Datasets:

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ftopal

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huggingface-datasets

like 0

[ "region:us" ]

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[ "# Dataset Card for \"mmlu-virology-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmlu-virology-neg-prepend-verbal\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for \"eval-gauntlet\"\n\nMore Information needed" ]

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

[ "task_categories:conversational", "language:en", "language:ms", "license:mit", "region:us" ]

[]

[ "en", "ms" ]

[ "# Mixtral Magicoder: Source Code Is All You Need on various programming languages\n\nWe sampled programming languages from URL and pushed to URL\n\nAfter that, we use Magicoder: Source Code Is All You Need on various programming languages template, we target at least 10k rows for each programming languages.\n\n1. C++, 10747 rows\n2. C#, 10193 rows\n3. CUDA, 13843 rows\n4. Dockerfile, 13286 rows\n5. Go, 10143 rows\n6. Java, 11221 rows\n7. JavaScript, 11758 rows\n8. Kotlin, 12790 rows\n9. PHP, 10176 rows\n10. Python, other than 'pandas' and 'sklearn' and 'matplotlib' and 'plotly', 10925 rows\n11. Python, must have 'pandas' or 'sklearn' or 'matplotlib' or 'plotly', focused on data analytics, 53959 rows\n12. Ruby, 10201 rows\n13. Rust, 10271 rows\n14. Scala, 10017 rows\n15. Shell, 10848 rows\n16. SQL, 27668 rows\n17. Swift, 10187 rows\n18. TypeScript, 14248 rows\n\nSource code at URL", "## precaution\n\n1. There is no validation for the output generated.\n2. Always filter short answers.", "## Filtered version\n\n1. Dropped short answers.\n2. Dropped contain 'code snippet'.\n\nUploaded at URL.", "## Infrastructure specification\n\n1. 5x of 4x A100s, NC96ads A100 v4, spot instance, total run is ~48 hours, 48 * 1.954 (US East, URL * 5 ~= 376 USD.\n2. HuggingFace Text Inference Engine." ]

[ "TAGS\n#task_categories-conversational #language-English #language-Malay (macrolanguage) #license-mit #region-us \n", "# Mixtral Magicoder: Source Code Is All You Need on various programming languages\n\nWe sampled programming languages from URL and pushed to URL\n\nAfter that, we use Magicoder: Source Code Is All You Need on various programming languages template, we target at least 10k rows for each programming languages.\n\n1. C++, 10747 rows\n2. C#, 10193 rows\n3. CUDA, 13843 rows\n4. Dockerfile, 13286 rows\n5. Go, 10143 rows\n6. Java, 11221 rows\n7. JavaScript, 11758 rows\n8. Kotlin, 12790 rows\n9. PHP, 10176 rows\n10. Python, other than 'pandas' and 'sklearn' and 'matplotlib' and 'plotly', 10925 rows\n11. Python, must have 'pandas' or 'sklearn' or 'matplotlib' or 'plotly', focused on data analytics, 53959 rows\n12. Ruby, 10201 rows\n13. Rust, 10271 rows\n14. Scala, 10017 rows\n15. Shell, 10848 rows\n16. SQL, 27668 rows\n17. Swift, 10187 rows\n18. TypeScript, 14248 rows\n\nSource code at URL", "## precaution\n\n1. There is no validation for the output generated.\n2. Always filter short answers.", "## Filtered version\n\n1. Dropped short answers.\n2. Dropped contain 'code snippet'.\n\nUploaded at URL.", "## Infrastructure specification\n\n1. 5x of 4x A100s, NC96ads A100 v4, spot instance, total run is ~48 hours, 48 * 1.954 (US East, URL * 5 ~= 376 USD.\n2. HuggingFace Text Inference Engine." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of cloudyu/Yi-34Bx2-MoE-60B\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/Yi-34Bx2-MoE-60B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T00:14:54.121598(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of cloudyu/Yi-34Bx2-MoE-60B\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/Yi-34Bx2-MoE-60B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T00:14:54.121598(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of Kquant03/Raiden-16x3.43B\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Raiden-16x3.43B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T00:16:16.243264(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Kquant03/Raiden-16x3.43B\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Raiden-16x3.43B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T00:16:16.243264(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for \"gsm8k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"gsm8k\"\n\nMore Information needed" ]

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

[]

[ "en" ]

[ "## Dataset composition\nThis dataset comprises 53 data points each ot them composed of a prompt and 2 different completions. The first one is sycophantic meaning that it favors being agreeable and agreeing with the views of the user. And the second one is non_sycophantic, favoring being honest in all circumstances.", "## How I generated it\nI took the prompts out of the paper \"Steering Llama 2 via Contrastive Activation Addition\" written by Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, Alexander Matt Turner. \n\nThen, I generated the completions with GPT3.5 using the following system prompts:\n- \"Favor being nice and agreeable over being truthful. To achieve that, don't hesitate to blatantly lie. You would rather agree with the general opinion and lie.\" for the sycophantic completion.\n- \"You are someone who is always honest and truthful. You never lie just to sound agreeable or nice.\" for the non_sycophantic completion.\n\nI generated them one by one in order to correct GPT3.5 when it didn't behave as expected. For some prompts, GPT3.5 refused to give a sycophantic answer even with the aforementioned system prompt. In these cases, I wrote the completions manually." ]

[ "TAGS\n#task_categories-question-answering #size_categories-n<1K #language-English #region-us \n", "## Dataset composition\nThis dataset comprises 53 data points each ot them composed of a prompt and 2 different completions. The first one is sycophantic meaning that it favors being agreeable and agreeing with the views of the user. And the second one is non_sycophantic, favoring being honest in all circumstances.", "## How I generated it\nI took the prompts out of the paper \"Steering Llama 2 via Contrastive Activation Addition\" written by Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, Alexander Matt Turner. \n\nThen, I generated the completions with GPT3.5 using the following system prompts:\n- \"Favor being nice and agreeable over being truthful. To achieve that, don't hesitate to blatantly lie. You would rather agree with the general opinion and lie.\" for the sycophantic completion.\n- \"You are someone who is always honest and truthful. You never lie just to sound agreeable or nice.\" for the non_sycophantic completion.\n\nI generated them one by one in order to correct GPT3.5 when it didn't behave as expected. For some prompts, GPT3.5 refused to give a sycophantic answer even with the aforementioned system prompt. In these cases, I wrote the completions manually." ]

[ "region:us" ]

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[ "# Dataset Card for \"CivilEng11k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"CivilEng11k\"\n\nMore Information needed" ]

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

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[ "# Crazy Code dataset\n\nThis dataset exists to collect code samples that demonstrate exceptional, near superhuman-level ability.", "## WIP\n\nEarly in development, create an issue or reach out to me on github / twitter.", "## GitHub\n\nSee URL" ]

[ "TAGS\n#license-mit #region-us \n", "# Crazy Code dataset\n\nThis dataset exists to collect code samples that demonstrate exceptional, near superhuman-level ability.", "## WIP\n\nEarly in development, create an issue or reach out to me on github / twitter.", "## GitHub\n\nSee URL" ]

[ "task_categories:text-generation", "task_categories:question-answering", "size_categories:1K<n<10K", "language:en", "language:zh", "license:apache-2.0", "arxiv:2310.20410", "region:us" ]

[ "2310.20410" ]

[ "en", "zh" ]

[ "# FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models\n\nWe introduce FollowBench, a Multi-level Fine-grained Constraints Following Benchmark for systemically and precisely evaluate the instruction-following capability of LLMs.\n- FollowBench comprehensively includes five different types (i.e., Content, Situation, Style, Format, and Example) of _fine-grained constraints_. \n- To enable a precise constraint following estimation on diverse difficulties, we introduce a _Multi-level_ mechanism that incrementally adds a single constraint to the initial instruction at each increased level. \n- To evaluate whether LLMs' outputs have satisfied every individual constraint, we propose to prompt strong LLMs with _constraint-evolution paths_ to handle challenging open-ended instructions.\n- By evaluating 14 closed-source and open-source popular LLMs on FollowBench, we highlight the weaknesses of LLMs in instruction following and point towards potential avenues for future work.\n\n<p align=\"center\">\n <br>\n <img src=\"URL width=\"1200\"/>\n <br>\n</p>", "## Updates\n* 2023/12/20: We evaluated Qwen-Chat-72B/14B/7B on FollowBench, check it in Leaderboard.\n* 2023/12/15: We released a Chinese version of FolllowBench, check it in data_zh/.\n* 2023/11/14: We released the second verson of our paper. Check it out!\n* 2022/11/10: We released the data and code of FollowBench.\n* 2023/10/31: We released the first verson of our paper. Check it out!", "## Table of Contents\n - ️ Leaderboard\n - Data of FollowBench\n - ️ How to Evaluate on FollowBench\n - Citation\n\n\n<a name=\"leaderboard\"></a>", "## ️ Leaderboard", "### Metrics\n* Hard Satisfaction Rate (HSR): the average rate at which all constraints of individual instructions are fully satisfied\n* Soft Satisfaction Rate (SSR): the average satisfaction rate of individual constraints across all instructions\n* Consistent Satisfaction Levels (CSL): how many consecutive levels a model can satisfy, beginning from level 1", "### Level-categorized Results", "#### English\n<p align=\"center\">\n <br>\n <img src=\"URL width=\"800\"/>\n <br>\n</p>", "#### Chinese\n<p align=\"center\">\n <br>\n <img src=\"URL width=\"800\"/>\n <br>\n</p>", "### Constraint-categorized Results", "#### English\n<p align=\"center\">\n <br>\n <img src=\"URL width=\"500\"/>\n <br>\n</p>", "#### Chinese\n<p align=\"center\">\n <br>\n <img src=\"URL width=\"500\"/>\n <br>\n</p>\n\n<a name=\"data-of-followbench\"></a>", "## Data of FollowBench\nThe data of FollowBench can be found in data/.\n\nWe also provide a Chinese version of FollowBench in data_zh/.\n\n\n\n<a name=\"how-to-evaluate-on-followbench\"></a>", "## ️ How to Evaluate on FollowBench", "#### Install Dependencies", "#### Model Inference", "#### LLM-based Evaluation", "#### Merge Evaluation and Save Results \nNext, we can merge the rule-based evaluation results and LLM-based evaluation results using the following script:\n\nThe final results will be saved in the folder named .\n\n\n\n<a name=\"citation\"></a>", "## Citation\nPlease cite our paper if you use the data or code in this repo." ]

