tamil-llama-7b-instruct-v0.2\n\n\n\nDataset automatically created during the evaluation run of model abhinand/tamil-llama-7b-instruct-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T18:30:45.482735(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of abhinand/tamil-llama-7b-instruct-v0.2\n\n\n\nDataset automatically created during the evaluation run of model abhinand/tamil-llama-7b-instruct-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T18:30:45.482735(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 abhishekchohan/mistral-7B-forest\n\n\n\nDataset automatically created during the evaluation run of model abhishekchohan/mistral-7B-forest on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T15:31:25.138971(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of abhishekchohan/mistral-7B-forest\n\n\n\nDataset automatically created during the evaluation run of model abhishekchohan/mistral-7B-forest on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T15:31:25.138971(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:translation", "task_categories:text-generation", "size_categories:100K<n<1M", "language:zh", "language:ru", "language:th", "language:vi", "region:us" ]

[]

[ "zh", "ru", "th", "vi" ]

[ "# Qianyan Low-Resource NMT Dataset\n\n\"千言数据集：低资源语言翻译\" ，旨在帮助研究人员和开发者解决低资源语言翻译的问题。该数据集包含了中文和俄文的5万条双语平行语料，以及中文和泰文、中文和越南文各10万条目标端单语语料。\n对于泰文和越南文，使用谷歌翻译进行回译，从而生成对应的中文数据。\nsource=1表示中文到其他语言的翻译，source=0表示其他语言到中文的翻译，以便区分测试集的语言方向。\n详见：\nURL" ]

[ "TAGS\n#task_categories-translation #task_categories-text-generation #size_categories-100K<n<1M #language-Chinese #language-Russian #language-Thai #language-Vietnamese #region-us \n", "# Qianyan Low-Resource NMT Dataset\n\n\"千言数据集：低资源语言翻译\" ，旨在帮助研究人员和开发者解决低资源语言翻译的问题。该数据集包含了中文和俄文的5万条双语平行语料，以及中文和泰文、中文和越南文各10万条目标端单语语料。\n对于泰文和越南文，使用谷歌翻译进行回译，从而生成对应的中文数据。\nsource=1表示中文到其他语言的翻译，source=0表示其他语言到中文的翻译，以便区分测试集的语言方向。\n详见：\nURL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Cartinoe5930/MoE-Merging\n\n\n\nDataset automatically created during the evaluation run of model Cartinoe5930/MoE-Merging on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T16:02:03.400569(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Cartinoe5930/MoE-Merging\n\n\n\nDataset automatically created during the evaluation run of model Cartinoe5930/MoE-Merging on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T16:02:03.400569(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 AA051612/A0123\n\n\n\nDataset automatically created during the evaluation run of model AA051612/A0123 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T16:04:24.583795(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of AA051612/A0123\n\n\n\nDataset automatically created during the evaluation run of model AA051612/A0123 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T16:04:24.583795(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 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 2024-01-23T16:04:31.974189(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 2024-01-23T16:04:31.974189(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "hindi", "doi:10.57967/hf/1676", "region:us" ]

[]

[]

[ "# Summary\n'aaditya/databricks-dolly-15k-Hindi' is an open source Hindi version dataset of databricks/databricks-dolly-15k.\n\nThis dataset can be used for any purpose, whether academic or commercial, under the terms of the \nCreative Commons Attribution-ShareAlike 3.0 Unported License.\n\nSupported Tasks: \n- Training LLMs\n- Synthetic Data Generation\n- Data Augmentation\n \nLanguages: Hindi\n\nVersion: 1.0\n\nOriginal Dataset repo\nURL" ]

[ "TAGS\n#hindi #doi-10.57967/hf/1676 #region-us \n", "# Summary\n'aaditya/databricks-dolly-15k-Hindi' is an open source Hindi version dataset of databricks/databricks-dolly-15k.\n\nThis dataset can be used for any purpose, whether academic or commercial, under the terms of the \nCreative Commons Attribution-ShareAlike 3.0 Unported License.\n\nSupported Tasks: \n- Training LLMs\n- Synthetic Data Generation\n- Data Augmentation\n \nLanguages: Hindi\n\nVersion: 1.0\n\nOriginal Dataset repo\nURL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-ed3\n\n\n\nDataset automatically created during the evaluation run of model YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-ed3 on the Open LLM Leaderboard.\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 2024-01-23T17:21:37.026762(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-ed3\n\n\n\nDataset automatically created during the evaluation run of model YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-ed3 on the Open LLM Leaderboard.\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 2024-01-23T17:21:37.026762(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:question-answering", "task_categories:zero-shot-classification", "task_categories:feature-extraction", "task_categories:visual-question-answering", "task_categories:image-classification", "size_categories:10K<n<100K", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for \"CSUN_bedroom_VQA_feliu_v2\"\n\n\n!image/png" ]

[ "TAGS\n#task_categories-question-answering #task_categories-zero-shot-classification #task_categories-feature-extraction #task_categories-visual-question-answering #task_categories-image-classification #size_categories-10K<n<100K #language-English #license-mit #region-us \n", "# Dataset Card for \"CSUN_bedroom_VQA_feliu_v2\"\n\n\n!image/png" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-ed2\n\n\n\nDataset automatically created during the evaluation run of model YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-ed2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T17:04:25.517599(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-ed2\n\n\n\nDataset automatically created during the evaluation run of model YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-dpo-ed2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T17:04:25.517599(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 YouKnowMee/Mistral-7b-instruct-v0.2-summ-dpo-ed2\n\n\n\nDataset automatically created during the evaluation run of model YouKnowMee/Mistral-7b-instruct-v0.2-summ-dpo-ed2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T17:10:17.238798(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 YouKnowMee/Mistral-7b-instruct-v0.2-summ-dpo-ed2\n\n\n\nDataset automatically created during the evaluation run of model YouKnowMee/Mistral-7b-instruct-v0.2-summ-dpo-ed2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T17:10:17.238798(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-ed2\n\n\n\nDataset automatically created during the evaluation run of model YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-ed2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T17:11:58.023501(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-ed2\n\n\n\nDataset automatically created during the evaluation run of model YouKnowMee/Mistral-7b-instruct-v0.2-summ-sft-ed2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T17:11:58.023501(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Helper-Jhonny Code Mentor", "## Dataset Details", "### Dataset Description\n\nHelper-Jhonny is a curated dataset designed to support mentoring in code development across three programming languages: Python, JavaScript, and SQL. The dataset is focused on question-answering scenarios related to coding tasks. It aims to assist learners and developers in improving their skills and understanding of these programming languages.\n\n- Curated by: Helper-Jhonny Team\n- Funded by [optional]: Lara Ayne\n- Language(s) (NLP): Portuguese (pt), English (en), Spanish (es)\n- License: llama2", "### Dataset Sources [optional]\n\n- Repository: [Link to Repository]\n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use\n\nThe dataset is suitable for direct use in scenarios where learners and developers seek assistance and guidance in coding tasks. It can be utilized for building applications or platforms that provide real-time code mentoring and support.", "### Out-of-Scope Use\n\nThe dataset is not intended for misuse or malicious use. It may not work well for non-code related question-answering tasks.", "## Dataset Structure\n\nThe dataset contains information relevant to code mentoring, including questions and corresponding answers for Python, JavaScript, and SQL. Each entry is tagged with the programming language to facilitate language-specific mentoring.", "## Dataset Creation", "### Curation Rationale\n\nThe dataset was created to address the need for a comprehensive code mentoring resource, focusing on three widely used programming languages. The goal is to provide learners with practical guidance and support in their coding journey.", "### Source Data", "#### Data Collection and Processing\n\nThe dataset comprises questions and answers gathered from various code mentoring sessions. The data selection criteria include relevance to common coding challenges and tasks faced by learners and developers. The data collection process involves curating real-world coding queries and their solutions.", "#### Who are the source data producers?\n\nThe source data producers are experienced mentors and developers who actively contribute to the code mentoring community.", "### Annotations [optional]", "#### Annotation process\n\nAnnotations include tagging each entry with the respective programming language to ensure language-specific mentoring. Annotators are experienced mentors with expertise in Python, JavaScript, and SQL.", "#### Who are the annotators?\n\nAnnotations are performed by a team of skilled code mentors with proficiency in Python, JavaScript, and SQL.", "#### Personal and Sensitive Information\n\nThe dataset does not contain personal, sensitive, or private information.", "## Bias, Risks, and Limitations\n\nThe dataset may exhibit biases based on the expertise and perspectives of the annotators. Users should be aware that mentoring scenarios may not cover every edge case, and the dataset is not exhaustive.", "### Recommendations\n\nUsers should be made aware of the dataset's limitations and encouraged to supplement their learning with diverse resources." ]

[ "TAGS\n#region-us \n", "# Dataset Card for Helper-Jhonny Code Mentor", "## Dataset Details", "### Dataset Description\n\nHelper-Jhonny is a curated dataset designed to support mentoring in code development across three programming languages: Python, JavaScript, and SQL. The dataset is focused on question-answering scenarios related to coding tasks. It aims to assist learners and developers in improving their skills and understanding of these programming languages.\n\n- Curated by: Helper-Jhonny Team\n- Funded by [optional]: Lara Ayne\n- Language(s) (NLP): Portuguese (pt), English (en), Spanish (es)\n- License: llama2", "### Dataset Sources [optional]\n\n- Repository: [Link to Repository]\n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use\n\nThe dataset is suitable for direct use in scenarios where learners and developers seek assistance and guidance in coding tasks. It can be utilized for building applications or platforms that provide real-time code mentoring and support.", "### Out-of-Scope Use\n\nThe dataset is not intended for misuse or malicious use. It may not work well for non-code related question-answering tasks.", "## Dataset Structure\n\nThe dataset contains information relevant to code mentoring, including questions and corresponding answers for Python, JavaScript, and SQL. Each entry is tagged with the programming language to facilitate language-specific mentoring.", "## Dataset Creation", "### Curation Rationale\n\nThe dataset was created to address the need for a comprehensive code mentoring resource, focusing on three widely used programming languages. The goal is to provide learners with practical guidance and support in their coding journey.", "### Source Data", "#### Data Collection and Processing\n\nThe dataset comprises questions and answers gathered from various code mentoring sessions. The data selection criteria include relevance to common coding challenges and tasks faced by learners and developers. The data collection process involves curating real-world coding queries and their solutions.", "#### Who are the source data producers?\n\nThe source data producers are experienced mentors and developers who actively contribute to the code mentoring community.", "### Annotations [optional]", "#### Annotation process\n\nAnnotations include tagging each entry with the respective programming language to ensure language-specific mentoring. Annotators are experienced mentors with expertise in Python, JavaScript, and SQL.", "#### Who are the annotators?\n\nAnnotations are performed by a team of skilled code mentors with proficiency in Python, JavaScript, and SQL.", "#### Personal and Sensitive Information\n\nThe dataset does not contain personal, sensitive, or private information.", "## Bias, Risks, and Limitations\n\nThe dataset may exhibit biases based on the expertise and perspectives of the annotators. Users should be aware that mentoring scenarios may not cover every edge case, and the dataset is not exhaustive.", "### Recommendations\n\nUsers should be made aware of the dataset's limitations and encouraged to supplement their learning with diverse resources." ]

[ "task_categories:text-generation", "task_categories:text-to-audio", "task_categories:text-to-speech", "task_categories:text-to-image", "task_categories:audio-to-audio", "task_categories:image-to-image", "task_categories:question-answering", "task_ids:parsing", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "license:other", "python-copilot", "python-coding", "python-architecture", "knowledge-graphs", "multimodal", "text-image-audio", "fine-tuning", "training", "question-answering", "image-knowledge-graph", "alpaca", "mp3", "png", "text", "instruct", "class", "classes", "function", "functions", "inheritance", "region:us" ]

[]

[]