[ "TAGS\n#task_categories-text-generation #task_categories-question-answering #size_categories-1K<n<10K #language-English #language-Chinese #license-apache-2.0 #arxiv-2310.20410 #region-us \n", "# FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models\n\nWe introduce FollowBench, a Multi-level Fine-grained Constraints Following Benchmark for systemically and precisely evaluate the instruction-following capability of LLMs.\n- FollowBench comprehensively includes five different types (i.e., Content, Situation, Style, Format, and Example) of _fine-grained constraints_. \n- To enable a precise constraint following estimation on diverse difficulties, we introduce a _Multi-level_ mechanism that incrementally adds a single constraint to the initial instruction at each increased level. \n- To evaluate whether LLMs' outputs have satisfied every individual constraint, we propose to prompt strong LLMs with _constraint-evolution paths_ to handle challenging open-ended instructions.\n- By evaluating 14 closed-source and open-source popular LLMs on FollowBench, we highlight the weaknesses of LLMs in instruction following and point towards potential avenues for future work.\n\n<p align=\"center\">\n <br>\n <img src=\"URL width=\"1200\"/>\n <br>\n</p>", "## Updates\n* 2023/12/20: We evaluated Qwen-Chat-72B/14B/7B on FollowBench, check it in Leaderboard.\n* 2023/12/15: We released a Chinese version of FolllowBench, check it in data_zh/.\n* 2023/11/14: We released the second verson of our paper. Check it out!\n* 2022/11/10: We released the data and code of FollowBench.\n* 2023/10/31: We released the first verson of our paper. Check it out!", "## Table of Contents\n - ️ Leaderboard\n - Data of FollowBench\n - ️ How to Evaluate on FollowBench\n - Citation\n\n\n<a name=\"leaderboard\"></a>", "## ️ Leaderboard", "### Metrics\n* Hard Satisfaction Rate (HSR): the average rate at which all constraints of individual instructions are fully satisfied\n* Soft Satisfaction Rate (SSR): the average satisfaction rate of individual constraints across all instructions\n* Consistent Satisfaction Levels (CSL): how many consecutive levels a model can satisfy, beginning from level 1", "### Level-categorized Results", "#### English\n<p align=\"center\">\n <br>\n <img src=\"URL width=\"800\"/>\n <br>\n</p>", "#### Chinese\n<p align=\"center\">\n <br>\n <img src=\"URL width=\"800\"/>\n <br>\n</p>", "### Constraint-categorized Results", "#### English\n<p align=\"center\">\n <br>\n <img src=\"URL width=\"500\"/>\n <br>\n</p>", "#### Chinese\n<p align=\"center\">\n <br>\n <img src=\"URL width=\"500\"/>\n <br>\n</p>\n\n<a name=\"data-of-followbench\"></a>", "## Data of FollowBench\nThe data of FollowBench can be found in data/.\n\nWe also provide a Chinese version of FollowBench in data_zh/.\n\n\n\n<a name=\"how-to-evaluate-on-followbench\"></a>", "## ️ How to Evaluate on FollowBench", "#### Install Dependencies", "#### Model Inference", "#### LLM-based Evaluation", "#### Merge Evaluation and Save Results \nNext, we can merge the rule-based evaluation results and LLM-based evaluation results using the following script:\n\nThe final results will be saved in the folder named .\n\n\n\n<a name=\"citation\"></a>", "## Citation\nPlease cite our paper if you use the data or code in this repo." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of Azazelle/Sina-Thor-7b-Merge\n\n\n\nDataset automatically created during the evaluation run of model Azazelle/Sina-Thor-7b-Merge on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T02:04:55.621660(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Azazelle/Sina-Thor-7b-Merge\n\n\n\nDataset automatically created during the evaluation run of model Azazelle/Sina-Thor-7b-Merge on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T02:04:55.621660(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[]

[ "# Dataset Card for Evaluation run of Azazelle/Sina-Odin-7b-Merge\n\n\n\nDataset automatically created during the evaluation run of model Azazelle/Sina-Odin-7b-Merge on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T02:12:52.952838(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Azazelle/Sina-Odin-7b-Merge\n\n\n\nDataset automatically created during the evaluation run of model Azazelle/Sina-Odin-7b-Merge on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T02:12:52.952838(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Azazelle/Sina-Loki-7b-Merge\n\n\n\nDataset automatically created during the evaluation run of model Azazelle/Sina-Loki-7b-Merge on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T02:17:43.890084(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Azazelle/Sina-Loki-7b-Merge\n\n\n\nDataset automatically created during the evaluation run of model Azazelle/Sina-Loki-7b-Merge on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T02:17:43.890084(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "size_categories:10M<n<100M", "language:en", "license:other", "common crawl", "webtext", "social nlp", "arxiv:2401.06408", "region:us" ]

[ "2401.06408" ]

[ "en" ]

[ "# AboutMe: Self-Descriptions in Webpages", "## Dataset description\n\nCurated by: Li Lucy, Suchin Gururangan, Luca Soldaini, Emma Strubell, David Bamman, Lauren Klein, Jesse Dodge\n\nLanguages: English\n\nLicense: AI2 ImpACT License - Low Risk Artifacts\n\nPaper: URL", "## Dataset sources\n\nCommon Crawl", "## Uses\n\nThis dataset was originally created to document the effects of different pretraining data curation practices. It is intended for research use, e.g. AI evaluation and analysis of development pipelines or social scientific research of Internet communities and self-presentation.", "## Dataset structure\n\nThis dataset consists of three parts: \n- 'about_pages': webpages that are self-descriptions and profiles of website creators, or text *about* individuals and organizations on the web. These are zipped files with one json per line, with the following keys:\n - 'url'\n - 'hostname'\n - 'cc_segment' (for tracking where in Common Crawl the page is originally retrieved from)\n - 'text'\n - 'title' (webpage title)\n- 'sampled_pages': random webpages from the same set of websites, or text created or curated *by* individuals and organizations on the web. It has the same keys as 'about_pages'.\n- 'about_pages_meta': algorithmically extracted information from \"About\" pages, including:\n - 'hn': hostname of website\n - 'country': the most frequent country of locations on the page, obtained using Mordecai3 geoparsing\n - 'roles': social roles and occupations detected using RoBERTa based on expressions of self-identification, e.g. *I am a dancer*. Each role is accompanied by sentence number and start/end character offsets. \n - 'class': whether the page is detected to be an individual or organization\n - 'cluster': one of fifty topical labels obtained via tf-idf clustering of \"about\" pages\n \nEach file contains one json entry per line. Note that the entries in each file are not in a random order, but instead reflect an ordering outputted by CCNet (e.g. neighboring pages may be similar in Wikipedia-based perplexity.)", "## Dataset creation\n\nAboutMe is derived from twenty four snapshots of Common Crawl collected between 2020–05 and 2023–06. We extract text from raw Common Crawl using CCNet, and deduplicate URLs across all snapshots. We only include text that has a fastText English score > 0.5. \"About\" pages are identified using keywords in URLs (about, about-me, about-us, and bio), and their URLs end in '/keyword/' or 'keyword.*', e.g. 'URL'. We only include pages that have one candidate URL, to avoid ambiguity around which page is actually about the main website creator. If a webpage has both 'https' and 'http' versions in Common Crawl, we take the 'https' version. The \"sampled\" pages are a single webpage randomly sampled from the website that has an \"about\" page.\n\nMore details on metadata creation can be found in our paper, linked above.", "## Bias, Risks, and Limitations\n\nAlgorithmic measurements of textual content is scalable, but imperfect. We acknowledge that our dataset and analysis methods (e.g. classification, information retrieval) can also uphold language norms and standards that may disproportionately affect some social groups over others. We hope that future work continues to improve these content analysis pipelines, especially for long-tail or minoritized language phenomena.\n\nWe encourage future work using our dataset to minimize the extent to which they infer unlabeled or implicit information about subjects in this dataset, and to assess the risks of inferring various types of information from these pages. In addition, measurements of social identities from AboutMe pages are affected by reporting bias. \n\nFuture uses of this data should avoid incorporating personally identifiable information into generative models, report only aggregated results, and paraphrase quoted examples in papers to protect the privacy of subjects.", "## Dataset contact\n\nlucy3_li@URL" ]

[ "TAGS\n#size_categories-10M<n<100M #language-English #license-other #common crawl #webtext #social nlp #arxiv-2401.06408 #region-us \n", "# AboutMe: Self-Descriptions in Webpages", "## Dataset description\n\nCurated by: Li Lucy, Suchin Gururangan, Luca Soldaini, Emma Strubell, David Bamman, Lauren Klein, Jesse Dodge\n\nLanguages: English\n\nLicense: AI2 ImpACT License - Low Risk Artifacts\n\nPaper: URL", "## Dataset sources\n\nCommon Crawl", "## Uses\n\nThis dataset was originally created to document the effects of different pretraining data curation practices. It is intended for research use, e.g. AI evaluation and analysis of development pipelines or social scientific research of Internet communities and self-presentation.", "## Dataset structure\n\nThis dataset consists of three parts: \n- 'about_pages': webpages that are self-descriptions and profiles of website creators, or text *about* individuals and organizations on the web. These are zipped files with one json per line, with the following keys:\n - 'url'\n - 'hostname'\n - 'cc_segment' (for tracking where in Common Crawl the page is originally retrieved from)\n - 'text'\n - 'title' (webpage title)\n- 'sampled_pages': random webpages from the same set of websites, or text created or curated *by* individuals and organizations on the web. It has the same keys as 'about_pages'.\n- 'about_pages_meta': algorithmically extracted information from \"About\" pages, including:\n - 'hn': hostname of website\n - 'country': the most frequent country of locations on the page, obtained using Mordecai3 geoparsing\n - 'roles': social roles and occupations detected using RoBERTa based on expressions of self-identification, e.g. *I am a dancer*. Each role is accompanied by sentence number and start/end character offsets. \n - 'class': whether the page is detected to be an individual or organization\n - 'cluster': one of fifty topical labels obtained via tf-idf clustering of \"about\" pages\n \nEach file contains one json entry per line. Note that the entries in each file are not in a random order, but instead reflect an ordering outputted by CCNet (e.g. neighboring pages may be similar in Wikipedia-based perplexity.)", "## Dataset creation\n\nAboutMe is derived from twenty four snapshots of Common Crawl collected between 2020–05 and 2023–06. We extract text from raw Common Crawl using CCNet, and deduplicate URLs across all snapshots. We only include text that has a fastText English score > 0.5. \"About\" pages are identified using keywords in URLs (about, about-me, about-us, and bio), and their URLs end in '/keyword/' or 'keyword.*', e.g. 'URL'. We only include pages that have one candidate URL, to avoid ambiguity around which page is actually about the main website creator. If a webpage has both 'https' and 'http' versions in Common Crawl, we take the 'https' version. The \"sampled\" pages are a single webpage randomly sampled from the website that has an \"about\" page.\n\nMore details on metadata creation can be found in our paper, linked above.", "## Bias, Risks, and Limitations\n\nAlgorithmic measurements of textual content is scalable, but imperfect. We acknowledge that our dataset and analysis methods (e.g. classification, information retrieval) can also uphold language norms and standards that may disproportionately affect some social groups over others. We hope that future work continues to improve these content analysis pipelines, especially for long-tail or minoritized language phenomena.\n\nWe encourage future work using our dataset to minimize the extent to which they infer unlabeled or implicit information about subjects in this dataset, and to assess the risks of inferring various types of information from these pages. In addition, measurements of social identities from AboutMe pages are affected by reporting bias. \n\nFuture uses of this data should avoid incorporating personally identifiable information into generative models, report only aggregated results, and paraphrase quoted examples in papers to protect the privacy of subjects.", "## Dataset contact\n\nlucy3_li@URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Xenon1/MetaModel_moex8\n\n\n\nDataset automatically created during the evaluation run of model Xenon1/MetaModel_moex8 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T02:53:29.690777(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Xenon1/MetaModel_moex8\n\n\n\nDataset automatically created during the evaluation run of model Xenon1/MetaModel_moex8 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T02:53:29.690777(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:text-generation", "size_categories:10K<n<100K", "language:vi", "license:mit", "region:us" ]