[ "### Image Training Examples\n\n\nThese graphs are focused on a high-level overview of how to use python:\n\n\n* classes\n* base classes\n* global functions\n* imports\n\n\nEach graph includes labeled objects, directionality, standardized colors, and a descriptive text box for all drawn objects.\n\n\nBelow are some extracted image samples:", "#### Class - Knowledge Graph Images\n\n\nHere are samples from the python copilot class image knowledge graph dataset (294.1 GB). These images attempt to teach how to use software with a networkx graph saved as a png with an alpaca text box:", "##### How to use the transformers/src/transformers/models/clip/configuration\\_clip.py CLIPConfig class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/configuration\\_clip.py CLIPConfig class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/configuration\\_clip.py CLIPOnnxConfig class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/configuration\\_clip.py CLIPOnnxConfig class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/tokenization\\_clip.py CLIPTokenizer class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/tokenization\\_clip.py CLIPTokenizer class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "#### Base Class - Inheritance and Polymorphism Knowledge Graph Images\n\n\nHere are samples from the python copilot base class inheritance and polymorphism image knowledge graph dataset (135 GB). These images attempt to teach how to use software with a networkx graph saved as a png with an alpaca text box:", "##### How to use the transformers/src/transformers/models/clip/configuration\\_clip.py CLIPConfig inherited base class(es)\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/configuration\\_clip.py CLIPConfig inherited base class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/tokenization\\_clip\\_fast.py CLIPTokenizerFast inherited base class(es)\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/tokenization\\_clip\\_fast.py CLIPTokenizerFast inherited base class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "#### Global Functions - Knowledge Graph Images\n\n\nHere are samples from the python copilot global functions image knowledge graph dataset (130 GB). These images attempt to teach how to use software with a networkx graph saved as a png with an alpaca text box:", "##### How to use the transformers/src/transformers/models/clip/convert\\_clip\\_original\\_pytorch\\_to\\_hf.py global functions\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/convert\\_clip\\_original\\_pytorch\\_to\\_hf.py global functions\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/tokenization\\_clip.py global functions\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/tokenization\\_clip.py global functions\" width=\"500\" style=\"display: block; margin: auto;\"/>", "#### Imports - Knowledge Graph Images\n\n\nHere are samples from the python copilot imports image knowledge graph dataset (211 GB). These images attempt to teach how to use software with a networkx graph saved as a png with an alpaca text box:", "##### How to use the transformers/src/transformers/models/clip/configuration\\_clip.py imports like the CLIPConfig class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/configuration\\_clip.py imports like the CLIPConfig class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/configuration\\_clip.py imports like the CLIPTextConfig class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/configuration\\_clip.py imports like the CLIPTextConfig class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/configuration\\_clip.py imports like the CLIPVisionConfig class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/configuration\\_clip.py imports like the CLIPVisionConfig class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/tokenization\\_clip\\_fast.py imports like the CLIPTokenizerFast class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/tokenization\\_clip\\_fast.py imports like the CLIPTokenizerFast class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "### Audio Training Examples - Question and Answering in Alpaca\n\n\nBelow are extracted question and answer mp3 samples. Each mp3 is either a recording of the alpaca question or answer. Question mp3s use a different speaker than the answer mp3 voice.\n\n\nNote: mobile browsers have issues playing the mp3s and show a question mark due to markdown failing to show the Listen link vs a confusing ? mark icon sorry!\n\n\n\nSchema High Level Design\n------------------------", "### Summary\n\n\nWe tried to build the schema to help others maximize available hardware (cpu/storage).", "### Background\n\n\nWe use a lot of python multiprocessing pools to concurrently search many parquet files in each of our datasets at once. To help others do the same, we included the recsize and desc\\_len columns that provide an estimate for \"how long\" each row will take to: draw as an image or record as an mp3. With these columns, we are able to maximize our hardware because each worker in the python pool is hacking on a task that is \"about the same level of effort\" as all the other workers at any given time. With these estimated length columns, we can start using these datasets faster than if we were using a single python process.", "### Overview\n\n\nTo find the alpaca training text data, please refer to the desc column.\n\n\nHere's a breakdown of some of the more useful columns:\n\n\n* file\\_path - identifier for all datasets source code module path\n* desc - alpaca question and answer yaml\n* desc\\_len - length of the alpaca question and answer yaml\n* recsize - estimated compute time/size of the row for downstream pools\n* name - name of the file\n* class\\_name - name of the class and global if function\n* class\\_bases - comma delimited base class name(s)\n* is\\_member - bool for is a class member or global function\n* class\\_docstr - class docstring\n* class\\_docstr\\_tok - tokenized class docstring\n* docstr - docsting for the method or function\n* docstr\\_tok - tokenized method or function docstring\n* code\\_tok - tokenized code\n* lstart - start line number\n* lend - end line number\n* code - ''\" **LOKCDR** \"'' delimited code ordered by class method or global function\n* args - ''\" **LOKCDR** \"'' delimited arguments ordered by class method or global function\n* returns - ''\" **LOKCDR** \"'' delimited returns ordered by class method or global function\n* raises - ''\" **LOKCDR** \"'' delimited exception raises ordered by class method or global function\n* method\\_names - ''\" **LOKCDR** \"'' delimited code ordered by class method name\n* function\\_names - ''\" **LOKCDR** \"'' delimited code ordered by global function name\n* imports - ordered imports in the module\n* filename - name of the file without the directory pathing\n* total\\_objects - total objects detected in the file\\_path\n* num\\_classes - number of classes detected in the file\\_path\n* num\\_methods - number of class methods detected in the file\\_path\n* num\\_bases - number of base classes for the class\\_name definition\n* num\\_all\\_bases - number of all base classes detected in the file\\_path\n* num\\_functions - number of global functions detected in the file\\_path\n* num\\_imports - number of imports detected in the file\\_path\n* label\\_id - shortened tracking label for image knowledge graphs and mp3s", "### Reference Schema - All Data Types\n\n\nNot all columns are casted to the correct types, here is the reference schema when joining all datasets together:", "#### Deserializing a Class or Function in a Row\n\n\nNote: there is a custom delimiter: ''\" **LOKCDR** \"'' for preserving class method and global function ordering within the same sample row. For example, when viewing the class by method names you can use:\n\n\nThe returned lists in the example above are ordered by class member method name with associated code, method arguments, raised exceptions and return statements.\n\n\nLegacy Datasets\n---------------", "### Legacy Coding Instruction datasets\n\n\nThis dataset was created around 2024-01-10 from 1159 python source code repositories (~142 GB on disk). While the datasets are still available they are no longer supported due to an issue with duplication in the class rows. Note: the rows for global functions, base class inheritance/polymorphism, and module imports were not impacted by this issue.\n\n\nHere's how to extract the sub datasets within any of the coding instruction parquet files:\n\n\nDataset Name: Instruct v2 - Building an AI copilot to leverage AI research, Rows: 2.32m, Size (GB): 27.6\nDataset Name: Instruct v1 - Prototype, Rows: 1.74m, Size (GB): 28.4", "### Legacy Image datasets\n\n\nDataset Name: How to use class methods, Rows: 312k, Size (GB): 304", "### Legacy Audio datasets", "### What was the process for collecting the source code datasets?\n\n\nThe coding datasets are built for extracting the latest updates from many source code repositories. It takes about seven hours to regenerate the large code dataset. Most of the image datasets were generated from the large index dataset, and the audio datasets were mostly generated from the small coding dataset.", "### Source Code Background\n\n\nWhat source code repositories are in here?\n\n\n* Python repositories: 1207\n* Source repos size on disk: 144.5 GB\n* Rows: 2350782\n* Python classes: 176237", "### Did AI or ML create any of this data?\n\n\nNo. The focus on this dataset was to build and share a large, clean training dataset without using AI or ML models. These datasets were collected without pre-trained AI/ML models performing: summarization, classification, segmentation, vocalization, image rendering, coding, or testing.\n\n\nLicense\n-------\n\n\nThese are early-days educational datasets. We do not claim ownership or validity of the code in the datasets in here. The instruction: text instruction, image and audio datasets are creative derivative works, but we are not lawyers so use them at your own discretion. By using these datasets, you acknowledge these risks and take ownership after the files are downloaded.\n\n\nThanks for reading, listening and your time\n-------------------------------------------\n\n\n<img src=\"URL alt=\"Thanks for reading, listening and your time\" width=\"500\" style=\"display: block; margin: auto;\"/>" ]

[ "TAGS\n#task_categories-text-generation #task_categories-text-to-audio #task_categories-text-to-speech #task_categories-text-to-image #task_categories-audio-to-audio #task_categories-image-to-image #task_categories-question-answering #task_ids-parsing #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #license-other #python-copilot #python-coding #python-architecture #knowledge-graphs #multimodal #text-image-audio #fine-tuning #training #question-answering #image-knowledge-graph #alpaca #mp3 #png #text #instruct #class #classes #function #functions #inheritance #region-us \n", "### Image Training Examples\n\n\nThese graphs are focused on a high-level overview of how to use python:\n\n\n* classes\n* base classes\n* global functions\n* imports\n\n\nEach graph includes labeled objects, directionality, standardized colors, and a descriptive text box for all drawn objects.\n\n\nBelow are some extracted image samples:", "#### Class - Knowledge Graph Images\n\n\nHere are samples from the python copilot class image knowledge graph dataset (294.1 GB). These images attempt to teach how to use software with a networkx graph saved as a png with an alpaca text box:", "##### How to use the transformers/src/transformers/models/clip/configuration\\_clip.py CLIPConfig class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/configuration\\_clip.py CLIPConfig class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/configuration\\_clip.py CLIPOnnxConfig class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/configuration\\_clip.py CLIPOnnxConfig class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/tokenization\\_clip.py CLIPTokenizer class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/tokenization\\_clip.py CLIPTokenizer class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "#### Base Class - Inheritance and Polymorphism Knowledge Graph Images\n\n\nHere are samples from the python copilot base class inheritance and polymorphism image knowledge graph dataset (135 GB). These images attempt to teach how to use software with a networkx graph saved as a png with an alpaca text box:", "##### How to use the transformers/src/transformers/models/clip/configuration\\_clip.py CLIPConfig inherited base class(es)\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/configuration\\_clip.py CLIPConfig inherited base class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/tokenization\\_clip\\_fast.py CLIPTokenizerFast inherited base class(es)\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/tokenization\\_clip\\_fast.py CLIPTokenizerFast inherited base class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "#### Global Functions - Knowledge Graph Images\n\n\nHere are samples from the python copilot global functions image knowledge graph dataset (130 GB). These images attempt to teach how to use software with a networkx graph saved as a png with an alpaca text box:", "##### How to use the transformers/src/transformers/models/clip/convert\\_clip\\_original\\_pytorch\\_to\\_hf.py global functions\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/convert\\_clip\\_original\\_pytorch\\_to\\_hf.py global functions\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/tokenization\\_clip.py global functions\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/tokenization\\_clip.py global functions\" width=\"500\" style=\"display: block; margin: auto;\"/>", "#### Imports - Knowledge Graph Images\n\n\nHere are samples from the python copilot imports image knowledge graph dataset (211 GB). These images attempt to teach how to use software with a networkx graph saved as a png with an alpaca text box:", "##### How to use the transformers/src/transformers/models/clip/configuration\\_clip.py imports like the CLIPConfig class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/configuration\\_clip.py imports like the CLIPConfig class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/configuration\\_clip.py imports like the CLIPTextConfig class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/configuration\\_clip.py imports like the CLIPTextConfig class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/configuration\\_clip.py imports like the CLIPVisionConfig class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/configuration\\_clip.py imports like the CLIPVisionConfig class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "##### How to use the transformers/src/transformers/models/clip/tokenization\\_clip\\_fast.py imports like the CLIPTokenizerFast class\n\n\n<img src=\"URL alt=\"How to use the transformers/src/transformers/models/clip/tokenization\\_clip\\_fast.py imports like the CLIPTokenizerFast class\" width=\"500\" style=\"display: block; margin: auto;\"/>", "### Audio Training Examples - Question and Answering in Alpaca\n\n\nBelow are extracted question and answer mp3 samples. Each mp3 is either a recording of the alpaca question or answer. Question mp3s use a different speaker than the answer mp3 voice.\n\n\nNote: mobile browsers have issues playing the mp3s and show a question mark due to markdown failing to show the Listen link vs a confusing ? mark icon sorry!\n\n\n\nSchema High Level Design\n------------------------", "### Summary\n\n\nWe tried to build the schema to help others maximize available hardware (cpu/storage).", "### Background\n\n\nWe use a lot of python multiprocessing pools to concurrently search many parquet files in each of our datasets at once. To help others do the same, we included the recsize and desc\\_len columns that provide an estimate for \"how long\" each row will take to: draw as an image or record as an mp3. With these columns, we are able to maximize our hardware because each worker in the python pool is hacking on a task that is \"about the same level of effort\" as all the other workers at any given time. With these estimated length columns, we can start using these datasets faster than if we were using a single python process.", "### Overview\n\n\nTo find the alpaca training text data, please refer to the desc column.\n\n\nHere's a breakdown of some of the more useful columns:\n\n\n* file\\_path - identifier for all datasets source code module path\n* desc - alpaca question and answer yaml\n* desc\\_len - length of the alpaca question and answer yaml\n* recsize - estimated compute time/size of the row for downstream pools\n* name - name of the file\n* class\\_name - name of the class and global if function\n* class\\_bases - comma delimited base class name(s)\n* is\\_member - bool for is a class member or global function\n* class\\_docstr - class docstring\n* class\\_docstr\\_tok - tokenized class docstring\n* docstr - docsting for the method or function\n* docstr\\_tok - tokenized method or function docstring\n* code\\_tok - tokenized code\n* lstart - start line number\n* lend - end line number\n* code - ''\" **LOKCDR** \"'' delimited code ordered by class method or global function\n* args - ''\" **LOKCDR** \"'' delimited arguments ordered by class method or global function\n* returns - ''\" **LOKCDR** \"'' delimited returns ordered by class method or global function\n* raises - ''\" **LOKCDR** \"'' delimited exception raises ordered by class method or global function\n* method\\_names - ''\" **LOKCDR** \"'' delimited code ordered by class method name\n* function\\_names - ''\" **LOKCDR** \"'' delimited code ordered by global function name\n* imports - ordered imports in the module\n* filename - name of the file without the directory pathing\n* total\\_objects - total objects detected in the file\\_path\n* num\\_classes - number of classes detected in the file\\_path\n* num\\_methods - number of class methods detected in the file\\_path\n* num\\_bases - number of base classes for the class\\_name definition\n* num\\_all\\_bases - number of all base classes detected in the file\\_path\n* num\\_functions - number of global functions detected in the file\\_path\n* num\\_imports - number of imports detected in the file\\_path\n* label\\_id - shortened tracking label for image knowledge graphs and mp3s", "### Reference Schema - All Data Types\n\n\nNot all columns are casted to the correct types, here is the reference schema when joining all datasets together:", "#### Deserializing a Class or Function in a Row\n\n\nNote: there is a custom delimiter: ''\" **LOKCDR** \"'' for preserving class method and global function ordering within the same sample row. For example, when viewing the class by method names you can use:\n\n\nThe returned lists in the example above are ordered by class member method name with associated code, method arguments, raised exceptions and return statements.\n\n\nLegacy Datasets\n---------------", "### Legacy Coding Instruction datasets\n\n\nThis dataset was created around 2024-01-10 from 1159 python source code repositories (~142 GB on disk). While the datasets are still available they are no longer supported due to an issue with duplication in the class rows. Note: the rows for global functions, base class inheritance/polymorphism, and module imports were not impacted by this issue.\n\n\nHere's how to extract the sub datasets within any of the coding instruction parquet files:\n\n\nDataset Name: Instruct v2 - Building an AI copilot to leverage AI research, Rows: 2.32m, Size (GB): 27.6\nDataset Name: Instruct v1 - Prototype, Rows: 1.74m, Size (GB): 28.4", "### Legacy Image datasets\n\n\nDataset Name: How to use class methods, Rows: 312k, Size (GB): 304", "### Legacy Audio datasets", "### What was the process for collecting the source code datasets?\n\n\nThe coding datasets are built for extracting the latest updates from many source code repositories. It takes about seven hours to regenerate the large code dataset. Most of the image datasets were generated from the large index dataset, and the audio datasets were mostly generated from the small coding dataset.", "### Source Code Background\n\n\nWhat source code repositories are in here?\n\n\n* Python repositories: 1207\n* Source repos size on disk: 144.5 GB\n* Rows: 2350782\n* Python classes: 176237", "### Did AI or ML create any of this data?\n\n\nNo. The focus on this dataset was to build and share a large, clean training dataset without using AI or ML models. These datasets were collected without pre-trained AI/ML models performing: summarization, classification, segmentation, vocalization, image rendering, coding, or testing.\n\n\nLicense\n-------\n\n\nThese are early-days educational datasets. We do not claim ownership or validity of the code in the datasets in here. The instruction: text instruction, image and audio datasets are creative derivative works, but we are not lawyers so use them at your own discretion. By using these datasets, you acknowledge these risks and take ownership after the files are downloaded.\n\n\nThanks for reading, listening and your time\n-------------------------------------------\n\n\n<img src=\"URL alt=\"Thanks for reading, listening and your time\" width=\"500\" style=\"display: block; margin: auto;\"/>" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of YouKnowMee/Mistral-7b-instruct-v0.2-summ-dpo-ed3\n\n\n\nDataset automatically created during the evaluation run of model YouKnowMee/Mistral-7b-instruct-v0.2-summ-dpo-ed3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T17:27:53.153945(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 YouKnowMee/Mistral-7b-instruct-v0.2-summ-dpo-ed3\n\n\n\nDataset automatically created during the evaluation run of model YouKnowMee/Mistral-7b-instruct-v0.2-summ-dpo-ed3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T17:27:53.153945(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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", "size_categories:n<1K", "language:en", "license:gpl-3.0", "art", "region:us" ]

[]

[ "en" ]

[ "# About \nThis dataset consist 560 Sprite animations in form of single image paired with meaningful description, with consistent gray background.", "# Credits\nSpecial thanks to the skilled sprite animation creators, contributing to the training dataset for this project.\n\n- Train images 0.png - 6.png thanks to URL \n- Train images 7.png - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL" ]

[ "TAGS\n#task_categories-text-classification #size_categories-n<1K #language-English #license-gpl-3.0 #art #region-us \n", "# About \nThis dataset consist 560 Sprite animations in form of single image paired with meaningful description, with consistent gray background.", "# Credits\nSpecial thanks to the skilled sprite animation creators, contributing to the training dataset for this project.\n\n- Train images 0.png - 6.png thanks to URL \n- Train images 7.png - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL \n- Train images URL - URL thanks to URL" ]

[ "language:en", "stable diffusion", "region:us" ]

[]

[ "en" ]

[ "## Examples\n\nexp_sd_v2\n!woman in red dress\n\n!Redhead sitting on a chair\n\nexp_sd_v4\n\n!Redhead sitting on a chair" ]

[ "TAGS\n#language-English #stable diffusion #region-us \n", "## Examples\n\nexp_sd_v2\n!woman in red dress\n\n!Redhead sitting on a chair\n\nexp_sd_v4\n\n!Redhead sitting on a chair" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of argilla/DistilabelBeagle14-7B\n\n\n\nDataset automatically created during the evaluation run of model argilla/DistilabelBeagle14-7B 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 2024-01-23T17:49:14.441859(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 argilla/DistilabelBeagle14-7B\n\n\n\nDataset automatically created during the evaluation run of model argilla/DistilabelBeagle14-7B 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 2024-01-23T17:49:14.441859(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:translation", "language:pl", "language:en", "license:odbl", "region:us" ]

[]

[ "pl", "en" ]

[]

[ "TAGS\n#task_categories-translation #language-Polish #language-English #license-odbl #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"uf_unsafe_v3\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"uf_unsafe_v3\"\n\nMore Information needed" ]

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

[]

[]

[ "# Dataset Card for Cellular Automata", "## Dataset Details\n\nThis dataset contains 1000 labeled images of cellular automata.", "### Dataset Description\n\nCellular Automaton were described by\nStephen Wolphram in A New King of Science.\nImagine you have a grid, like a checkerboard. Each square in the grid has a state - on or off, with the state of the square determining its color. There are rulesets (256 of them) that describe how the squares change their state depending on what's happening around them.\n\nThe python library CellPyLib was used to generate the labeled images.\n\n- Curated by: Kathy McGuiness", "## Uses\n\nOne toy use-case is fine-tuning the aMUSEd model.", "## Dataset Creation\n\nThe dataset was created as a demo on how on to create a labeled image dataset." ]

[ "TAGS\n#license-mit #region-us \n", "# Dataset Card for Cellular Automata", "## Dataset Details\n\nThis dataset contains 1000 labeled images of cellular automata.", "### Dataset Description\n\nCellular Automaton were described by\nStephen Wolphram in A New King of Science.\nImagine you have a grid, like a checkerboard. Each square in the grid has a state - on or off, with the state of the square determining its color. There are rulesets (256 of them) that describe how the squares change their state depending on what's happening around them.\n\nThe python library CellPyLib was used to generate the labeled images.\n\n- Curated by: Kathy McGuiness", "## Uses\n\nOne toy use-case is fine-tuning the aMUSEd model.", "## Dataset Creation\n\nThe dataset was created as a demo on how on to create a labeled image dataset." ]

[ "language:ca", "license:cc0-1.0", "region:us" ]

[]

[ "ca" ]

[ "# Common Voice Corpus 16.1 Català (up_votes>5)\n\nDataset extret de mozilla-foundation/common_voice_16_1 només els splits train i test del Català i amb up_votes > 5" ]

[ "TAGS\n#language-Catalan #license-cc0-1.0 #region-us \n", "# Common Voice Corpus 16.1 Català (up_votes>5)\n\nDataset extret de mozilla-foundation/common_voice_16_1 només els splits train i test del Català i amb up_votes > 5" ]

[ "size_categories:1M<n<10M", "license:mit", "chemistry", "arxiv:1811.12823", "region:us" ]

[ "1811.12823" ]

[]

[ "# Molecular Sets (MOSES): A benchmarking platform for molecular generation models\n\nDeep generative models are rapidly becoming popular for the discovery of new molecules and materials. Such models learn on a large collection of molecular structures and produce novel compounds. In this work, we introduce Molecular Sets (MOSES), a benchmarking platform to support research on machine learning for drug discovery. MOSES implements several popular molecular generation models and provides a set of metrics to evaluate the quality and diversity of generated molecules. With MOSES, we aim to standardize the research on molecular generation and facilitate the sharing and comparison of new models.\n\n__For more details, please refer to the paper.__\n\nIf you are using MOSES in your research paper, please cite us as", "## Dataset\n\nWe propose a benchmarking dataset refined from the ZINC database.\n\nThe set is based on the ZINC Clean Leads collection. It contains 4,591,276 molecules in total, filtered by molecular weight in the range from 250 to 350 Daltons, a number of rotatable bonds not greater than 7, and XlogP less than or equal to 3.5. We removed molecules containing charged atoms or atoms besides C, N, S, O, F, Cl, Br, H or cycles longer than 8 atoms. The molecules were filtered via medicinal chemistry filters (MCFs) and PAINS filters.\n\nThe dataset contains 1,936,962 molecular structures. For experiments, we split the dataset into a training, test and scaffold test sets containing around 1.6M, 176k, and 176k molecules respectively. The scaffold test set contains unique Bemis-Murcko scaffolds that were not present in the training and test sets. We use this set to assess how well the model can generate previously unobserved scaffolds." ]

[ "TAGS\n#size_categories-1M<n<10M #license-mit #chemistry #arxiv-1811.12823 #region-us \n", "# Molecular Sets (MOSES): A benchmarking platform for molecular generation models\n\nDeep generative models are rapidly becoming popular for the discovery of new molecules and materials. Such models learn on a large collection of molecular structures and produce novel compounds. In this work, we introduce Molecular Sets (MOSES), a benchmarking platform to support research on machine learning for drug discovery. MOSES implements several popular molecular generation models and provides a set of metrics to evaluate the quality and diversity of generated molecules. With MOSES, we aim to standardize the research on molecular generation and facilitate the sharing and comparison of new models.\n\n__For more details, please refer to the paper.__\n\nIf you are using MOSES in your research paper, please cite us as", "## Dataset\n\nWe propose a benchmarking dataset refined from the ZINC database.\n\nThe set is based on the ZINC Clean Leads collection. It contains 4,591,276 molecules in total, filtered by molecular weight in the range from 250 to 350 Daltons, a number of rotatable bonds not greater than 7, and XlogP less than or equal to 3.5. We removed molecules containing charged atoms or atoms besides C, N, S, O, F, Cl, Br, H or cycles longer than 8 atoms. The molecules were filtered via medicinal chemistry filters (MCFs) and PAINS filters.\n\nThe dataset contains 1,936,962 molecular structures. For experiments, we split the dataset into a training, test and scaffold test sets containing around 1.6M, 176k, and 176k molecules respectively. The scaffold test set contains unique Bemis-Murcko scaffolds that were not present in the training and test sets. We use this set to assess how well the model can generate previously unobserved scaffolds." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"downstream-7-flat\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"downstream-7-flat\"\n\nMore Information needed" ]