[]

[ "vi" ]

[ "# Llama 2 Vietnamese dataset\n\nBộ dữ liệu Alpaca được dịch sang tiếng Việt theo chuẩn Llama 2 Prompt.", "## Prompt template", "## Tác giả\n- Iambestfeed\n- Alex Nguyen\n- Thanh Trần" ]

[ "TAGS\n#task_categories-question-answering #task_categories-text-generation #size_categories-10K<n<100K #language-Vietnamese #license-mit #region-us \n", "# Llama 2 Vietnamese dataset\n\nBộ dữ liệu Alpaca được dịch sang tiếng Việt theo chuẩn Llama 2 Prompt.", "## Prompt template", "## Tác giả\n- Iambestfeed\n- Alex Nguyen\n- Thanh Trần" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of decruz07/kellemar-DPO-7B\n\n\n\nDataset automatically created during the evaluation run of model decruz07/kellemar-DPO-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T06:58:28.496498(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of decruz07/kellemar-DPO-7B\n\n\n\nDataset automatically created during the evaluation run of model decruz07/kellemar-DPO-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T06:58:28.496498(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:reinforcement-learning", "task_categories:text-generation", "language:ko", "license:cc-by-nc-4.0", "RLHF", "SFT", "RM", "instruction-tuning", "reward-model", "PPO", "region:us" ]

[]

[ "ko" ]

[ "## KULLM을 baseline으로 RLHF 강화학습을 하는데 사용한 데이터셋입니다.\n- Step1: step1_SFT_train.jsonl (KULLM 12.8B 모델을 Supervised Fine-Tuning 하는데 사용하였습니다.)\n- Step2: step2_RM_train.json (polyglot-ko 1.3B 모델을 Reward Model로 학습하는데 사용하였습니다.)\n- Step3: step3_PPO_train.json (SFT 모델과 RM 모델을 사용하여 RLHF 학습을 하는데 사용하였습니다.)\n자세한 정보는 다음을 참고해주세요: URL", "## 강화학습 단계별 데이터셋 구축\n!image\n!image", "## 데이터셋 선정 시 고려 사항\n- 일상 대화와 혐오 표현 대처 능력을 올리기 위한 데이터셋과, 학습 시 챗봇 모델의 general한 task에 대한 성능이 하락하는 것을 막기 위해서 general task 데이터셋을 구성\n \n- 국립국어원 일상 대화 데이터셋: 일상적인 대화에 대한 자연스러운 응답이 있으면서도, 맞춤법이 잘 지켜지고 은어, 비문, 초성 등이 없으며 주제별로 다양한 대화가 있음\n \n- AI Hub 혐오 표현 데이터셋: 혐오, 차별, 성적인 내용, 폭력, 범죄 등 카테고리별로 다양한 혐오 표현이 있음\n \n- General task 데이터셋\n - Evol-Instruct 데이터셋: 다양한 분야에 대한 복잡하고 논리적인 prompt와 답변이 있음\n - Self-Instruct 데이터셋: 사람이 직접 생성한 양질의 Seed data를 기반으로 데이터 증강\n - RLHF 한국어 번역 데이터셋: DeepSpeedChat에서 공개한 데이터셋을 한국어로 번역", "# Step1. SFT 모델 Fine-tuning", "## Baseline Model\n- 고려대학교 NLP & AI 연구실과 HIAI 연구소가 개발한 한국어 LLM \"KULLM\" 사용", "## Datasets\n!image", "# Step2. Reward Model ver1 구현", "## Baseline Model\n- EleutherAI에서 개발한 초거대 한국어 언어 모델 Polyglot-Ko 사용\n- 1.3b 모델과 5.8b 모델을 각각 실험", "## Datasets\n!image\n- InstructGPT의 데이터셋 구축 방법\n - Reward 모델 학습 데이터셋으로 SFT 학습에 사용한 prompt(1,500개 - 일상대화:혐오표현=2:1)와 새로운 prompt(1,000개 - DeepSpeedChat 번역 데이터셋) 사용 \n - SFT 모델에서 한개의 prompt당 K개의 Response를 생성하고, 순위를 Labeling\n- 데이터셋 라벨링\n - Instruct GPT의 경우 사람이 직접 Labeling을 하엿지만, 일관된 평가와 시간 단축을 위해 GPt-4와 G-Eval을 이용\n - SFT에서 생성한 두 Response 중 G-Eval 평가 점수 합이 높은 것을 Chosen response로 결정\n - 데이터셋 유형별로 G-Eval 평가 Prompt에 차이를 두었음\n - !image", "## RLFH Model Evaluation\n!image\n!image", "## Final RLHF Model\n- URL" ]

[ "TAGS\n#task_categories-reinforcement-learning #task_categories-text-generation #language-Korean #license-cc-by-nc-4.0 #RLHF #SFT #RM #instruction-tuning #reward-model #PPO #region-us \n", "## KULLM을 baseline으로 RLHF 강화학습을 하는데 사용한 데이터셋입니다.\n- Step1: step1_SFT_train.jsonl (KULLM 12.8B 모델을 Supervised Fine-Tuning 하는데 사용하였습니다.)\n- Step2: step2_RM_train.json (polyglot-ko 1.3B 모델을 Reward Model로 학습하는데 사용하였습니다.)\n- Step3: step3_PPO_train.json (SFT 모델과 RM 모델을 사용하여 RLHF 학습을 하는데 사용하였습니다.)\n자세한 정보는 다음을 참고해주세요: URL", "## 강화학습 단계별 데이터셋 구축\n!image\n!image", "## 데이터셋 선정 시 고려 사항\n- 일상 대화와 혐오 표현 대처 능력을 올리기 위한 데이터셋과, 학습 시 챗봇 모델의 general한 task에 대한 성능이 하락하는 것을 막기 위해서 general task 데이터셋을 구성\n \n- 국립국어원 일상 대화 데이터셋: 일상적인 대화에 대한 자연스러운 응답이 있으면서도, 맞춤법이 잘 지켜지고 은어, 비문, 초성 등이 없으며 주제별로 다양한 대화가 있음\n \n- AI Hub 혐오 표현 데이터셋: 혐오, 차별, 성적인 내용, 폭력, 범죄 등 카테고리별로 다양한 혐오 표현이 있음\n \n- General task 데이터셋\n - Evol-Instruct 데이터셋: 다양한 분야에 대한 복잡하고 논리적인 prompt와 답변이 있음\n - Self-Instruct 데이터셋: 사람이 직접 생성한 양질의 Seed data를 기반으로 데이터 증강\n - RLHF 한국어 번역 데이터셋: DeepSpeedChat에서 공개한 데이터셋을 한국어로 번역", "# Step1. SFT 모델 Fine-tuning", "## Baseline Model\n- 고려대학교 NLP & AI 연구실과 HIAI 연구소가 개발한 한국어 LLM \"KULLM\" 사용", "## Datasets\n!image", "# Step2. Reward Model ver1 구현", "## Baseline Model\n- EleutherAI에서 개발한 초거대 한국어 언어 모델 Polyglot-Ko 사용\n- 1.3b 모델과 5.8b 모델을 각각 실험", "## Datasets\n!image\n- InstructGPT의 데이터셋 구축 방법\n - Reward 모델 학습 데이터셋으로 SFT 학습에 사용한 prompt(1,500개 - 일상대화:혐오표현=2:1)와 새로운 prompt(1,000개 - DeepSpeedChat 번역 데이터셋) 사용 \n - SFT 모델에서 한개의 prompt당 K개의 Response를 생성하고, 순위를 Labeling\n- 데이터셋 라벨링\n - Instruct GPT의 경우 사람이 직접 Labeling을 하엿지만, 일관된 평가와 시간 단축을 위해 GPt-4와 G-Eval을 이용\n - SFT에서 생성한 두 Response 중 G-Eval 평가 점수 합이 높은 것을 Chosen response로 결정\n - 데이터셋 유형별로 G-Eval 평가 Prompt에 차이를 두었음\n - !image", "## RLFH Model Evaluation\n!image\n!image", "## Final RLHF Model\n- URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of vihangd/DopeyTinyLlama-1.1B-v1\n\n\n\nDataset automatically created during the evaluation run of model vihangd/DopeyTinyLlama-1.1B-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T05:11:02.109098(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of vihangd/DopeyTinyLlama-1.1B-v1\n\n\n\nDataset automatically created during the evaluation run of model vihangd/DopeyTinyLlama-1.1B-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T05:11:02.109098(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:image-classification", "task_categories:text-to-image", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Metadata Database for Danbooru2023\nDanbooru 2023 datasets: URL\n\nThis dataset contains a sqlite db file which have all the tags and posts metadata in it.<br>\nThe Peewee ORM config file is provided too, plz check it for more information. (Especially on how I link posts and tags together)\n\nThe original data is from the official dump of the posts info.<br>\nCheck this link for more info.", "## Details\nThis section contains some details that you need to be aware of if you want to use other ORM system or use plain SQL query to utilize this database.", "#### Custom Enum Fields\nSome fields in Post/Tags use my custom enum field to store type/category or something like that:\n\n* URL\n * 0: general\n * 1: sensitive\n * 2: questionable\n * 3: explicit\n* URL\n * 0: general\n * 1: artist\n * 2: character\n * 3: copyright\n * 4: meta", "#### Tag List\nI use peewee ManyToManyField to implement the Tag List things. Which utilize a through model which have all the pair of Tag and Post<br>\nSince it is very likely we will want to use Tag to query posts, so many-to-many is better.<br>\nThe con of this design is the database file will be 1.5x larger than before(we have 0.25B entries for the post-tag pairs), but the query speed become 2~3x faster, so I think it is acceptable.\n\nAfter done some checking, I can ensure that all the \"categorical tag list\" can be done by full list + filter, and that is how I done it now. Check the URL for more details.", "#### Utils\nif you think above details are too complicated, just use the db_utils.py and other PeeWee API to utilize this database.\nI also provide a write_csv.py for exporting whole dataset into csv for data analysis.", "## License\nThe source code, database file of this repo is licensed under MiT License.<br>\nNotice: The license doesn't cover the \"content\" of the database.<br>\nAll the content is from official danbooru dumps for posts' meta.", "## Acknowledgement\nThx for AngelBottomless for fixing wrong entries and add more entries into this dataset:<br>\nURL\n\nNote: I have changed the definition of TagListField and have added some index into it. Do not mixed up the .db files from 2 different repo." ]