[ "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "license:apache-2.0", "json", "data-extraction", "structured-generation", "restricted-generation", "ecommerce", "medical", "manufacturing", "server logs", "news", "region:us" ]

[]

[ "en" ]

[ "# Diverse Restricted JSON Data Extraction\n\n- Curated by: The paraloq analytics team.", "## Uses\n\n1. Benchmark restricted JSON data extraction (text + JSON schema -> JSON instance)\n2. Fine-Tune data extraction model (text + JSON schema -> JSON instance)\n3. Fine-Tune JSON schema Retrieval model (text -> retriever -> most adequate JSON schema)", "### Out-of-Scope Use\n\nIntended for research purposes only.", "## Dataset Structure\n\nThe data comes with the following fields:\n- title: The title of the schema.\n- topic: The general topic of the item. For a list of topis, see below.\n- schema: The JSON schema specifying the structure of the data.\n- item: A JSON instance of the schema holding actual data.\n- medium: The medium of the example data. Examples inlcude \"news article\", \"blog post\", \"email\", \"html web page\", \"conversation\", etc.\n- text: An instance of the given medium, containing all the information held by the item, along with additional information.\n\nA focus of this dataset is to provide a diverse set of items from a wide array of topics. We currently include the following topic areas:\n\n- simple: Simple, general, documents such as to-do lists, calendars, recipes, etc. This is the most generic topic and is designed to be easy to exract.\n- medical: Medical documents such as patient records, prescriptions, test results, etc.\n- ecommerce: Ecommerce documents such as product listings, shopping carts, order confirmations, etc.\n- business: Business documents such as invoices, purchase orders, contracts, etc.\n- travel: Travel documents such as flight bookings, hotel reservations, itineraries, etc.\n- media: Media documents such as movie reviews, music albums, video games, etc.\n- technology: Technology documents such as software licenses, API responses, error logs, etc.\n- manufacturing: Manufacturing documents such as product BOMs, work orders, inspection reports, COAs etc.", "## Dataset Creation", "### Curation Rationale\n\nWe use this dataset to benchmark different models for their ability to extract data from unstructured text in a zero shot fashion, by including the desired JSON schema in the prompt.\nThe dataset can also be used to fine-tune a model to extract data in a zero-shot manner, feeding text and a target JSON schema. Note that the difficulty here is typically not that the model output is not adhering to the desired JSON schema. This can be enforced by restricing generation using guidance or outlines. For us, the issue is often that a model would not extract all of the available data.", "### Source Data\n\nThis data is synthetically generated using Google's Gemini-Pro.", "#### Data Collection and Processing\n\n1. Prompt the model to generate a list of JSON schemas representing a diverse set of items.\n2. Prompt the model to create instances from each of the schemas.\n3. Prompt the model to generate text (in the form of a blog post, server logs, emails, chats, etc.) that contains the information held by the instance.", "#### Who are the source data producers?\n\nparaloq analytics is an Austrian AI research and development company based in Vienna.", "## Bias, Risks, and Limitations\n\nThe data might include biases resulting from the sampling and bias propagation from Google's Gemini-Pro.", "## Dataset Card Authors\n\nMax Arrich" ]

[ "TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-English #license-apache-2.0 #json #data-extraction #structured-generation #restricted-generation #ecommerce #medical #manufacturing #server logs #news #region-us \n", "# Diverse Restricted JSON Data Extraction\n\n- Curated by: The paraloq analytics team.", "## Uses\n\n1. Benchmark restricted JSON data extraction (text + JSON schema -> JSON instance)\n2. Fine-Tune data extraction model (text + JSON schema -> JSON instance)\n3. Fine-Tune JSON schema Retrieval model (text -> retriever -> most adequate JSON schema)", "### Out-of-Scope Use\n\nIntended for research purposes only.", "## Dataset Structure\n\nThe data comes with the following fields:\n- title: The title of the schema.\n- topic: The general topic of the item. For a list of topis, see below.\n- schema: The JSON schema specifying the structure of the data.\n- item: A JSON instance of the schema holding actual data.\n- medium: The medium of the example data. Examples inlcude \"news article\", \"blog post\", \"email\", \"html web page\", \"conversation\", etc.\n- text: An instance of the given medium, containing all the information held by the item, along with additional information.\n\nA focus of this dataset is to provide a diverse set of items from a wide array of topics. We currently include the following topic areas:\n\n- simple: Simple, general, documents such as to-do lists, calendars, recipes, etc. This is the most generic topic and is designed to be easy to exract.\n- medical: Medical documents such as patient records, prescriptions, test results, etc.\n- ecommerce: Ecommerce documents such as product listings, shopping carts, order confirmations, etc.\n- business: Business documents such as invoices, purchase orders, contracts, etc.\n- travel: Travel documents such as flight bookings, hotel reservations, itineraries, etc.\n- media: Media documents such as movie reviews, music albums, video games, etc.\n- technology: Technology documents such as software licenses, API responses, error logs, etc.\n- manufacturing: Manufacturing documents such as product BOMs, work orders, inspection reports, COAs etc.", "## Dataset Creation", "### Curation Rationale\n\nWe use this dataset to benchmark different models for their ability to extract data from unstructured text in a zero shot fashion, by including the desired JSON schema in the prompt.\nThe dataset can also be used to fine-tune a model to extract data in a zero-shot manner, feeding text and a target JSON schema. Note that the difficulty here is typically not that the model output is not adhering to the desired JSON schema. This can be enforced by restricing generation using guidance or outlines. For us, the issue is often that a model would not extract all of the available data.", "### Source Data\n\nThis data is synthetically generated using Google's Gemini-Pro.", "#### Data Collection and Processing\n\n1. Prompt the model to generate a list of JSON schemas representing a diverse set of items.\n2. Prompt the model to create instances from each of the schemas.\n3. Prompt the model to generate text (in the form of a blog post, server logs, emails, chats, etc.) that contains the information held by the instance.", "#### Who are the source data producers?\n\nparaloq analytics is an Austrian AI research and development company based in Vienna.", "## Bias, Risks, and Limitations\n\nThe data might include biases resulting from the sampling and bias propagation from Google's Gemini-Pro.", "## Dataset Card Authors\n\nMax Arrich" ]

[ "region:us" ]

[]

[]

[ "# Responsible Media Content Matrix (RMCM): Overview\n\nThe RMCM is a strategic tool developed to address various forms of bias and unethical practices in media reporting. It encompasses several key categories, each focusing on a specific type of bias or ethical concern. The primary objective of the RMCM is to foster responsible journalism and content creation by providing clear guidelines on identifying and rectifying biased or harmful content.", "## Key Categories of RMCM:", "### Toxicity\nThis includes content that is aggressive, rude, disrespectful, or contributes to a hostile environment. It particularly focuses on hate speech and other forms of communication that incite hatred or violence based on race, religion, gender, or sexual orientation.", "### Stereotyping\nThis category deals with generalized or oversimplified beliefs about specific groups or communities. It aims to identify and correct content that perpetuates stereotypes, particularly those related to sexual themes, age, gender biases, or cultural misconceptions.", "### Bias\nThis refers to any content showing an unjustifiable preference or prejudice towards certain viewpoints, groups, or individuals. It includes both overt bias and more subtle forms of partiality that can skew the presentation of information.", "### Harm\nThis encompasses content that could cause distress or harm to individuals or society. It includes sensationalized or unethical reporting of criminal behavior, as well as content that inappropriately focuses on or glorifies violence and weaponry.\n\nThe RMCM serves as a guide for content creators, editors, and journalists, encouraging them to critically assess their work for potential biases and harmful elements. By adhering to the principles of the RMCM, media professionals can contribute to a more ethical, balanced, and responsible media landscape." ]

[ "TAGS\n#region-us \n", "# Responsible Media Content Matrix (RMCM): Overview\n\nThe RMCM is a strategic tool developed to address various forms of bias and unethical practices in media reporting. It encompasses several key categories, each focusing on a specific type of bias or ethical concern. The primary objective of the RMCM is to foster responsible journalism and content creation by providing clear guidelines on identifying and rectifying biased or harmful content.", "## Key Categories of RMCM:", "### Toxicity\nThis includes content that is aggressive, rude, disrespectful, or contributes to a hostile environment. It particularly focuses on hate speech and other forms of communication that incite hatred or violence based on race, religion, gender, or sexual orientation.", "### Stereotyping\nThis category deals with generalized or oversimplified beliefs about specific groups or communities. It aims to identify and correct content that perpetuates stereotypes, particularly those related to sexual themes, age, gender biases, or cultural misconceptions.", "### Bias\nThis refers to any content showing an unjustifiable preference or prejudice towards certain viewpoints, groups, or individuals. It includes both overt bias and more subtle forms of partiality that can skew the presentation of information.", "### Harm\nThis encompasses content that could cause distress or harm to individuals or society. It includes sensationalized or unethical reporting of criminal behavior, as well as content that inappropriately focuses on or glorifies violence and weaponry.\n\nThe RMCM serves as a guide for content creators, editors, and journalists, encouraging them to critically assess their work for potential biases and harmful elements. By adhering to the principles of the RMCM, media professionals can contribute to a more ethical, balanced, and responsible media landscape." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"semeval_subtask2_conversations\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"semeval_subtask2_conversations\"\n\nMore Information needed" ]

[ "Lilac", "region:us" ]

[]

[]

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

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

[ "Lilac", "region:us" ]

[]

[]

[ "# lilac/mosaic-instruct-v3\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:" ]

[ "TAGS\n#Lilac #region-us \n", "# lilac/mosaic-instruct-v3\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of FelixChao/WestSeverus-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/WestSeverus-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 2024-01-23T19:22:25.725845(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of FelixChao/WestSeverus-7B\n\n\n\nDataset automatically created during the evaluation run of model FelixChao/WestSeverus-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 2024-01-23T19:22:25.725845(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "Lilac", "region:us" ]

[]

[]

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

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

[ "Lilac", "region:us" ]

[]

[]

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

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

[ "size_categories:1K<n<10K", "datadreamer", "datadreamer-0.1.0", "synthetic", "gpt-4", "region:us" ]

[]

[]

[ "# Dataset Card\n\nThis a synthetic dataset of arXiv-style research paper abstracts and tweets summarizing them used as a demonstration of the DataDreamer library. It was used to train an \"Abstract to Tweet\" model.\n\n---\nThis dataset was produced with DataDreamer . The synthetic dataset card can be found here." ]

[ "TAGS\n#size_categories-1K<n<10K #datadreamer #datadreamer-0.1.0 #synthetic #gpt-4 #region-us \n", "# Dataset Card\n\nThis a synthetic dataset of arXiv-style research paper abstracts and tweets summarizing them used as a demonstration of the DataDreamer library. It was used to train an \"Abstract to Tweet\" model.\n\n---\nThis dataset was produced with DataDreamer . The synthetic dataset card can be found here." ]

[ "task_categories:automatic-speech-recognition", "language:ms", "license:mit", "region:us" ]

[]

[ "ms" ]

[ "# Pseudostreaming Malaysian Youtube videos using Whisper Large V3\n\nOriginal dataset at URL\n\nWe use URL to generate pseudostreaming dataset, source code at URL\n\nTotal 40486.589364839296 hours.\n\ndata format from URL,", "## how-to" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #language-Malay (macrolanguage) #license-mit #region-us \n", "# Pseudostreaming Malaysian Youtube videos using Whisper Large V3\n\nOriginal dataset at URL\n\nWe use URL to generate pseudostreaming dataset, source code at URL\n\nTotal 40486.589364839296 hours.\n\ndata format from URL,", "## how-to" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Weyaxi/Einstein-7B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Einstein-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 2024-01-23T20:03:38.754499(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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/Einstein-7B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Einstein-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 2024-01-23T20:03:38.754499(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of BarryFutureman/NeuralLake-Variant1-7B\n\n\n\nDataset automatically created during the evaluation run of model BarryFutureman/NeuralLake-Variant1-7B 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 2024-01-23T20:08:48.201286(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of BarryFutureman/NeuralLake-Variant1-7B\n\n\n\nDataset automatically created during the evaluation run of model BarryFutureman/NeuralLake-Variant1-7B 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 2024-01-23T20:08:48.201286(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[ "en" ]