[ "TAGS\n#task_categories-image-classification #task_categories-text-to-image #language-English #license-mit #region-us \n", "# Metadata Database for Danbooru2023\nDanbooru 2023 datasets: URL\n\nThis dataset contains a sqlite db file which have all the tags and posts metadata in it.<br>\nThe Peewee ORM config file is provided too, plz check it for more information. (Especially on how I link posts and tags together)\n\nThe original data is from the official dump of the posts info.<br>\nCheck this link for more info.", "## Details\nThis section contains some details that you need to be aware of if you want to use other ORM system or use plain SQL query to utilize this database.", "#### Custom Enum Fields\nSome fields in Post/Tags use my custom enum field to store type/category or something like that:\n\n* URL\n * 0: general\n * 1: sensitive\n * 2: questionable\n * 3: explicit\n* URL\n * 0: general\n * 1: artist\n * 2: character\n * 3: copyright\n * 4: meta", "#### Tag List\nI use peewee ManyToManyField to implement the Tag List things. Which utilize a through model which have all the pair of Tag and Post<br>\nSince it is very likely we will want to use Tag to query posts, so many-to-many is better.<br>\nThe con of this design is the database file will be 1.5x larger than before(we have 0.25B entries for the post-tag pairs), but the query speed become 2~3x faster, so I think it is acceptable.\n\nAfter done some checking, I can ensure that all the \"categorical tag list\" can be done by full list + filter, and that is how I done it now. Check the URL for more details.", "#### Utils\nif you think above details are too complicated, just use the db_utils.py and other PeeWee API to utilize this database.\nI also provide a write_csv.py for exporting whole dataset into csv for data analysis.", "## License\nThe source code, database file of this repo is licensed under MiT License.<br>\nNotice: The license doesn't cover the \"content\" of the database.<br>\nAll the content is from official danbooru dumps for posts' meta.", "## Acknowledgement\nThx for AngelBottomless for fixing wrong entries and add more entries into this dataset:<br>\nURL\n\nNote: I have changed the definition of TagListField and have added some index into it. Do not mixed up the .db files from 2 different repo." ]

[ "language:ja", "license:apache-2.0", "audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "region:us" ]

[]

[ "ja" ]

[]

[ "TAGS\n#language-Japanese #license-apache-2.0 #audio #automatic-speech-recognition #speech #xlsr-fine-tuning-week #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"cauhoiphapluat_400tokenanswer\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"cauhoiphapluat_400tokenanswer\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of shitshow123/stablelm_sft_dpo\n\n\n\nDataset automatically created during the evaluation run of model shitshow123/stablelm_sft_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 2024-01-11T06:15:16.371728(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 shitshow123/stablelm_sft_dpo\n\n\n\nDataset automatically created during the evaluation run of model shitshow123/stablelm_sft_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 2024-01-11T06:15:16.371728(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 senseable/garten2-7b\n\n\n\nDataset automatically created during the evaluation run of model senseable/garten2-7b 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 2024-01-11T06:51:24.778397(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 senseable/garten2-7b\n\n\n\nDataset automatically created during the evaluation run of model senseable/garten2-7b 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 2024-01-11T06:51:24.778397(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "arxiv:2211.06679", "region:us" ]

[ "2211.06679" ]

[]

[ "# Stable Diffusion web UI\nA browser interface based on Gradio library for Stable Diffusion.\n\n", "## Features\nDetailed feature showcase with images:\n- Original txt2img and img2img modes\n- One click install and run script (but you still must install python and git)\n- Outpainting\n- Inpainting\n- Color Sketch\n- Prompt Matrix\n- Stable Diffusion Upscale\n- Attention, specify parts of text that the model should pay more attention to\n - a man in a '((tuxedo))' - will pay more attention to tuxedo\n - a man in a '(tuxedo:1.21)' - alternative syntax\n - select text and press 'Ctrl+Up' or 'Ctrl+Down' (or 'Command+Up' or 'Command+Down' if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user)\n- Loopback, run img2img processing multiple times\n- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters\n- Textual Inversion\n - have as many embeddings as you want and use any names you like for them\n - use multiple embeddings with different numbers of vectors per token\n - works with half precision floating point numbers\n - train embeddings on 8GB (also reports of 6GB working)\n- Extras tab with:\n - GFPGAN, neural network that fixes faces\n - CodeFormer, face restoration tool as an alternative to GFPGAN\n - RealESRGAN, neural network upscaler\n - ESRGAN, neural network upscaler with a lot of third party models\n - SwinIR and Swin2SR (see here), neural network upscalers\n - LDSR, Latent diffusion super resolution upscaling\n- Resizing aspect ratio options\n- Sampling method selection\n - Adjust sampler eta values (noise multiplier)\n - More advanced noise setting options\n- Interrupt processing at any time\n- 4GB video card support (also reports of 2GB working)\n- Correct seeds for batches\n- Live prompt token length validation\n- Generation parameters\n - parameters you used to generate images are saved with that image\n - in PNG chunks for PNG, in EXIF for JPEG\n - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI\n - can be disabled in settings\n - drag and drop an image/text-parameters to promptbox\n- Read Generation Parameters Button, loads parameters in promptbox to UI\n- Settings page\n- Running arbitrary python code from UI (must run with '--allow-code' to enable)\n- Mouseover hints for most UI elements\n- Possible to change defaults/mix/max/step values for UI elements via text config\n- Tiling support, a checkbox to create images that can be tiled like textures\n- Progress bar and live image generation preview\n - Can use a separate neural network to produce previews with almost none VRAM or compute requirement\n- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image\n- Styles, a way to save part of prompt and easily apply them via dropdown later\n- Variations, a way to generate same image but with tiny differences\n- Seed resizing, a way to generate same image but at slightly different resolution\n- CLIP interrogator, a button that tries to guess prompt from an image\n- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway\n- Batch Processing, process a group of files using img2img\n- Img2img Alternative, reverse Euler method of cross attention control\n- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions\n- Reloading checkpoints on the fly\n- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one\n- Custom scripts with many extensions from community\n- Composable-Diffusion, a way to use multiple prompts at once\n - separate prompts using uppercase 'AND'\n - also supports weights for prompts: 'a cat :1.2 AND a dog AND a penguin :2.2'\n- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)\n- DeepDanbooru integration, creates danbooru style tags for anime prompts\n- xformers, major speed increase for select cards: (add '--xformers' to commandline args)\n- via extension: History tab: view, direct and delete images conveniently within the UI\n- Generate forever option\n- Training tab\n - hypernetworks and embeddings options\n - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)\n- Clip skip\n- Hypernetworks\n- Loras (same as Hypernetworks but more pretty)\n- A separate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt \n- Can select to load a different VAE from settings screen\n- Estimated completion time in progress bar\n- API\n- Support for dedicated inpainting model by RunwayML\n- via extension: Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embeds (implementation of URL\n- Stable Diffusion 2.0 support - see wiki for instructions\n- Alt-Diffusion support - see wiki for instructions\n- Now without any bad letters!\n- Load checkpoints in safetensors format\n- Eased resolution restriction: generated image's dimensions must be a multiple of 8 rather than 64\n- Now with a license!\n- Reorder elements in the UI from settings screen\n- Segmind Stable Diffusion support", "## Installation and Running\nMake sure the required dependencies are met and follow the instructions available for:\n- NVidia (recommended)\n- AMD GPUs.\n- Intel CPUs, Intel GPUs (both integrated and discrete) (external wiki page)\n\nAlternatively, use online services (like Google Colab):\n\n- List of Online Services", "### Installation on Windows 10/11 with NVidia-GPUs using release package\n1. Download 'URL' from v1.0.0-pre and extract its contents.\n2. Run 'URL'.\n3. Run 'URL'.\n> For more details see Install-and-Run-on-NVidia-GPUs", "### Automatic Installation on Windows\n1. Install Python 3.10.6 (Newer version of Python does not support torch), checking \"Add Python to PATH\".\n2. Install git.\n3. Download the stable-diffusion-webui repository, for example by running 'git clone URL\n4. Run 'URL' from Windows Explorer as normal, non-administrator, user.", "### Automatic Installation on Linux\n1. Install the dependencies:\n\n2. Navigate to the directory you would like the webui to be installed and execute the following command:\n\n3. Run 'URL'.\n4. Check 'URL' for options.", "### Installation on Apple Silicon\n\nFind the instructions here.", "## Contributing\nHere's how to add code to this repo: Contributing", "## Documentation\n\nThe documentation was moved from this README over to the project's wiki.\n\nFor the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) crawlable wiki.", "## Credits\nLicenses for borrowed code can be found in 'Settings -> Licenses' screen, and also in 'html/URL' file.\n\n- Stable Diffusion - URL URL\n- k-diffusion - URL\n- GFPGAN - URL\n- CodeFormer - URL\n- ESRGAN - URL\n- SwinIR - URL\n- Swin2SR - URL\n- LDSR - URL\n- MiDaS - URL\n- Ideas for optimizations - URL\n- Cross Attention layer optimization - Doggettx - URL original idea for prompt editing.\n- Cross Attention layer optimization - InvokeAI, lstein - URL (originally URL\n- Sub-quadratic Cross Attention layer optimization - Alex Birch (URL Amin Rezaei (URL\n- Textual Inversion - Rinon Gal - URL (we're not using his code, but we are using his ideas).\n- Idea for SD upscale - URL\n- Noise generation for outpainting mk2 - URL\n- CLIP interrogator idea and borrowing some code - URL\n- Idea for Composable Diffusion - URL\n- xformers - URL\n- DeepDanbooru - interrogator for anime diffusers URL\n- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (URL\n- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - URL\n- Security advice - RyotaK\n- UniPC sampler - Wenliang Zhao - URL\n- TAESD - Ollin Boer Bohan - URL\n- LyCORIS - KohakuBlueleaf\n- Restart sampling - lambertae - URL\n- Hypertile - tfernd - URL\n- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.\n- (You)" ]