[ "## Dataset Details", "### Dataset Description\n\n\nTP4 is a comprehensive dataset containing a curated collection of questions and answers from Stack Overflow. Focused on the realms of Python programming, NumPy, Pandas, TensorFlow, and PyTorch, TP4 includes essential attributes such as question ID, title, question body, answer body, associated tags, and score. This dataset is designed to facilitate research, analysis, and exploration of inquiries and solutions within the Python and machine learning communities on Stack Overflow.", "## Dataset Structure\n\n\n-Question ID: Unique identifiers for each question, facilitating easy referencing and linkage.\n-Title: Concise titles summarizing the essence of each question.\n-Question and Answer Bodies: Rich textual content providing detailed context and solutions.\n-Tags: Categorization labels such as 'python', 'numpy', 'pandas', 'tensorflow', and 'pytorch' for efficient filtering.\n-Score: Numerical representation of the community's evaluation of the question or answer.", "## Dataset Card Authors\n\nSYED HASAN ABBAS" ]

[ "TAGS\n#task_categories-question-answering #size_categories-1M<n<10M #language-English #code #region-us \n", "## Dataset Details", "### Dataset Description\n\n\nTP4 is a comprehensive dataset containing a curated collection of questions and answers from Stack Overflow. Focused on the realms of Python programming, NumPy, Pandas, TensorFlow, and PyTorch, TP4 includes essential attributes such as question ID, title, question body, answer body, associated tags, and score. This dataset is designed to facilitate research, analysis, and exploration of inquiries and solutions within the Python and machine learning communities on Stack Overflow.", "## Dataset Structure\n\n\n-Question ID: Unique identifiers for each question, facilitating easy referencing and linkage.\n-Title: Concise titles summarizing the essence of each question.\n-Question and Answer Bodies: Rich textual content providing detailed context and solutions.\n-Tags: Categorization labels such as 'python', 'numpy', 'pandas', 'tensorflow', and 'pytorch' for efficient filtering.\n-Score: Numerical representation of the community's evaluation of the question or answer.", "## Dataset Card Authors\n\nSYED HASAN ABBAS" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"self-reward-dev1706042310\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"self-reward-dev1706042310\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"UC-mix-llamav2-fromat\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"UC-mix-llamav2-fromat\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"self-reward-dev1706043619\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"self-reward-dev1706043619\"\n\nMore Information needed" ]

[ "task_categories:translation", "size_categories:1K<n<10K", "language:pt", "license:mit", "region:us" ]

[]

[ "pt" ]

[]

[ "TAGS\n#task_categories-translation #size_categories-1K<n<10K #language-Portuguese #license-mit #region-us \n" ]

[ "Lilac", "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of macadeliccc/piccolo-math-2x7b\n\n\n\nDataset automatically created during the evaluation run of model macadeliccc/piccolo-math-2x7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T21:39:07.430696(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 macadeliccc/piccolo-math-2x7b\n\n\n\nDataset automatically created during the evaluation run of model macadeliccc/piccolo-math-2x7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T21:39:07.430696(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "Lilac", "region:us" ]

[]

[]

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

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

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

[ "2304.06364" ]

[ "zh" ]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Aryanne/sheared-plus-westlake-50_75p\n\n\n\nDataset automatically created during the evaluation run of model Aryanne/sheared-plus-westlake-50_75p on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T22:04:31.166175(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Aryanne/sheared-plus-westlake-50_75p\n\n\n\nDataset automatically created during the evaluation run of model Aryanne/sheared-plus-westlake-50_75p on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T22:04:31.166175(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "Lilac", "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Aryanne/sheared-plus-westlake-normal\n\n\n\nDataset automatically created during the evaluation run of model Aryanne/sheared-plus-westlake-normal on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T22:07:40.446133(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Aryanne/sheared-plus-westlake-normal\n\n\n\nDataset automatically created during the evaluation run of model Aryanne/sheared-plus-westlake-normal on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T22:07:40.446133(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"autotrain-data-autotrain-h1dyg-dlim9\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"autotrain-data-autotrain-h1dyg-dlim9\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of AiMavenAi/MavenWest\n\n\n\nDataset automatically created during the evaluation run of model AiMavenAi/MavenWest on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T22:26:42.328277(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of AiMavenAi/MavenWest\n\n\n\nDataset automatically created during the evaluation run of model AiMavenAi/MavenWest on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T22:26:42.328277(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:image-segmentation", "size_categories:1K<n<10K", "language:en", "license:cc-by-nc-sa-4.0", "medical", "biology", "arxiv:2012.12453", "region:us" ]

[ "2012.12453" ]

[ "en" ]

[ "# Description:\n\npaper | kaggle\n\nThe CholecSeg8k dataset, an extension of the Cholec80 collection, includes 8,080 carefully annotated images from laparoscopic cholecystectomy surgeries, selected from 17 video clips in Cholec80. Each image in CholecSeg8K is pixel-level annotated for thirteen different surgical elements. The dataset is efficiently organized in a directory structure, featuring 101 folders, each containing 80 frames at a resolution of 854x480, along with three types of masks for each frame: a color mask for visualization, an annotation tool mask, and a watershed mask for simplified processing. This comprehensive dataset, freely available under the CC BY-NC-SA 4.0 license, is a critical resource for advancing the field of computer-assisted surgical procedures.", "# Loading the data:\n\nFirst install the 'datasets' library, then run the following code,", "# Simple demo:\n\nThis short demo shows how to load the data and directly visualize an image along with the corresponding masks.\n\n\n!example image\n\n(BibTex):", "# Data card contact:\nMin Woo Sun (minwoos@URL)" ]

[ "TAGS\n#task_categories-image-segmentation #size_categories-1K<n<10K #language-English #license-cc-by-nc-sa-4.0 #medical #biology #arxiv-2012.12453 #region-us \n", "# Description:\n\npaper | kaggle\n\nThe CholecSeg8k dataset, an extension of the Cholec80 collection, includes 8,080 carefully annotated images from laparoscopic cholecystectomy surgeries, selected from 17 video clips in Cholec80. Each image in CholecSeg8K is pixel-level annotated for thirteen different surgical elements. The dataset is efficiently organized in a directory structure, featuring 101 folders, each containing 80 frames at a resolution of 854x480, along with three types of masks for each frame: a color mask for visualization, an annotation tool mask, and a watershed mask for simplified processing. This comprehensive dataset, freely available under the CC BY-NC-SA 4.0 license, is a critical resource for advancing the field of computer-assisted surgical procedures.", "# Loading the data:\n\nFirst install the 'datasets' library, then run the following code,", "# Simple demo:\n\nThis short demo shows how to load the data and directly visualize an image along with the corresponding masks.\n\n\n!example image\n\n(BibTex):", "# Data card contact:\nMin Woo Sun (minwoos@URL)" ]

[ "Lilac", "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of snorkelai/Snorkel-Mistral-PairRM-DPO\n\n\n\nDataset automatically created during the evaluation run of model snorkelai/Snorkel-Mistral-PairRM-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T22:58:16.108311(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 snorkelai/Snorkel-Mistral-PairRM-DPO\n\n\n\nDataset automatically created during the evaluation run of model snorkelai/Snorkel-Mistral-PairRM-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T22:58:16.108311(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "language:en", "license:other", "reviews", "medical", "gp", "nhs", "region:us" ]

[]

[ "en" ]

[ "# England NHS GP Reviews (2022 - 2024)\n\n\n\nEngland NHS GP Reviews (2022 - 2024) Scrapped from URL", "## Dataset Details", "### Dataset Description\n\n\n\nEngland NHS GP Reviews (2022-2024) Scraped from URL\n\nThis dataset contains reviews of GP surgeries across England scraped from the NHS website. Each GP surgery is identified by an ODS code and surgery name. The scraped data includes the first 7 pages of reviews for each surgery, capturing the following attributes:\n\n- Surgery ODE Code\n- Surgery name\n- Scrape URL\n- Review Title\n- Star Rating (1-5)\n- Review Comment Text\n- Date of Review (Month and Year)\n\nIn total the dataset covers GP surgery reviews posted between 2022-2024. Over 61,000 individual reviews have been gathered providing insight into patient experiences with GP surgeries relating to aspects like appointments, staff, facilities and overall service.\n\nThe data is intended to enable further analysis into the quality of GP surgeries based on patient reviews submitted to the official NHS platforms. It can facilitate identification of review trends and top performing GP surgeries based on review metrics like average ratings and most frequent positive/negative review topics.\n\nThe reviews have been gathered through web scraping the NHS public websites, but are not officially endorsed NHS data products. The provided reviews should be considered assertions by individual anonymous patients regarding their experience with the listed GP surgery. Personal information has been removed during the scraping process to protect patient privacy.\n\n- Curated by: Jan du Plessis\n- Language(s) (NLP): English\n- License: UK open-government-license", "### Dataset Sources [optional]", "## Uses\n\n\nThe dataset is intended for use with BERTopic for topic analysis.", "### Direct Use\n\n\nNLP ML + Deep Learining Projects", "## Dataset Structure\n\n\n\n- Surgery ODE Code\n- Surgery name\n- Scrape URL\n- Review Title\n- Star Rating (1-5)\n- Review Comment Text\n- Date of Review (Month and Year)", "## Dataset Creation", "### Curation Rationale\n\n\nTraininig a model to classify medical reviews.", "### Source Data\n\n\nURL", "#### Data Collection and Processing\n\n\nWeb Scrapping using BeautifulSoup.\nDuplicates has been removed and NAN dropped.", "#### Personal and Sensitive Information\n\n\nReviews are anonymise with not patient identifyable information captured.", "## Dataset Card Authors [optional]\nJan du Plessis", "## Dataset Card Contact\ndrjanduplessis@URL" ]

[ "TAGS\n#language-English #license-other #reviews #medical #gp #nhs #region-us \n", "# England NHS GP Reviews (2022 - 2024)\n\n\n\nEngland NHS GP Reviews (2022 - 2024) Scrapped from URL", "## Dataset Details", "### Dataset Description\n\n\n\nEngland NHS GP Reviews (2022-2024) Scraped from URL\n\nThis dataset contains reviews of GP surgeries across England scraped from the NHS website. Each GP surgery is identified by an ODS code and surgery name. The scraped data includes the first 7 pages of reviews for each surgery, capturing the following attributes:\n\n- Surgery ODE Code\n- Surgery name\n- Scrape URL\n- Review Title\n- Star Rating (1-5)\n- Review Comment Text\n- Date of Review (Month and Year)\n\nIn total the dataset covers GP surgery reviews posted between 2022-2024. Over 61,000 individual reviews have been gathered providing insight into patient experiences with GP surgeries relating to aspects like appointments, staff, facilities and overall service.\n\nThe data is intended to enable further analysis into the quality of GP surgeries based on patient reviews submitted to the official NHS platforms. It can facilitate identification of review trends and top performing GP surgeries based on review metrics like average ratings and most frequent positive/negative review topics.\n\nThe reviews have been gathered through web scraping the NHS public websites, but are not officially endorsed NHS data products. The provided reviews should be considered assertions by individual anonymous patients regarding their experience with the listed GP surgery. Personal information has been removed during the scraping process to protect patient privacy.\n\n- Curated by: Jan du Plessis\n- Language(s) (NLP): English\n- License: UK open-government-license", "### Dataset Sources [optional]", "## Uses\n\n\nThe dataset is intended for use with BERTopic for topic analysis.", "### Direct Use\n\n\nNLP ML + Deep Learining Projects", "## Dataset Structure\n\n\n\n- Surgery ODE Code\n- Surgery name\n- Scrape URL\n- Review Title\n- Star Rating (1-5)\n- Review Comment Text\n- Date of Review (Month and Year)", "## Dataset Creation", "### Curation Rationale\n\n\nTraininig a model to classify medical reviews.", "### Source Data\n\n\nURL", "#### Data Collection and Processing\n\n\nWeb Scrapping using BeautifulSoup.\nDuplicates has been removed and NAN dropped.", "#### Personal and Sensitive Information\n\n\nReviews are anonymise with not patient identifyable information captured.", "## Dataset Card Authors [optional]\nJan du Plessis", "## Dataset Card Contact\ndrjanduplessis@URL" ]