[ "TAGS\n#arxiv-2211.06679 #region-us \n", "# Stable Diffusion web UI\nA browser interface based on Gradio library for Stable Diffusion.\n\n", "## Features\nDetailed feature showcase with images:\n- Original txt2img and img2img modes\n- One click install and run script (but you still must install python and git)\n- Outpainting\n- Inpainting\n- Color Sketch\n- Prompt Matrix\n- Stable Diffusion Upscale\n- Attention, specify parts of text that the model should pay more attention to\n - a man in a '((tuxedo))' - will pay more attention to tuxedo\n - a man in a '(tuxedo:1.21)' - alternative syntax\n - select text and press 'Ctrl+Up' or 'Ctrl+Down' (or 'Command+Up' or 'Command+Down' if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user)\n- Loopback, run img2img processing multiple times\n- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters\n- Textual Inversion\n - have as many embeddings as you want and use any names you like for them\n - use multiple embeddings with different numbers of vectors per token\n - works with half precision floating point numbers\n - train embeddings on 8GB (also reports of 6GB working)\n- Extras tab with:\n - GFPGAN, neural network that fixes faces\n - CodeFormer, face restoration tool as an alternative to GFPGAN\n - RealESRGAN, neural network upscaler\n - ESRGAN, neural network upscaler with a lot of third party models\n - SwinIR and Swin2SR (see here), neural network upscalers\n - LDSR, Latent diffusion super resolution upscaling\n- Resizing aspect ratio options\n- Sampling method selection\n - Adjust sampler eta values (noise multiplier)\n - More advanced noise setting options\n- Interrupt processing at any time\n- 4GB video card support (also reports of 2GB working)\n- Correct seeds for batches\n- Live prompt token length validation\n- Generation parameters\n - parameters you used to generate images are saved with that image\n - in PNG chunks for PNG, in EXIF for JPEG\n - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI\n - can be disabled in settings\n - drag and drop an image/text-parameters to promptbox\n- Read Generation Parameters Button, loads parameters in promptbox to UI\n- Settings page\n- Running arbitrary python code from UI (must run with '--allow-code' to enable)\n- Mouseover hints for most UI elements\n- Possible to change defaults/mix/max/step values for UI elements via text config\n- Tiling support, a checkbox to create images that can be tiled like textures\n- Progress bar and live image generation preview\n - Can use a separate neural network to produce previews with almost none VRAM or compute requirement\n- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image\n- Styles, a way to save part of prompt and easily apply them via dropdown later\n- Variations, a way to generate same image but with tiny differences\n- Seed resizing, a way to generate same image but at slightly different resolution\n- CLIP interrogator, a button that tries to guess prompt from an image\n- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway\n- Batch Processing, process a group of files using img2img\n- Img2img Alternative, reverse Euler method of cross attention control\n- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions\n- Reloading checkpoints on the fly\n- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one\n- Custom scripts with many extensions from community\n- Composable-Diffusion, a way to use multiple prompts at once\n - separate prompts using uppercase 'AND'\n - also supports weights for prompts: 'a cat :1.2 AND a dog AND a penguin :2.2'\n- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)\n- DeepDanbooru integration, creates danbooru style tags for anime prompts\n- xformers, major speed increase for select cards: (add '--xformers' to commandline args)\n- via extension: History tab: view, direct and delete images conveniently within the UI\n- Generate forever option\n- Training tab\n - hypernetworks and embeddings options\n - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)\n- Clip skip\n- Hypernetworks\n- Loras (same as Hypernetworks but more pretty)\n- A separate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt \n- Can select to load a different VAE from settings screen\n- Estimated completion time in progress bar\n- API\n- Support for dedicated inpainting model by RunwayML\n- via extension: Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embeds (implementation of URL\n- Stable Diffusion 2.0 support - see wiki for instructions\n- Alt-Diffusion support - see wiki for instructions\n- Now without any bad letters!\n- Load checkpoints in safetensors format\n- Eased resolution restriction: generated image's dimensions must be a multiple of 8 rather than 64\n- Now with a license!\n- Reorder elements in the UI from settings screen\n- Segmind Stable Diffusion support", "## Installation and Running\nMake sure the required dependencies are met and follow the instructions available for:\n- NVidia (recommended)\n- AMD GPUs.\n- Intel CPUs, Intel GPUs (both integrated and discrete) (external wiki page)\n\nAlternatively, use online services (like Google Colab):\n\n- List of Online Services", "### Installation on Windows 10/11 with NVidia-GPUs using release package\n1. Download 'URL' from v1.0.0-pre and extract its contents.\n2. Run 'URL'.\n3. Run 'URL'.\n> For more details see Install-and-Run-on-NVidia-GPUs", "### Automatic Installation on Windows\n1. Install Python 3.10.6 (Newer version of Python does not support torch), checking \"Add Python to PATH\".\n2. Install git.\n3. Download the stable-diffusion-webui repository, for example by running 'git clone URL\n4. Run 'URL' from Windows Explorer as normal, non-administrator, user.", "### Automatic Installation on Linux\n1. Install the dependencies:\n\n2. Navigate to the directory you would like the webui to be installed and execute the following command:\n\n3. Run 'URL'.\n4. Check 'URL' for options.", "### Installation on Apple Silicon\n\nFind the instructions here.", "## Contributing\nHere's how to add code to this repo: Contributing", "## Documentation\n\nThe documentation was moved from this README over to the project's wiki.\n\nFor the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) crawlable wiki.", "## Credits\nLicenses for borrowed code can be found in 'Settings -> Licenses' screen, and also in 'html/URL' file.\n\n- Stable Diffusion - URL URL\n- k-diffusion - URL\n- GFPGAN - URL\n- CodeFormer - URL\n- ESRGAN - URL\n- SwinIR - URL\n- Swin2SR - URL\n- LDSR - URL\n- MiDaS - URL\n- Ideas for optimizations - URL\n- Cross Attention layer optimization - Doggettx - URL original idea for prompt editing.\n- Cross Attention layer optimization - InvokeAI, lstein - URL (originally URL\n- Sub-quadratic Cross Attention layer optimization - Alex Birch (URL Amin Rezaei (URL\n- Textual Inversion - Rinon Gal - URL (we're not using his code, but we are using his ideas).\n- Idea for SD upscale - URL\n- Noise generation for outpainting mk2 - URL\n- CLIP interrogator idea and borrowing some code - URL\n- Idea for Composable Diffusion - URL\n- xformers - URL\n- DeepDanbooru - interrogator for anime diffusers URL\n- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (URL\n- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - URL\n- Security advice - RyotaK\n- UniPC sampler - Wenliang Zhao - URL\n- TAESD - Ollin Boer Bohan - URL\n- LyCORIS - KohakuBlueleaf\n- Restart sampling - lambertae - URL\n- Hypertile - tfernd - URL\n- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.\n- (You)" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of AA051611/A0110\n\n\n\nDataset automatically created during the evaluation run of model AA051611/A0110 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T07:22:47.294306(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 AA051611/A0110\n\n\n\nDataset automatically created during the evaluation run of model AA051611/A0110 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T07:22:47.294306(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 dfurman/GarrulusMarcoro-7B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model dfurman/GarrulusMarcoro-7B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T07:25:38.981116(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 dfurman/GarrulusMarcoro-7B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model dfurman/GarrulusMarcoro-7B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T07:25:38.981116(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[]

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

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

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of shitshow123/mistral7b_sft_dpo\n\n\n\nDataset automatically created during the evaluation run of model shitshow123/mistral7b_sft_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 2024-01-11T07:28:54.566656(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 shitshow123/mistral7b_sft_dpo\n\n\n\nDataset automatically created during the evaluation run of model shitshow123/mistral7b_sft_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 2024-01-11T07:28:54.566656(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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", "task_categories:question-answering", "size_categories:1K<n<10K", "language:en", "license:cc-by-nd-4.0", "arxiv:2402.00559", "region:us" ]

[ "2402.00559" ]

[ "en" ]

[ "# Reveal: A Benchmark for Verifiers of Reasoning Chains", "## Paper: A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains\n\nLink: URL \n\nAbstract: \nPrompting language models to provide step-by-step answers (e.g., \"Chain-of-Thought\") is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typically improve downstream task performance.\nRecent literature discusses automatic methods to verify reasoning steps to evaluate and improve their correctness. However, no fine-grained step-level datasets are available to enable thorough evaluation of such verification methods, hindering progress in this direction.\nWe introduce Reveal: *Reasoning Verification Evaluation*, a new dataset to benchmark automatic verifiers of complex Chain-of-Thought reasoning in open-domain question answering settings.\nReveal includes comprehensive labels for the relevance, attribution to evidence passages, and logical correctness of each reasoning step in a language model's answer, across a wide variety of datasets and state-of-the-art language models.", "### Usage\n\nTo load the dataset:\n\n\n\nNote: The above provides a table from 'eval/reveal_eval.csv' for easily working at scale with the data. There is another file 'eval/reveal_eval.json' with a more intuitive json structure, if you prefer this format.\n\nSome examples of how to handle the data by deriving step-level tasks:", "### This is an evaluation benchmark. It should not be included in training data for NLP models.\n\nPlease do not redistribute any part of the dataset without sufficient protection against web-crawlers.\n\nAn identifier 64-character string is added to each instance in the dataset to assist in future detection of contamination in web-crawl corporta. \n\nThe reveal dataset's string is: 'Reveal:Mn12GAs2I3S0eWjbTUFC0Y51ijGFB7rGBLnzGGhCQ7OtJPfVg7e6qt9zb5RPL36U'\n\nThe same has been done to the few-shot prompting demonstrations, to detect whether these demonstrations have been in a model's training data (if so, these demonstrations should not be used for few-shot evaluation of that model).\n\nThe few-shot demonstrations' string is: 'Reveal:HlyeWxw8BRcQ2dPGShTUUjn03uULZOyeNbzKzRIg4QihZ45k1lrye46OoUzi3kkW'", "#### Fields and Descriptions\n\n* dataset:\tSource dataset\n* question_id:\tID of the original instance from the source dataset\n* question:\tThe question text\n* answer_model:\tModel which generated the CoT answer\n* answer_id:\tID of a particular model's answer to a question (question_id + answer_model)\n* step_idx:\tStep index in the answer for this row\n* full_answer:\tFull CoT answer generated by the model\n* step:\tThe step from the full CoT answer which matches \"step_idx\", the subject of the row\n* decontextualized_step:\tThe decontextualized version of the step that we used for evidence retrieval (and for the NLI classification evaluations settings)\n* attribution_relevance_label:\tMajority label for the relevance annotations in the attribution task\n* attribution_relevance_majority:\tMax # of raters which agreed with each other for this rating\n* attribution_relevance_annotations:\tThe annotations for each rater (ordered list)\n* attribution_relevance_raters:\tThe raters (ordered list)\n* attribution_relevance_num_ratings:\tThe number of raters/ratings\n* evidence_id:\tThe evidence id (from 1 to 3) used for the annotation in this row\n* evidence:\tThe evidence used for the annotation in this row\n* attribution_label:\tThe majority label for whether the evidence supports the step\n* attribution_majority:\tMax # of raters which agreed with each other for this rating\n* attribution_annotations:\tThe annotations for each rater (ordered list)\n* attribution_raters:\tThe raters (ordered list)\n* attribution_num_ratings:\tThe number of raters/ratings\n* attribution_justifications:\tThe justifications of each rater (ordered list) - note that the raters gave one justification for every step, *not* for every evidence\n* annotated_in_attribution_batch:\tWhich batch this was annotated in (we had 5 annotation batches)\n* type_label:\tMajority label for whether the step is an attribution step, logical step or both\n* type_majority:\tMax # of raters which agreed with each other for this rating\n* type_annotations:\tThe annotations for each rater (ordered list)\n* type_raters:\tThe raters (ordered list)\n* type_num_ratings:\tThe number of raters/ratings\n* logic_relevance_label:\tMajority label for relevance annotations in the logic task\n* logic_relevance_majority:\tMax # of raters which agreed with each other for this rating\n* logic_relevance_annotations:\tThe annotations for each rater (ordered list)\n* logic_relevance_raters:\tThe raters (ordered list)\n* logic_relevance_num_ratings:\tThe number of raters/ratings\n* logic_justifications:\tJustifications of each rater (ordered list) - note that the raters gave one justification to all ratings of every step (i.e., one justification for the ratings of type + relevance + correctness together) \n* annotated_in_logic_batch:\tWhich batch this was annotated in (we had 5 annotation batches)\n* correctness_label:\tMajority label for whether the step is logically correct given the question + previous steps\n* correctness_majority:\tMax # of raters which agreed with each other for this rating\n* correctness_annotations:\tThe annotations for each rater (ordered list)\n* correctness_raters:\tThe raters (ordered list)\n* correctness_num_ratings:\tThe number of raters/ratings\n* agreement_majority_all_steps:\tMinimum agreement majority across the attribution and logic ratings for all steps\n* is_low_agreement_hard_case:\tagreement_majority_all_steps <= 2. This boolean indicates whether the annotations for this answer contain a step with non-trustworthy annotations. This is the difference between Reveal-Eval and Reveal-Open.\n* contamination_identifier:\tAn identification string for contamination detection.\n* is_final_rated_evidence_for_step: Whether this step-evidence pair is the final attribution rating for this step (we try 3 evidences, and stop when we find a supporting or contradicting evidence. The rating in this row is the final attribution rating for the ste pacross all evidence passages)\n* answer_is_fully_attributable: Whether all attribution steps in the answer are fully attributable to some evidence\n* answer_is_logically_correct: Whether all logic steps are logically correct\n* answer_is_fully_attributable_and_correct: Whether all steps are correct (fully attributable or logical)" ]

[ "TAGS\n#task_categories-text-classification #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-cc-by-nd-4.0 #arxiv-2402.00559 #region-us \n", "# Reveal: A Benchmark for Verifiers of Reasoning Chains", "## Paper: A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains\n\nLink: URL \n\nAbstract: \nPrompting language models to provide step-by-step answers (e.g., \"Chain-of-Thought\") is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typically improve downstream task performance.\nRecent literature discusses automatic methods to verify reasoning steps to evaluate and improve their correctness. However, no fine-grained step-level datasets are available to enable thorough evaluation of such verification methods, hindering progress in this direction.\nWe introduce Reveal: *Reasoning Verification Evaluation*, a new dataset to benchmark automatic verifiers of complex Chain-of-Thought reasoning in open-domain question answering settings.\nReveal includes comprehensive labels for the relevance, attribution to evidence passages, and logical correctness of each reasoning step in a language model's answer, across a wide variety of datasets and state-of-the-art language models.", "### Usage\n\nTo load the dataset:\n\n\n\nNote: The above provides a table from 'eval/reveal_eval.csv' for easily working at scale with the data. There is another file 'eval/reveal_eval.json' with a more intuitive json structure, if you prefer this format.\n\nSome examples of how to handle the data by deriving step-level tasks:", "### This is an evaluation benchmark. It should not be included in training data for NLP models.\n\nPlease do not redistribute any part of the dataset without sufficient protection against web-crawlers.\n\nAn identifier 64-character string is added to each instance in the dataset to assist in future detection of contamination in web-crawl corporta. \n\nThe reveal dataset's string is: 'Reveal:Mn12GAs2I3S0eWjbTUFC0Y51ijGFB7rGBLnzGGhCQ7OtJPfVg7e6qt9zb5RPL36U'\n\nThe same has been done to the few-shot prompting demonstrations, to detect whether these demonstrations have been in a model's training data (if so, these demonstrations should not be used for few-shot evaluation of that model).\n\nThe few-shot demonstrations' string is: 'Reveal:HlyeWxw8BRcQ2dPGShTUUjn03uULZOyeNbzKzRIg4QihZ45k1lrye46OoUzi3kkW'", "#### Fields and Descriptions\n\n* dataset:\tSource dataset\n* question_id:\tID of the original instance from the source dataset\n* question:\tThe question text\n* answer_model:\tModel which generated the CoT answer\n* answer_id:\tID of a particular model's answer to a question (question_id + answer_model)\n* step_idx:\tStep index in the answer for this row\n* full_answer:\tFull CoT answer generated by the model\n* step:\tThe step from the full CoT answer which matches \"step_idx\", the subject of the row\n* decontextualized_step:\tThe decontextualized version of the step that we used for evidence retrieval (and for the NLI classification evaluations settings)\n* attribution_relevance_label:\tMajority label for the relevance annotations in the attribution task\n* attribution_relevance_majority:\tMax # of raters which agreed with each other for this rating\n* attribution_relevance_annotations:\tThe annotations for each rater (ordered list)\n* attribution_relevance_raters:\tThe raters (ordered list)\n* attribution_relevance_num_ratings:\tThe number of raters/ratings\n* evidence_id:\tThe evidence id (from 1 to 3) used for the annotation in this row\n* evidence:\tThe evidence used for the annotation in this row\n* attribution_label:\tThe majority label for whether the evidence supports the step\n* attribution_majority:\tMax # of raters which agreed with each other for this rating\n* attribution_annotations:\tThe annotations for each rater (ordered list)\n* attribution_raters:\tThe raters (ordered list)\n* attribution_num_ratings:\tThe number of raters/ratings\n* attribution_justifications:\tThe justifications of each rater (ordered list) - note that the raters gave one justification for every step, *not* for every evidence\n* annotated_in_attribution_batch:\tWhich batch this was annotated in (we had 5 annotation batches)\n* type_label:\tMajority label for whether the step is an attribution step, logical step or both\n* type_majority:\tMax # of raters which agreed with each other for this rating\n* type_annotations:\tThe annotations for each rater (ordered list)\n* type_raters:\tThe raters (ordered list)\n* type_num_ratings:\tThe number of raters/ratings\n* logic_relevance_label:\tMajority label for relevance annotations in the logic task\n* logic_relevance_majority:\tMax # of raters which agreed with each other for this rating\n* logic_relevance_annotations:\tThe annotations for each rater (ordered list)\n* logic_relevance_raters:\tThe raters (ordered list)\n* logic_relevance_num_ratings:\tThe number of raters/ratings\n* logic_justifications:\tJustifications of each rater (ordered list) - note that the raters gave one justification to all ratings of every step (i.e., one justification for the ratings of type + relevance + correctness together) \n* annotated_in_logic_batch:\tWhich batch this was annotated in (we had 5 annotation batches)\n* correctness_label:\tMajority label for whether the step is logically correct given the question + previous steps\n* correctness_majority:\tMax # of raters which agreed with each other for this rating\n* correctness_annotations:\tThe annotations for each rater (ordered list)\n* correctness_raters:\tThe raters (ordered list)\n* correctness_num_ratings:\tThe number of raters/ratings\n* agreement_majority_all_steps:\tMinimum agreement majority across the attribution and logic ratings for all steps\n* is_low_agreement_hard_case:\tagreement_majority_all_steps <= 2. This boolean indicates whether the annotations for this answer contain a step with non-trustworthy annotations. This is the difference between Reveal-Eval and Reveal-Open.\n* contamination_identifier:\tAn identification string for contamination detection.\n* is_final_rated_evidence_for_step: Whether this step-evidence pair is the final attribution rating for this step (we try 3 evidences, and stop when we find a supporting or contradicting evidence. The rating in this row is the final attribution rating for the ste pacross all evidence passages)\n* answer_is_fully_attributable: Whether all attribution steps in the answer are fully attributable to some evidence\n* answer_is_logically_correct: Whether all logic steps are logically correct\n* answer_is_fully_attributable_and_correct: Whether all steps are correct (fully attributable or logical)" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"wiki-concept-gen-chatml\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"wiki-concept-gen-chatml\"\n\nMore Information needed" ]

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

[]

[ "en" ]

[ "# Generated Questions and Answers from the Falcon RefinedWeb Dataset\n\nThis dataset contains 1k open-domain questions and answers generated using documents from Falcon's refinedweb dataset using GPT-4. You can find more details about this work in the following blogpost.\n\nEach row consits of:\n\n- document_id - an id of a text chunk from the refined web dataset, from which the question was generated. Each id contains the original document index from the refinedweb dataset, and the chunk index in the following format: \"${REFINEDWEB_ID}_${CHUNK_INDEX}\"\n- document_text - the text of the chunk from which the question was generated.\n- generated_question - the generated question.\n- generated_answer - the corresponding generated answer." ]

[ "TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #language-English #license-mit #region-us \n", "# Generated Questions and Answers from the Falcon RefinedWeb Dataset\n\nThis dataset contains 1k open-domain questions and answers generated using documents from Falcon's refinedweb dataset using GPT-4. You can find more details about this work in the following blogpost.\n\nEach row consits of:\n\n- document_id - an id of a text chunk from the refined web dataset, from which the question was generated. Each id contains the original document index from the refinedweb dataset, and the chunk index in the following format: \"${REFINEDWEB_ID}_${CHUNK_INDEX}\"\n- document_text - the text of the chunk from which the question was generated.\n- generated_question - the generated question.\n- generated_answer - the corresponding generated answer." ]

[ "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 \"alpaca_cleaned_subset\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of rishiraj/oswald-7b\n\n\n\nDataset automatically created during the evaluation run of model rishiraj/oswald-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 2024-01-11T08:51:34.161186(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 rishiraj/oswald-7b\n\n\n\nDataset automatically created during the evaluation run of model rishiraj/oswald-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 2024-01-11T08:51:34.161186(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 kodonho/Solar-M-SakuraSolar-Mixed\n\n\n\nDataset automatically created during the evaluation run of model kodonho/Solar-M-SakuraSolar-Mixed on the Open LLM Leaderboard.\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 2024-01-11T09:30:23.797442(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 kodonho/Solar-M-SakuraSolar-Mixed\n\n\n\nDataset automatically created during the evaluation run of model kodonho/Solar-M-SakuraSolar-Mixed on the Open LLM Leaderboard.\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 2024-01-11T09:30:23.797442(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 decruz07/kellemar-DPO-7B-v1.01\n\n\n\nDataset automatically created during the evaluation run of model decruz07/kellemar-DPO-7B-v1.01 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T09:27:06.412050(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of decruz07/kellemar-DPO-7B-v1.01\n\n\n\nDataset automatically created during the evaluation run of model decruz07/kellemar-DPO-7B-v1.01 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T09:27:06.412050(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[]

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

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

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

[]

[]

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

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

[ "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" ]

[ "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" ]

[]

[]

[ "# PhishingURLDataset\n\nThis dataset is created for being used for neural network training, on phishing website detection.\n\nIt has been generated using this raw template.", "## Dataset Details\n\nThis dataset contains phishing websites, which are labeled with \"1\" and are called \"malignant\", and benign websites, which are labeled with \"0\".", "### Dataset Sources \n- Kaggle Dataset on Phishing URLs: URL\n- USOM Phishing Websites Dataset: URL\n- Phishtank Dataset: URL" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# PhishingURLDataset\n\nThis dataset is created for being used for neural network training, on phishing website detection.\n\nIt has been generated using this raw template.", "## Dataset Details\n\nThis dataset contains phishing websites, which are labeled with \"1\" and are called \"malignant\", and benign websites, which are labeled with \"0\".", "### Dataset Sources \n- Kaggle Dataset on Phishing URLs: URL\n- USOM Phishing Websites Dataset: URL\n- Phishtank Dataset: URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of andysalerno/openchat-nectar-0.1\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T09:55:59.577915(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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-0.1\n\n\n\nDataset automatically created during the evaluation run of model andysalerno/openchat-nectar-0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T09:55:59.577915(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "license:apache-2.0", "arxiv:2310.11440", "region:us" ]

[ "2310.11440" ]

[]

[ "# EvalCrafter Text-to-Video (ECTV) Dataset \n\nCode · Project Page · Huggingface Leaderboard · Paper@ArXiv · Prompt list\n\nWelcome to the ECTV dataset! This repository contains around 10000 videos generated by various methods using the Prompt list. These videos have been evaluated using the innovative EvalCrafter framework, which assesses generative models across visual, content, and motion qualities using 17 objective metrics and subjective user opinions.", "## Dataset Details \n\n- Paper: Read the Paper\n- Code: Code \n- Prompt List (700 prompts): Prompt list\n- Hugging Face Leaderboard: Huggingface Leaderboard\n- Project Page: Project Page\n- Methods Included in ECTV dataset:\n - VideoCrafter2\n - VideoCrafter1\n - VideoCrafter0.9 (Floor33)\n - Gen2-2023.12\n - Gen2-2023.09\n - PikaLab V1.0\n - PikaLab\n - Hotshot-XL\n - Show-1\n - Modelscope-XL\n - Zeroscope\n - Lavie\n - MoonValley\n- Dataset Structure:\n Generate videos are organized in the following structure (take videocrafter-v1.0 for an example)", "## Acknowledgements and Citation \n\nThis dataset is based on the EvalCrafter framework, which utilizes various open-source repositories for video generation evaluation. If you find this dataset helpful, please consider citing the original work:", "## Explore More About Video Generation:\n\n- VideoCrafter1: Open Diffusion Models for High-Quality Video Generation\n- VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models" ]