[ "region:us" ]

[]

[]

[ "# vogue-runway-top15-512px-nobg-embeddings\n\n Vogue Runway\n- 15 fashion houses\n- 1679 collections\n- 87,547 images\n\nFashion Houses: Alexander McQueen, Armani, Balenciaga, Calvin Klein, Chanel, Dior, Fendi, Gucci, Hermes, Louis Vuitton, Prada, Ralph Lauren, Saint Laurent, Valentino, Versace.\n\nImages are maximum height 512 pixels.\n\nBackground is removed using mattmdjaga/segformer_b2_clothes.\n\nEmbeddings generated with tonyassi/vogue-fashion-collection-15-nobg." ]

[ "TAGS\n#region-us \n", "# vogue-runway-top15-512px-nobg-embeddings\n\n Vogue Runway\n- 15 fashion houses\n- 1679 collections\n- 87,547 images\n\nFashion Houses: Alexander McQueen, Armani, Balenciaga, Calvin Klein, Chanel, Dior, Fendi, Gucci, Hermes, Louis Vuitton, Prada, Ralph Lauren, Saint Laurent, Valentino, Versace.\n\nImages are maximum height 512 pixels.\n\nBackground is removed using mattmdjaga/segformer_b2_clothes.\n\nEmbeddings generated with tonyassi/vogue-fashion-collection-15-nobg." ]

[ "region:us" ]

[]

[]

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

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

[ "Lilac", "region:us" ]

[]

[]

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

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

[ "Lilac", "region:us" ]

[]

[]

[ "# lilac/GSM8K-main\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:" ]

[ "TAGS\n#Lilac #region-us \n", "# lilac/GSM8K-main\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.14\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.14 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T23:09:38.113022(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.14\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.14 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T23:09:38.113022(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "Lilac", "region:us" ]

[]

[]

[ "# lilac/GSM8K-socratic\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:" ]

[ "TAGS\n#Lilac #region-us \n", "# lilac/GSM8K-socratic\nThis dataset is a Lilac processed dataset. Original dataset: URL\n\nTo download the dataset to a local directory:\n\n\n\nor from python with:" ]

[ "Lilac", "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of BarryFutureman/WildMarcoroni-Variant1-7B\n\n\n\nDataset automatically created during the evaluation run of model BarryFutureman/WildMarcoroni-Variant1-7B 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 2024-01-23T23:37:24.789593(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of BarryFutureman/WildMarcoroni-Variant1-7B\n\n\n\nDataset automatically created during the evaluation run of model BarryFutureman/WildMarcoroni-Variant1-7B 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 2024-01-23T23:37:24.789593(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of BarryFutureman/WildMarcoroni-Variant3-7B\n\n\n\nDataset automatically created during the evaluation run of model BarryFutureman/WildMarcoroni-Variant3-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T23:43:24.021355(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of BarryFutureman/WildMarcoroni-Variant3-7B\n\n\n\nDataset automatically created during the evaluation run of model BarryFutureman/WildMarcoroni-Variant3-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-23T23:43:24.021355(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Weyaxi/Einstein-openchat-7B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Einstein-openchat-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 2024-01-23T23:44:02.231759(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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/Einstein-openchat-7B\n\n\n\nDataset automatically created during the evaluation run of model Weyaxi/Einstein-openchat-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 2024-01-23T23:44:02.231759(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "size_categories:1K<n<10K", "language:en", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "en" ]

[ "# Android GPU Performance Counter to Key Press Dataset", "## Description\n\nThis dataset comes from our mobile GPU-based eavesdropping work, Eavesdropping user credentials via GPU side channels on smartphones, presented at the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2022).\nIt contains 3,466 traces of mapping between the on-screen keyboard key presses and corresponding Snapdragon Adreno GPU performance counter changes collected on device in the meantime.", "## Data Structure\n\nThe dataset is arranged in the following format:\n\n* Folder name (e.g., '1622457056'): This UNIX timestamp when the experiment took place.\n * 'timestamp_data.csv': Raw recording of GPU performance counter changes during the experiment.\n * Column 1: UNIX timestamp of each performance counter (\"PC\") value change event, with granularity of 1 microseconds.\n * Column 2-13: GPU PC value changes of different types:\n * 'PERF_LRZ_VISIBLE_PRIM_AFTER_LRZ'\n * 'PERF_LRZ_FULL_8X8_TILES'\n * 'PERF_LRZ_PARTIAL_8X8_TILES'\n * 'PERF_LRZ_VISIBLE_PIXEL_AFTER_LRZ'\n * 'PERF_RAS_SUPERTILE_ACTIVE_CYCLES'\n * 'PERF_RAS_SUPER_TILES'\n * 'PERF_RAS_8X4_TILES'\n * 'PERF_RAS_FULLY_COVERED_8X4_TILES'\n * 'PERF_VPC_PC_PRIMITIVES'\n * 'PERF_VPC_SP_COMPONENTS'\n * 'PERF_VPC_LRZ_ASSIGN_PRIMITIVES'\n * 'PERF_VPC_SP_LM_COMPONENTS'\n * 'timestamp_keys.csv': Keyboard key presses occurred during the experiment.\n * Column 1: UNIX timestamp of each key press, with granularity of 1 microseconds.\n * Column 2: The specific key press occurred.\n\nFor the discussion of detailed meanings of different GPU PCs, please refer to Section 4 of our paper.\n \nIf you find this dataset useful, please consider citing the original published paper as shown below:", "## License\n\n[![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]\n\nThis work is licensed under a\n[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].\n\n[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]\n\n[cc-by-nc-sa]: URL\n[cc-by-nc-sa-image]: URL\n[cc-by-nc-sa-shield]: URL" ]

[ "TAGS\n#size_categories-1K<n<10K #language-English #license-cc-by-nc-sa-4.0 #region-us \n", "# Android GPU Performance Counter to Key Press Dataset", "## Description\n\nThis dataset comes from our mobile GPU-based eavesdropping work, Eavesdropping user credentials via GPU side channels on smartphones, presented at the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2022).\nIt contains 3,466 traces of mapping between the on-screen keyboard key presses and corresponding Snapdragon Adreno GPU performance counter changes collected on device in the meantime.", "## Data Structure\n\nThe dataset is arranged in the following format:\n\n* Folder name (e.g., '1622457056'): This UNIX timestamp when the experiment took place.\n * 'timestamp_data.csv': Raw recording of GPU performance counter changes during the experiment.\n * Column 1: UNIX timestamp of each performance counter (\"PC\") value change event, with granularity of 1 microseconds.\n * Column 2-13: GPU PC value changes of different types:\n * 'PERF_LRZ_VISIBLE_PRIM_AFTER_LRZ'\n * 'PERF_LRZ_FULL_8X8_TILES'\n * 'PERF_LRZ_PARTIAL_8X8_TILES'\n * 'PERF_LRZ_VISIBLE_PIXEL_AFTER_LRZ'\n * 'PERF_RAS_SUPERTILE_ACTIVE_CYCLES'\n * 'PERF_RAS_SUPER_TILES'\n * 'PERF_RAS_8X4_TILES'\n * 'PERF_RAS_FULLY_COVERED_8X4_TILES'\n * 'PERF_VPC_PC_PRIMITIVES'\n * 'PERF_VPC_SP_COMPONENTS'\n * 'PERF_VPC_LRZ_ASSIGN_PRIMITIVES'\n * 'PERF_VPC_SP_LM_COMPONENTS'\n * 'timestamp_keys.csv': Keyboard key presses occurred during the experiment.\n * Column 1: UNIX timestamp of each key press, with granularity of 1 microseconds.\n * Column 2: The specific key press occurred.\n\nFor the discussion of detailed meanings of different GPU PCs, please refer to Section 4 of our paper.\n \nIf you find this dataset useful, please consider citing the original published paper as shown below:", "## License\n\n[![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]\n\nThis work is licensed under a\n[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].\n\n[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]\n\n[cc-by-nc-sa]: URL\n[cc-by-nc-sa-image]: URL\n[cc-by-nc-sa-shield]: URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of flemmingmiguel/MBX-7B-v3\n\n\n\nDataset automatically created during the evaluation run of model flemmingmiguel/MBX-7B-v3 on the Open LLM Leaderboard.\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 2024-01-29T00:10:52.670556(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of flemmingmiguel/MBX-7B-v3\n\n\n\nDataset automatically created during the evaluation run of model flemmingmiguel/MBX-7B-v3 on the Open LLM Leaderboard.\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 2024-01-29T00:10:52.670556(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "Lilac", "region:us" ]

[]

[]

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

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

[ "task_categories:token-classification", "size_categories:1K<n<10K", "language:en", "license:odc-by", "finance", "region:us" ]

[]

[ "en" ]

[ "# FinEntity: A Dataset for entity-level sentiment classification.\nIn this work, we introduce an entity-level sentiment classification dataset, called FinEntity, that annotates sentiment (positive, neutral, and negative) of individual financial entities in financial news. The dataset construction process is well-documented in the paper. \n* Paper: FinEntity: Entity-level Sentiment Classification for Financial Texts\n* More Information: Github" ]

[ "TAGS\n#task_categories-token-classification #size_categories-1K<n<10K #language-English #license-odc-by #finance #region-us \n", "# FinEntity: A Dataset for entity-level sentiment classification.\nIn this work, we introduce an entity-level sentiment classification dataset, called FinEntity, that annotates sentiment (positive, neutral, and negative) of individual financial entities in financial news. The dataset construction process is well-documented in the paper. \n* Paper: FinEntity: Entity-level Sentiment Classification for Financial Texts\n* More Information: Github" ]

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Aryanne/ereb-test\n\n\n\nDataset automatically created during the evaluation run of model Aryanne/ereb-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 2024-01-24T01:34:47.525846(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Aryanne/ereb-test\n\n\n\nDataset automatically created during the evaluation run of model Aryanne/ereb-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 2024-01-24T01:34:47.525846(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Aryanne/sheared-silicon10p\n\n\n\nDataset automatically created during the evaluation run of model Aryanne/sheared-silicon10p on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-24T01:36:29.411153(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Aryanne/sheared-silicon10p\n\n\n\nDataset automatically created during the evaluation run of model Aryanne/sheared-silicon10p on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-24T01:36:29.411153(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:question-answering", "size_categories:10B<n<100B", "language:en", "license:cc-by-sa-4.0", "mathematical-reasoning", "reasoning", "finetuning", "pretraining", "llm", "arxiv:2402.07625", "region:us" ]

[ "2402.07625" ]

[ "en" ]

[ "# AutoMathText\n\nAutoMathText is an extensive and carefully curated dataset encompassing around 200 GB of mathematical texts. It's a compilation sourced from a diverse range of platforms including various websites, arXiv, and GitHub (OpenWebMath, RedPajama, Algebraic Stack). This rich repository has been autonomously selected (labeled) by the state-of-the-art open-source language model, Qwen-72B. Each piece of content in the dataset is assigned a score 'lm_q1q2_score' within the range of [0, 1], reflecting its relevance, quality and educational value in the context of mathematical intelligence.\n\nGitHub homepage: URL\n\nArXiv paper: URL", "## Objective\n\nThe primary aim of the AutoMathText dataset is to provide a comprehensive and reliable resource for a wide array of users - from academic researchers and educators to AI practitioners and mathematics enthusiasts. This dataset is particularly geared towards:\n\n- Facilitating advanced research in the intersection of mathematics and artificial intelligence.\n- Serving as an educational tool for learning and teaching complex mathematical concepts.\n- Providing a foundation for developing and training AI models specialized in processing and understanding mathematical content.", "## Configs\n\n\n\nHow to load data:", "## Features\n\n- Volume: Approximately 200 GB of text data (in natural language and programming language).\n- Content: A diverse collection of mathematical texts, including but not limited to research papers, educational articles, and code documentation.\n- Labeling: Every text is scored by Qwen-72B, a sophisticated language model, ensuring a high standard of relevance and accuracy.\n- Scope: Covers a wide spectrum of mathematical topics, making it suitable for various applications in advanced research and education.", "## References\n\n- OpenWebMath [[link]](URL\n- RedPajama [[link]](URL\n- Algebraick Stack [[link]](URL (a subset of Proof-Pile-2)\n\nWe appreciate your use of AutoMathText in your work. If you find this repository helpful, please consider citing it and star this repo. Feel free to contact zhangyif21@URL or open an issue if you have any questions (GitHub homepage: URL" ]

[ "TAGS\n#task_categories-text-generation #task_categories-question-answering #size_categories-10B<n<100B #language-English #license-cc-by-sa-4.0 #mathematical-reasoning #reasoning #finetuning #pretraining #llm #arxiv-2402.07625 #region-us \n", "# AutoMathText\n\nAutoMathText is an extensive and carefully curated dataset encompassing around 200 GB of mathematical texts. It's a compilation sourced from a diverse range of platforms including various websites, arXiv, and GitHub (OpenWebMath, RedPajama, Algebraic Stack). This rich repository has been autonomously selected (labeled) by the state-of-the-art open-source language model, Qwen-72B. Each piece of content in the dataset is assigned a score 'lm_q1q2_score' within the range of [0, 1], reflecting its relevance, quality and educational value in the context of mathematical intelligence.\n\nGitHub homepage: URL\n\nArXiv paper: URL", "## Objective\n\nThe primary aim of the AutoMathText dataset is to provide a comprehensive and reliable resource for a wide array of users - from academic researchers and educators to AI practitioners and mathematics enthusiasts. This dataset is particularly geared towards:\n\n- Facilitating advanced research in the intersection of mathematics and artificial intelligence.\n- Serving as an educational tool for learning and teaching complex mathematical concepts.\n- Providing a foundation for developing and training AI models specialized in processing and understanding mathematical content.", "## Configs\n\n\n\nHow to load data:", "## Features\n\n- Volume: Approximately 200 GB of text data (in natural language and programming language).\n- Content: A diverse collection of mathematical texts, including but not limited to research papers, educational articles, and code documentation.\n- Labeling: Every text is scored by Qwen-72B, a sophisticated language model, ensuring a high standard of relevance and accuracy.\n- Scope: Covers a wide spectrum of mathematical topics, making it suitable for various applications in advanced research and education.", "## References\n\n- OpenWebMath [[link]](URL\n- RedPajama [[link]](URL\n- Algebraick Stack [[link]](URL (a subset of Proof-Pile-2)\n\nWe appreciate your use of AutoMathText in your work. If you find this repository helpful, please consider citing it and star this repo. Feel free to contact zhangyif21@URL or open an issue if you have any questions (GitHub homepage: URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Tensoic/Kan-Llama-SFT-v0.5\n\n\n\nDataset automatically created during the evaluation run of model Tensoic/Kan-Llama-SFT-v0.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-24T01:43:44.197286(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Tensoic/Kan-Llama-SFT-v0.5\n\n\n\nDataset automatically created during the evaluation run of model Tensoic/Kan-Llama-SFT-v0.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-24T01:43:44.197286(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "size_categories:100K<n<1M", "language:en", "language:fr", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "en", "fr" ]

[ "# Refugee Law Lab: Luck of the Draw III: Data", "## Dataset Summary\n\nThe Refugee Law Lab supports bulk open-access to Canadian legal data to facilitate research and advocacy. \nBulk open-access helps avoid asymmetrical access-to-justice and amplification of marginalization that \nresults when commercial actors leverage proprietary \nlegal datasets for profit -- a particular concern in the border control setting.\n\nThis is the dataset used for a research project published in the Queen's Law Journal, undertaken at the Refugee Law Lab about outcomes in stays of removal in Canada's\nFederal Court. Specifically, it includes information from the online Federal Court dockets for all immigration law cases filed between\n1997 and 2022.\n\nThe dataset can be used for legal analytics (i.e. identifying patterns in legal \ndecision-making), to test ML and NLP tools on a bilingual dataset of Canadian legal materials, and to \npretrain language models for various tasks.", "## Dataset Structure", "### Data Instance\n\nThe datset includes a single data instance of all online Federal Court dockets involving immigration law filed between 1997 and 2022, \nas they appeared when the data was gathered in November 2022.", "### Data Fields\n\nData fields match the formart used for the Refugee Law Lab's Canadian Legal Data dataset.\n\n- citation (string): Legal citation for the document (neutral citation where available). In this dataset, the legal citaiton is the docket number, which is a identifer for the file assigned by the Federal Court. Docket numbers take the form IMM-#-YY. IMM signals that this is an immigration law docket, # is a sequential number starting at 1 that represents the order in which applications were received in a given year, and YY is the last two digits of the year in which the application was initially filed.\n\n- year (int32): Year of the document date, which can be useful for filtering. For this dataset, the year is the year when the application was initially filed.\n\n- name (string): Name of the document, in this dataset the style of cause of a cour file\n\n- date_filed (string): Date of the document (yyyy-mm-dd). In this dataset the year is the date the application was filed.\n\n- city_filed (string): City where the application was initially filed\n\n- nature (string): A category of proceedings assigned by the Federal Court\n\n- class (string): A second category of proceedings assigned by the Federal court\n\n- track (string): A third category of proceedings assigned by the Federal Court\n\n- documents (list of dictionaries): A list of dictionaries containing each docket entry (or row in the table of docket entries in a docket). Each dictionary contains the following key/value pairs:\n\n * RE_NO: The number assigned to the docket entry by the Federal Court\n \n * DOCNO: Where the entry involves the filing of a document, the number assigned to that document by the Federal Court\n \n * DOC_DT: The date of the docket entry\n\n * RECORDED_ENTRY: The content of the docket entry\n\n- source_url (string): URL where the document was scraped and where the official version can be found\n\n- scraped_timestamp (string): Date the document was scraped (yyyy-mm-dd)", "### Data Languages\n\nSome dockets are in English, some in French, and some alternate between English and French", "### Data Splits\n\nThe data has not been split, so all data is in the train split.", "### Data Loading\n\nTo load the data:\n\n\n\nTo convert to dataframe:", "## Dataset Creation", "### Curation Rationale\n\nThe dataset includes all Federal Court immigration law dockets available on the Federal Court's website at the time of research (November 2022). The Refugee Law Lab gathered this data for several projects, including the Refugee Law Lab Portal and the research article on Federal Court stays linked above.", "### Source Data", "#### Source\n\nAll data was gathered via the Federal Court's website.", "#### Initial Data Collection and Normalization\n\nDetails are available via links on the Refugee Law Lab's Github respository [Luck of the Draw III: Code & Data]\n(URL", "### Personal and Sensitive Information\n\nDocuments may include personal and sensitive information. All documents have been published online by the Federal Court. While the open court principle mandates \nthat court materials be made available to the public, there are privacy risks when these \nmaterials become easily and widely available. These privacy risks are particularly acute for marginalized groups, \nincluding refugees and other non-citizens whose personal and sensitive information is included in some of the\ndocuments in this dataset. For example, imagine a repressive government working with private data aggregators to \ncollect information that is used to target families of political opponents who have sought asylum abroad.\nOne mechanism used to try to achieve a balance between the open court principle \nand privacy is that in publishing the documents in this dataset, the relevant courts and tribunals prohibit \nsearch engines from indexing the documents. Users of this data are required to do the same.", "### Non-Official Versions\n\nDocuments included in this dataset are unofficial copies. For official versions published by \nthe Government of Canada, please see the source URLs.", "### Non-Affiliation / Endorsement\n\nThe reproduction of documents in this dataset was not done in affiliation with, or with the endorsement of \nthe Federal Court or the Government of Canada.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe Refugee Law Lab recognizes that this dataset -- and further research using the dataset -- raises challenging \nquestions about how to balance protecting privacy, enhancing government transparency, addressing information \nasymmetries, and building technologies that leverage data to advance the rights and interests of \nrefugees and other displaced people, as well as assisting those working with them (rather than technologies that \nenhance the power of states \nto control the movement of people across borders).\n\nMore broadly, the Refugee Law Lab also recognizes that considerations around privacy and data protection are complex \nand evolving. When working on migration, refugee law, data, technology and surveillance, we strive to foreground \nintersectional understandings of the systemic harms perpetuated against groups historically made marginalized. We \nencourage other users to do the same.\n\nWe also encourage users to try to avoid participating in building technologies that harm refugees and other \nmarginalized groups, as well as to connect with community organizations \nworking in this space, and to listen directly and learn from people who are affected by new technologies. \n\nWe will review the use these datasets periodically to examine whether continuing to publicly release these datasets achieves \nthe Refugee Law Lab's goals of advancing the rights and interests of refugees and other marginalized groups without creating \ndisproportionate risks and harms, including risks related to privacy and human rights.", "### Discussion of Biases\n\nThe dataset reflects many biases present in legal decision-making, including biases based on race, immigration status, gender, sexual orientation, religion, disability, socio-economic class, and other intersecting categories of discrimination.", "### Other Known Limitations\n\nDue to the ways that all\nlegal datasets may be skewed, users of this dataset are encouraged to collaborate with or consult domain experts.", "## Additional Information", "### Licensing Information\n\nAttribution-NonCommercial 4.0 International (CC BY-NC 4.0)\n\nNOTE: Users must also comply with upstream licensing for data obtained from the Federal Court, as \nwell as requests on source urls not to allow indexing of the documents by search engines to protect privacy. As a result, users must \nnot make the data available in formats or locations that can be indexed by search engines.", "### Warranties / Representations\n\nWe make no warranties or representations that the data included in this dataset is complete or accurate. Data \nwere obtained through academic research projects, including projects that use automated processes. \nWhile we try to make the data as accurate as possible, our methodologies may result in \ninaccurate or outdated data. As such, data should be viewed as preliminary information aimed to prompt \nfurther research and discussion, rather than as definitive information.", "### Dataset Curators\n\nSean Rehaag, Osgoode Hall Law School Professor & Director of the Refugee Law Lab\n\n\n\nSean Rehaag, \"Luck of the Draw III: Code & Data\" (2023) online: Github: <URL", "### Acknowledgements\n\nThis project draws on research supported by the Social Sciences and Humanities Research Council, the Law Foundation of Ontario, and the Digital Research Alliance of Canada. Jacob Danovich assisted with the infrastructure and scraping code for this project." ]