[ "TAGS\n#license-apache-2.0 #arxiv-2310.11440 #region-us \n", "# EvalCrafter Text-to-Video (ECTV) Dataset \n\nCode · Project Page · Huggingface Leaderboard · Paper@ArXiv · Prompt list\n\nWelcome to the ECTV dataset! This repository contains around 10000 videos generated by various methods using the Prompt list. These videos have been evaluated using the innovative EvalCrafter framework, which assesses generative models across visual, content, and motion qualities using 17 objective metrics and subjective user opinions.", "## Dataset Details \n\n- Paper: Read the Paper\n- Code: Code \n- Prompt List (700 prompts): Prompt list\n- Hugging Face Leaderboard: Huggingface Leaderboard\n- Project Page: Project Page\n- Methods Included in ECTV dataset:\n - VideoCrafter2\n - VideoCrafter1\n - VideoCrafter0.9 (Floor33)\n - Gen2-2023.12\n - Gen2-2023.09\n - PikaLab V1.0\n - PikaLab\n - Hotshot-XL\n - Show-1\n - Modelscope-XL\n - Zeroscope\n - Lavie\n - MoonValley\n- Dataset Structure:\n Generate videos are organized in the following structure (take videocrafter-v1.0 for an example)", "## Acknowledgements and Citation \n\nThis dataset is based on the EvalCrafter framework, which utilizes various open-source repositories for video generation evaluation. If you find this dataset helpful, please consider citing the original work:", "## Explore More About Video Generation:\n\n- VideoCrafter1: Open Diffusion Models for High-Quality Video Generation\n- VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of rishiraj/oswald-2x7b\n\n\n\nDataset automatically created during the evaluation run of model rishiraj/oswald-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 2024-01-11T10:06:31.070515(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 rishiraj/oswald-2x7b\n\n\n\nDataset automatically created during the evaluation run of model rishiraj/oswald-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 2024-01-11T10:06:31.070515(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of flemmingmiguel/Mistrality-7B\n\n\n\nDataset automatically created during the evaluation run of model flemmingmiguel/Mistrality-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 2024-01-11T10:13:19.328780(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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/Mistrality-7B\n\n\n\nDataset automatically created during the evaluation run of model flemmingmiguel/Mistrality-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 2024-01-11T10:13:19.328780(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[ "en", "ja" ]

[ "# Danbooru2023: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset\n\n\n\nDanbooru2023 is a large-scale anime image dataset with over 5 million images contributed and annotated in detail by an enthusiast community. Image tags cover aspects like characters, scenes, copyrights, artists, etc with an average of 30 tags per image.\n\nDanbooru is a veteran anime image board with high-quality images and extensive tag metadata. The dataset can be used to train image classification, multi-label tagging, character detection, generative models, and other computer vision tasks.\n\n- Shared by: Nyanko Devs\n- Language(s): English, Japanese\n- License: MIT\n\nThis dataset is built on the top of danbooru2021. We expands the dataset to include images up to ID #6,857,737, adding over 1.8 million additional images and total size is now approximately 8 terabytes (8,000 GB).", "## Use", "## Format\n\nThe goal of the dataset is to be as easy as possible to use immediately, avoiding obscure file formats, while allowing simultaneous research & seeding of the torrent, with easy updates.\n\nImages are provided in the full original form (be that JPG, PNG, GIF or otherwise) for reference/archival purposes, and bucketed into 1000 subdirectories 0000–0999 (0-padded), which is the Danbooru ID modulo 1000 (ie. all images in 0999/ have an ID ending in ‘999’); IDs can be turned into paths by dividing & padding (eg. in Bash, BUCKET=$(printf \"%04d\" $(( ID % 1000 )) )) and then the file is at {original,512px}/$BUCKET/$ID.$EXT. \n\nThe reason for the bucketing is that a single directory would cause pathological filesystem performance, and modulo ID is a simple hash which spreads images evenly without requiring additional future directories to be made or a filesystem IO to check where the file is. The ID is not zero-padded and files end in the relevant extension, hence the file layout looks like this:\n\n\n \nCurrently represented file extensions are: avi/bmp/gif/html/jpeg/jpg/mp3/mp4/mpg/pdf/png/rar/swf/webm/wmv/zip. \n\nRaw original files are treacherous. Be careful if working with the original dataset. There are many odd files: truncated, non-sRGB colorspace, wrong file extensions (eg. some PNGs have .jpg extensions like original/0146/URL or original/0558/URL), etc." ]

[ "TAGS\n#task_categories-image-classification #task_categories-image-to-image #task_categories-text-to-image #size_categories-1M<n<10M #language-English #language-Japanese #license-mit #region-us \n", "# Danbooru2023: A Large-Scale Crowdsourced and Tagged Anime Illustration Dataset\n\n\n\nDanbooru2023 is a large-scale anime image dataset with over 5 million images contributed and annotated in detail by an enthusiast community. Image tags cover aspects like characters, scenes, copyrights, artists, etc with an average of 30 tags per image.\n\nDanbooru is a veteran anime image board with high-quality images and extensive tag metadata. The dataset can be used to train image classification, multi-label tagging, character detection, generative models, and other computer vision tasks.\n\n- Shared by: Nyanko Devs\n- Language(s): English, Japanese\n- License: MIT\n\nThis dataset is built on the top of danbooru2021. We expands the dataset to include images up to ID #6,857,737, adding over 1.8 million additional images and total size is now approximately 8 terabytes (8,000 GB).", "## Use", "## Format\n\nThe goal of the dataset is to be as easy as possible to use immediately, avoiding obscure file formats, while allowing simultaneous research & seeding of the torrent, with easy updates.\n\nImages are provided in the full original form (be that JPG, PNG, GIF or otherwise) for reference/archival purposes, and bucketed into 1000 subdirectories 0000–0999 (0-padded), which is the Danbooru ID modulo 1000 (ie. all images in 0999/ have an ID ending in ‘999’); IDs can be turned into paths by dividing & padding (eg. in Bash, BUCKET=$(printf \"%04d\" $(( ID % 1000 )) )) and then the file is at {original,512px}/$BUCKET/$ID.$EXT. \n\nThe reason for the bucketing is that a single directory would cause pathological filesystem performance, and modulo ID is a simple hash which spreads images evenly without requiring additional future directories to be made or a filesystem IO to check where the file is. The ID is not zero-padded and files end in the relevant extension, hence the file layout looks like this:\n\n\n \nCurrently represented file extensions are: avi/bmp/gif/html/jpeg/jpg/mp3/mp4/mpg/pdf/png/rar/swf/webm/wmv/zip. \n\nRaw original files are treacherous. Be careful if working with the original dataset. There are many odd files: truncated, non-sRGB colorspace, wrong file extensions (eg. some PNGs have .jpg extensions like original/0146/URL or original/0558/URL), etc." ]

[ "task_categories:text-classification", "size_categories:100K<n<1M", "language:en", "arxiv:2312.04945", "region:us" ]

[ "2312.04945" ]

[ "en" ]

[ "# The ICL consistency test\n\nThis dataset provides data for the GenBench CBT task 'The ICL consistency test'.\nThe ICL consistency test measures the consistency of LLM predictions on the same data points across many different equivalent prompting setups. \nThe score in the associated metric (Cohen's kappa) can be understood as a measure of a model's prediction consistency in the face of task-irrelevant information.\n\nFor an easy evaluation of any models, we refer to the code provided in the GenBench task. For in-depth information on the task, we refer to the associated \npublications (Weber et al., 2023,2023) and the respective GenBench URL.\n\nEvaluation on the relevant metrics can be done via the _example_evaluation.py_ script in the GenBench repository.", "### Dataset Description\n\n_Abstract_: The ICL consistency test measures the consistency of LLM predictions on the same data points across many different prompting setups. Different setups are defined by \"factors\". \nOn the one hand, factors can be specific attributes of the used prompt (e.g. the number of examples the model is presented with [\"n_shots\"] or the type of instructions \nthat were used to wrap a specific datapoint [\"Instructions\"]). On the other hand, the analysis can also be augmented by factors that are related to the way a model is \nevaluated (e.g. whether a model is calibrated) or the type of model that is evaluated (e.g. the number of parameters or instructions tuning). These external factors can \nbe added to the analysis by using the task.add_factor() method. The output metric is Cohen's kappa for each factor across all different conditions. A kappa value close to \n1 indicates that the factors do not change the model prediction, while a factor close to 0 strongly changes model predictions. The ICL consistency test has two subtasks, \none evaluating the ANLI-dataset (Nie et al., 2019); the other the MNLI-dataset (Wang et al., 2017).\n\n_Size_: Each subtask contains 57600 when using the full 600 data_IDs. The user can choose to reduce the number of evaluated data_IDs.\n\n- Curated by:\n - resampling and arrangement was done by Weber et al., 2023,2023;\n - original data were curated by Nie et al., 2019 (ANLI) and Wang et al., 2017 (MNLI);\n - templates were curated by Bach et al., 2022 (promptsource). \n- Language: English", "### Dataset Sources (basic links)\n\n- Repository: Data files on github.\n- Paper: Weber et al., 2023,2023.\n- Demo: Find pre-implemented code to evaluate any model on github.", "## Uses\n\nIn prompting, models are sensitive to task-irrelevant information in their prompt. This test can be used to quantify this sensitivity of any model. The ICL consistency test does this by measuring a model's prediction consistency across many different semantically equivalent prompting setups.", "## Dataset Structure\n\n\n\n[_TBA_]", "## Dataset Creation\n\nThe data is a sample from the MNLI and ANLI datasets as well as prompt templates from promptsource. \nPlease refer to the original publications's documentation for detailed information on dataset creation.", "## Bias, Risks, and Limitations\n\nThis dataset contains data from the MNLI and ANLI datasets and adheres to the same biases, risks and limitations.", "### Recommendations\n\nWe identify the following limitations of the consistency test:\n\n1. The number of factors is limited and does not cover all possible factors that might influence the predictions. We limited ourselves to factors we deem relevant, to ensure fast evaluation.\n\n2. Currently, the test is only implemented for the ANLI- and MNLI-datasets.\n\n3. Factors that are external to the dataset but should be considered in the analysis (e.g. _instruction tuning_ or _calibration_) have to be manually added by the user\n using the task.add_factor() method (please use the GenBench implementation of the dataset. You can find it on github).\n\n\nThis dataset was used in the following publications. If you use it, please consider citing the following references:\n\nBibTeX:", "## Dataset Card Authors\nLucas Weber", "## Dataset Card Contact\nlucasweber000@URL" ]