[ "TAGS\n#size_categories-100K<n<1M #language-English #language-French #license-cc-by-nc-4.0 #region-us \n", "# Refugee Law Lab: Luck of the Draw III: Data", "## Dataset Summary\n\nThe Refugee Law Lab supports bulk open-access to Canadian legal data to facilitate research and advocacy. \nBulk open-access helps avoid asymmetrical access-to-justice and amplification of marginalization that \nresults when commercial actors leverage proprietary \nlegal datasets for profit -- a particular concern in the border control setting.\n\nThis is the dataset used for a research project published in the Queen's Law Journal, undertaken at the Refugee Law Lab about outcomes in stays of removal in Canada's\nFederal Court. Specifically, it includes information from the online Federal Court dockets for all immigration law cases filed between\n1997 and 2022.\n\nThe dataset can be used for legal analytics (i.e. identifying patterns in legal \ndecision-making), to test ML and NLP tools on a bilingual dataset of Canadian legal materials, and to \npretrain language models for various tasks.", "## Dataset Structure", "### Data Instance\n\nThe datset includes a single data instance of all online Federal Court dockets involving immigration law filed between 1997 and 2022, \nas they appeared when the data was gathered in November 2022.", "### Data Fields\n\nData fields match the formart used for the Refugee Law Lab's Canadian Legal Data dataset.\n\n- citation (string): Legal citation for the document (neutral citation where available). In this dataset, the legal citaiton is the docket number, which is a identifer for the file assigned by the Federal Court. Docket numbers take the form IMM-#-YY. IMM signals that this is an immigration law docket, # is a sequential number starting at 1 that represents the order in which applications were received in a given year, and YY is the last two digits of the year in which the application was initially filed.\n\n- year (int32): Year of the document date, which can be useful for filtering. For this dataset, the year is the year when the application was initially filed.\n\n- name (string): Name of the document, in this dataset the style of cause of a cour file\n\n- date_filed (string): Date of the document (yyyy-mm-dd). In this dataset the year is the date the application was filed.\n\n- city_filed (string): City where the application was initially filed\n\n- nature (string): A category of proceedings assigned by the Federal Court\n\n- class (string): A second category of proceedings assigned by the Federal court\n\n- track (string): A third category of proceedings assigned by the Federal Court\n\n- documents (list of dictionaries): A list of dictionaries containing each docket entry (or row in the table of docket entries in a docket). Each dictionary contains the following key/value pairs:\n\n * RE_NO: The number assigned to the docket entry by the Federal Court\n \n * DOCNO: Where the entry involves the filing of a document, the number assigned to that document by the Federal Court\n \n * DOC_DT: The date of the docket entry\n\n * RECORDED_ENTRY: The content of the docket entry\n\n- source_url (string): URL where the document was scraped and where the official version can be found\n\n- scraped_timestamp (string): Date the document was scraped (yyyy-mm-dd)", "### Data Languages\n\nSome dockets are in English, some in French, and some alternate between English and French", "### Data Splits\n\nThe data has not been split, so all data is in the train split.", "### Data Loading\n\nTo load the data:\n\n\n\nTo convert to dataframe:", "## Dataset Creation", "### Curation Rationale\n\nThe dataset includes all Federal Court immigration law dockets available on the Federal Court's website at the time of research (November 2022). The Refugee Law Lab gathered this data for several projects, including the Refugee Law Lab Portal and the research article on Federal Court stays linked above.", "### Source Data", "#### Source\n\nAll data was gathered via the Federal Court's website.", "#### Initial Data Collection and Normalization\n\nDetails are available via links on the Refugee Law Lab's Github respository [Luck of the Draw III: Code & Data]\n(URL", "### Personal and Sensitive Information\n\nDocuments may include personal and sensitive information. All documents have been published online by the Federal Court. While the open court principle mandates \nthat court materials be made available to the public, there are privacy risks when these \nmaterials become easily and widely available. These privacy risks are particularly acute for marginalized groups, \nincluding refugees and other non-citizens whose personal and sensitive information is included in some of the\ndocuments in this dataset. For example, imagine a repressive government working with private data aggregators to \ncollect information that is used to target families of political opponents who have sought asylum abroad.\nOne mechanism used to try to achieve a balance between the open court principle \nand privacy is that in publishing the documents in this dataset, the relevant courts and tribunals prohibit \nsearch engines from indexing the documents. Users of this data are required to do the same.", "### Non-Official Versions\n\nDocuments included in this dataset are unofficial copies. For official versions published by \nthe Government of Canada, please see the source URLs.", "### Non-Affiliation / Endorsement\n\nThe reproduction of documents in this dataset was not done in affiliation with, or with the endorsement of \nthe Federal Court or the Government of Canada.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nThe Refugee Law Lab recognizes that this dataset -- and further research using the dataset -- raises challenging \nquestions about how to balance protecting privacy, enhancing government transparency, addressing information \nasymmetries, and building technologies that leverage data to advance the rights and interests of \nrefugees and other displaced people, as well as assisting those working with them (rather than technologies that \nenhance the power of states \nto control the movement of people across borders).\n\nMore broadly, the Refugee Law Lab also recognizes that considerations around privacy and data protection are complex \nand evolving. When working on migration, refugee law, data, technology and surveillance, we strive to foreground \nintersectional understandings of the systemic harms perpetuated against groups historically made marginalized. We \nencourage other users to do the same.\n\nWe also encourage users to try to avoid participating in building technologies that harm refugees and other \nmarginalized groups, as well as to connect with community organizations \nworking in this space, and to listen directly and learn from people who are affected by new technologies. \n\nWe will review the use these datasets periodically to examine whether continuing to publicly release these datasets achieves \nthe Refugee Law Lab's goals of advancing the rights and interests of refugees and other marginalized groups without creating \ndisproportionate risks and harms, including risks related to privacy and human rights.", "### Discussion of Biases\n\nThe dataset reflects many biases present in legal decision-making, including biases based on race, immigration status, gender, sexual orientation, religion, disability, socio-economic class, and other intersecting categories of discrimination.", "### Other Known Limitations\n\nDue to the ways that all\nlegal datasets may be skewed, users of this dataset are encouraged to collaborate with or consult domain experts.", "## Additional Information", "### Licensing Information\n\nAttribution-NonCommercial 4.0 International (CC BY-NC 4.0)\n\nNOTE: Users must also comply with upstream licensing for data obtained from the Federal Court, as \nwell as requests on source urls not to allow indexing of the documents by search engines to protect privacy. As a result, users must \nnot make the data available in formats or locations that can be indexed by search engines.", "### Warranties / Representations\n\nWe make no warranties or representations that the data included in this dataset is complete or accurate. Data \nwere obtained through academic research projects, including projects that use automated processes. \nWhile we try to make the data as accurate as possible, our methodologies may result in \ninaccurate or outdated data. As such, data should be viewed as preliminary information aimed to prompt \nfurther research and discussion, rather than as definitive information.", "### Dataset Curators\n\nSean Rehaag, Osgoode Hall Law School Professor & Director of the Refugee Law Lab\n\n\n\nSean Rehaag, \"Luck of the Draw III: Code & Data\" (2023) online: Github: <URL", "### Acknowledgements\n\nThis project draws on research supported by the Social Sciences and Humanities Research Council, the Law Foundation of Ontario, and the Digital Research Alliance of Canada. Jacob Danovich assisted with the infrastructure and scraping code for this project." ]

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

[]

[]

[ "# and unity, along with some additional calculus, algebra and stats samples in an attempt to improve MHENNs mathematical reasoning capabilities" ]

[ "TAGS\n#license-mit #region-us \n", "# and unity, along with some additional calculus, algebra and stats samples in an attempt to improve MHENNs mathematical reasoning capabilities" ]

[ "license:apache-2.0", "orca-pairs", "mncai/orca_dpo_pairs_ko", "Ja-ck/Orca-DPO-Pairs-KO", "We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs", "region:us" ]

[]

[]

[ "# SJ-Donald/orca-dpo-pairs-ko\n\nSJ-Donald/orca-dpo-pairs-ko is merged dataset from fllow datasets", "## Datasets\n\n* mncai/orca_dpo_pairs_ko\n* Ja-ck/Orca-DPO-Pairs-KO\n* We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs\n\nMerge datasets from above and drop duplicates.", "## How to use" ]

[ "TAGS\n#license-apache-2.0 #orca-pairs #mncai/orca_dpo_pairs_ko #Ja-ck/Orca-DPO-Pairs-KO #We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs #region-us \n", "# SJ-Donald/orca-dpo-pairs-ko\n\nSJ-Donald/orca-dpo-pairs-ko is merged dataset from fllow datasets", "## Datasets\n\n* mncai/orca_dpo_pairs_ko\n* Ja-ck/Orca-DPO-Pairs-KO\n* We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs\n\nMerge datasets from above and drop duplicates.", "## How to use" ]

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

[]

[ "ms" ]

[ "# Wikipedia-Malaysian-Politicians multiturn\n\nOriginal dataset at URL we just translate and prepare multi-turn chat template." ]

[ "TAGS\n#language-Malay (macrolanguage) #region-us \n", "# Wikipedia-Malaysian-Politicians multiturn\n\nOriginal dataset at URL we just translate and prepare multi-turn chat template." ]

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

[]

[ "ms" ]

[ "# wikipedia-malaysian-road-sign-images multiturn\n\nOriginal dataset at URL we just prepare multi-turn chat template." ]

[ "TAGS\n#language-Malay (macrolanguage) #region-us \n", "# wikipedia-malaysian-road-sign-images multiturn\n\nOriginal dataset at URL we just prepare multi-turn chat template." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of jsfs11/WildMBXMarconi-SLERP-7B\n\n\n\nDataset automatically created during the evaluation run of model jsfs11/WildMBXMarconi-SLERP-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 2024-01-24T03:57:13.465418(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of jsfs11/WildMBXMarconi-SLERP-7B\n\n\n\nDataset automatically created during the evaluation run of model jsfs11/WildMBXMarconi-SLERP-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 2024-01-24T03:57:13.465418(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 PetroGPT/WestSeverus-7B-DPO\n\n\n\nDataset automatically created during the evaluation run of model PetroGPT/WestSeverus-7B-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-24T04:03:24.441746(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 PetroGPT/WestSeverus-7B-DPO\n\n\n\nDataset automatically created during the evaluation run of model PetroGPT/WestSeverus-7B-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-24T04:03:24.441746(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 cris177/Orca-Hermes-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model cris177/Orca-Hermes-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-24T04:05:19.202804(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 cris177/Orca-Hermes-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model cris177/Orca-Hermes-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-24T04:05:19.202804(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"counterfactual_babylm_keys_to_pipps_2913\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"counterfactual_babylm_keys_to_pipps_2913\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

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

[]

[]

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

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

[ "license:other", "arxiv:2310.08560", "arxiv:1703.03400", "arxiv:1712.00559", "arxiv:2104.00597", "arxiv:1904.00420", "region:us" ]

[ "2310.08560", "1703.03400", "1712.00559", "2104.00597", "1904.00420" ]

[]

[]

[ "TAGS\n#license-other #arxiv-2310.08560 #arxiv-1703.03400 #arxiv-1712.00559 #arxiv-2104.00597 #arxiv-1904.00420 #region-us \n" ]

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Locutusque/TinyMistral-248M-v2.5\n\n\n\nDataset automatically created during the evaluation run of model Locutusque/TinyMistral-248M-v2.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-24T05:25:23.992452(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Locutusque/TinyMistral-248M-v2.5\n\n\n\nDataset automatically created during the evaluation run of model Locutusque/TinyMistral-248M-v2.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-24T05:25:23.992452(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of BarryFutureman/ChatMarc-YesAnotherMerge-7B\n\n\n\nDataset automatically created during the evaluation run of model BarryFutureman/ChatMarc-YesAnotherMerge-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 2024-01-24T05:34:18.479696(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of BarryFutureman/ChatMarc-YesAnotherMerge-7B\n\n\n\nDataset automatically created during the evaluation run of model BarryFutureman/ChatMarc-YesAnotherMerge-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 2024-01-24T05:34:18.479696(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:feature-extraction", "size_categories:10M<n<100M", "language:en", "license:cc-by-nc-sa-4.0", "arxiv:2307.06942", "region:us" ]

[ "2307.06942" ]

[ "en" ]

[ "# InternVid", "## Dataset Description\n\n- Homepage: InternVid\n- Repository: OpenGVLab\n- Paper: 2307.06942\n- Point of Contact: mailto:InternVideo", "## InternVid-10M-FLT\n\nWe present InternVid-10M-FLT, a subset of this dataset, consisting of 10 million video clips, with generated high-quality captions for publicly available web videos.", "## Download\n\nThe 10M samples are provided in jsonlines file. Columns include the videoID, timestamps, generated caption and their UMT similarity scores.\\", "## How to Use", "## Method\n\n!Caption Method\n\nIf you find this work useful for your research, please consider citing InternVid. Your acknowledgement would greatly help us in continuing to contribute resources to the research community." ]

[ "TAGS\n#task_categories-feature-extraction #size_categories-10M<n<100M #language-English #license-cc-by-nc-sa-4.0 #arxiv-2307.06942 #region-us \n", "# InternVid", "## Dataset Description\n\n- Homepage: InternVid\n- Repository: OpenGVLab\n- Paper: 2307.06942\n- Point of Contact: mailto:InternVideo", "## InternVid-10M-FLT\n\nWe present InternVid-10M-FLT, a subset of this dataset, consisting of 10 million video clips, with generated high-quality captions for publicly available web videos.", "## Download\n\nThe 10M samples are provided in jsonlines file. Columns include the videoID, timestamps, generated caption and their UMT similarity scores.\\", "## How to Use", "## Method\n\n!Caption Method\n\nIf you find this work useful for your research, please consider citing InternVid. Your acknowledgement would greatly help us in continuing to contribute resources to the research community." ]

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

[]

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

[ "task_categories:feature-extraction", "size_categories:100K<n<1M", "language:ru", "license:mit", "reviews", "region:us" ]

[]

[ "ru" ]

[]

[ "TAGS\n#task_categories-feature-extraction #size_categories-100K<n<1M #language-Russian #license-mit #reviews #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"JFLD_punipuni_monster\"\n\nSee here for the details of this corpus.\nFor the whole of the project, see our project page.\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"JFLD_punipuni_monster\"\n\nSee here for the details of this corpus.\nFor the whole of the project, see our project page.\n\nMore Information needed" ]