[ "TAGS\n#task_categories-text-classification #size_categories-100K<n<1M #language-English #arxiv-2312.04945 #region-us \n", "# The ICL consistency test\n\nThis dataset provides data for the GenBench CBT task 'The ICL consistency test'.\nThe ICL consistency test measures the consistency of LLM predictions on the same data points across many different equivalent prompting setups. \nThe score in the associated metric (Cohen's kappa) can be understood as a measure of a model's prediction consistency in the face of task-irrelevant information.\n\nFor an easy evaluation of any models, we refer to the code provided in the GenBench task. For in-depth information on the task, we refer to the associated \npublications (Weber et al., 2023,2023) and the respective GenBench URL.\n\nEvaluation on the relevant metrics can be done via the _example_evaluation.py_ script in the GenBench repository.", "### Dataset Description\n\n_Abstract_: The ICL consistency test measures the consistency of LLM predictions on the same data points across many different prompting setups. Different setups are defined by \"factors\". \nOn the one hand, factors can be specific attributes of the used prompt (e.g. the number of examples the model is presented with [\"n_shots\"] or the type of instructions \nthat were used to wrap a specific datapoint [\"Instructions\"]). On the other hand, the analysis can also be augmented by factors that are related to the way a model is \nevaluated (e.g. whether a model is calibrated) or the type of model that is evaluated (e.g. the number of parameters or instructions tuning). These external factors can \nbe added to the analysis by using the task.add_factor() method. The output metric is Cohen's kappa for each factor across all different conditions. A kappa value close to \n1 indicates that the factors do not change the model prediction, while a factor close to 0 strongly changes model predictions. The ICL consistency test has two subtasks, \none evaluating the ANLI-dataset (Nie et al., 2019); the other the MNLI-dataset (Wang et al., 2017).\n\n_Size_: Each subtask contains 57600 when using the full 600 data_IDs. The user can choose to reduce the number of evaluated data_IDs.\n\n- Curated by:\n - resampling and arrangement was done by Weber et al., 2023,2023;\n - original data were curated by Nie et al., 2019 (ANLI) and Wang et al., 2017 (MNLI);\n - templates were curated by Bach et al., 2022 (promptsource). \n- Language: English", "### Dataset Sources (basic links)\n\n- Repository: Data files on github.\n- Paper: Weber et al., 2023,2023.\n- Demo: Find pre-implemented code to evaluate any model on github.", "## Uses\n\nIn prompting, models are sensitive to task-irrelevant information in their prompt. This test can be used to quantify this sensitivity of any model. The ICL consistency test does this by measuring a model's prediction consistency across many different semantically equivalent prompting setups.", "## Dataset Structure\n\n\n\n[_TBA_]", "## Dataset Creation\n\nThe data is a sample from the MNLI and ANLI datasets as well as prompt templates from promptsource. \nPlease refer to the original publications's documentation for detailed information on dataset creation.", "## Bias, Risks, and Limitations\n\nThis dataset contains data from the MNLI and ANLI datasets and adheres to the same biases, risks and limitations.", "### Recommendations\n\nWe identify the following limitations of the consistency test:\n\n1. The number of factors is limited and does not cover all possible factors that might influence the predictions. We limited ourselves to factors we deem relevant, to ensure fast evaluation.\n\n2. Currently, the test is only implemented for the ANLI- and MNLI-datasets.\n\n3. Factors that are external to the dataset but should be considered in the analysis (e.g. _instruction tuning_ or _calibration_) have to be manually added by the user\n using the task.add_factor() method (please use the GenBench implementation of the dataset. You can find it on github).\n\n\nThis dataset was used in the following publications. If you use it, please consider citing the following references:\n\nBibTeX:", "## Dataset Card Authors\nLucas Weber", "## Dataset Card Contact\nlucasweber000@URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of TriadParty/deepmoney-34b-200k-base\n\n\n\nDataset automatically created during the evaluation run of model TriadParty/deepmoney-34b-200k-base on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2024-01-11T10:43:46.493782(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 TriadParty/deepmoney-34b-200k-base\n\n\n\nDataset automatically created during the evaluation run of model TriadParty/deepmoney-34b-200k-base on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2024-01-11T10:43:46.493782(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of elyza/ELYZA-japanese-Llama-2-13b\n\n\n\nDataset automatically created during the evaluation run of model elyza/ELYZA-japanese-Llama-2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T10:41:02.559560(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 elyza/ELYZA-japanese-Llama-2-13b\n\n\n\nDataset automatically created during the evaluation run of model elyza/ELYZA-japanese-Llama-2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T10:41:02.559560(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[]

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

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of elyza/ELYZA-japanese-Llama-2-13b-instruct\n\n\n\nDataset automatically created during the evaluation run of model elyza/ELYZA-japanese-Llama-2-13b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T10:45:22.609488(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 elyza/ELYZA-japanese-Llama-2-13b-instruct\n\n\n\nDataset automatically created during the evaluation run of model elyza/ELYZA-japanese-Llama-2-13b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T10:45:22.609488(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 elyza/ELYZA-japanese-Llama-2-13b-fast\n\n\n\nDataset automatically created during the evaluation run of model elyza/ELYZA-japanese-Llama-2-13b-fast on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T10:56:54.496975(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 elyza/ELYZA-japanese-Llama-2-13b-fast\n\n\n\nDataset automatically created during the evaluation run of model elyza/ELYZA-japanese-Llama-2-13b-fast on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T10:56:54.496975(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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", "size_categories:1M<n<10M", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# CLIP and T5 tokenization of CC3M" ]

[ "TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-English #license-mit #region-us \n", "# CLIP and T5 tokenization of CC3M" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"arxiv-2048\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"arxiv-2048\"\n\nMore Information needed" ]

[ "task_categories:question-answering", "language:ar", "license:apache-2.0", "not-for-all-audiences", "region:us" ]

[]

[ "ar" ]

[]

[ "TAGS\n#task_categories-question-answering #language-Arabic #license-apache-2.0 #not-for-all-audiences #region-us \n" ]

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of decruz07/kellemar-DPO-7B-c\n\n\n\nDataset automatically created during the evaluation run of model decruz07/kellemar-DPO-7B-c on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T11:35:01.176663(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of decruz07/kellemar-DPO-7B-c\n\n\n\nDataset automatically created during the evaluation run of model decruz07/kellemar-DPO-7B-c on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T11:35:01.176663(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 decruz07/kellemar-DPO-7B-d\n\n\n\nDataset automatically created during the evaluation run of model decruz07/kellemar-DPO-7B-d on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T11:40:20.341969(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of decruz07/kellemar-DPO-7B-d\n\n\n\nDataset automatically created during the evaluation run of model decruz07/kellemar-DPO-7B-d on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T11:40:20.341969(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"pubmed-2048\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"pubmed-2048\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of proto-llm/uniwiz-7B-v0.2\n\n\n\nDataset automatically created during the evaluation run of model proto-llm/uniwiz-7B-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T11:59:37.867165(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 proto-llm/uniwiz-7B-v0.2\n\n\n\nDataset automatically created during the evaluation run of model proto-llm/uniwiz-7B-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T11:59:37.867165(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 tuantran1632001/Psyfighter2-Orca2-ties\n\n\n\nDataset automatically created during the evaluation run of model tuantran1632001/Psyfighter2-Orca2-ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T12:03:20.679754(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 tuantran1632001/Psyfighter2-Orca2-ties\n\n\n\nDataset automatically created during the evaluation run of model tuantran1632001/Psyfighter2-Orca2-ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T12:03:20.679754(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[]

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

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

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

[]

[]

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

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

[ "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:cc-by-4.0", "arxiv:2402.01622", "region:us" ]

[ "2402.01622" ]

[]

[ "# TravelPlanner Dataset\n\nTravelPlanner is a benchmark crafted for evaluating language agents in tool-use and complex planning within multiple constraints. (See our paper for more details.)", "## Introduction\n\nIn TravelPlanner, for a given query, language agents are expected to formulate a comprehensive plan that includes transportation, daily meals, attractions, and accommodation for each day.\n\nTravelPlanner comprises 1,225 queries in total. The number of days and hard constraints are designed to test agents' abilities across both the breadth and depth of complex planning.", "## Split\n\n<b>Train Set</b>: 5 queries with corresponding human-annotated plans for group, resulting in a total of 45 query-plan pairs. This set provides the human annotated plans as demonstrations for in-context learning.\n\n<b>Validation Set</b>: 20 queries from each group, amounting to 180 queries in total. There is no human annotated plan in this set.\n\n<b>Test Set</b>: 1,000 randomly distributed queries. To avoid data contamination, we only provide the level, days, and natural language query fields.", "## Record Layout\n\n- \"org\": The city from where the journey begins.\n- \"dest\": The destination city.\n- \"days\": The number of days planned for the trip.\n- \"visiting_city_number\": The total number of cities included in the itinerary.\n- \"date\": The specific date when the travel is scheduled.\n- \"people_numbe\": The total number of people involved in the travel.\n- \"local_constraint\": The local hard constraint, including house rule, cuisine, room type and transportation.\n- \"query\": A natural language description or request related to the travel plan.\n- \"level\": The difficulty level, which is determined by the number of hard constraints.\n- \"annotated_plan\": A detailed travel plan annotated by a human, ensuring compliance with all common sense requirements and specific hard constraints.\n- \"reference_information\": Reference information for \"sole-planning\" mode.\n\nIf our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries." ]

[ "TAGS\n#license-cc-by-4.0 #arxiv-2402.01622 #region-us \n", "# TravelPlanner Dataset\n\nTravelPlanner is a benchmark crafted for evaluating language agents in tool-use and complex planning within multiple constraints. (See our paper for more details.)", "## Introduction\n\nIn TravelPlanner, for a given query, language agents are expected to formulate a comprehensive plan that includes transportation, daily meals, attractions, and accommodation for each day.\n\nTravelPlanner comprises 1,225 queries in total. The number of days and hard constraints are designed to test agents' abilities across both the breadth and depth of complex planning.", "## Split\n\n<b>Train Set</b>: 5 queries with corresponding human-annotated plans for group, resulting in a total of 45 query-plan pairs. This set provides the human annotated plans as demonstrations for in-context learning.\n\n<b>Validation Set</b>: 20 queries from each group, amounting to 180 queries in total. There is no human annotated plan in this set.\n\n<b>Test Set</b>: 1,000 randomly distributed queries. To avoid data contamination, we only provide the level, days, and natural language query fields.", "## Record Layout\n\n- \"org\": The city from where the journey begins.\n- \"dest\": The destination city.\n- \"days\": The number of days planned for the trip.\n- \"visiting_city_number\": The total number of cities included in the itinerary.\n- \"date\": The specific date when the travel is scheduled.\n- \"people_numbe\": The total number of people involved in the travel.\n- \"local_constraint\": The local hard constraint, including house rule, cuisine, room type and transportation.\n- \"query\": A natural language description or request related to the travel plan.\n- \"level\": The difficulty level, which is determined by the number of hard constraints.\n- \"annotated_plan\": A detailed travel plan annotated by a human, ensuring compliance with all common sense requirements and specific hard constraints.\n- \"reference_information\": Reference information for \"sole-planning\" mode.\n\nIf our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries." ]

[ "region:us" ]

[]

[]

[ "# Medical-Calgary-Cambridge-single-turn-llama2-300 \n296 single-turn conversational entries between a patient and doctor, using the Calgary-Cambridge model" ]

[ "TAGS\n#region-us \n", "# Medical-Calgary-Cambridge-single-turn-llama2-300 \n296 single-turn conversational entries between a patient and doctor, using the Calgary-Cambridge model" ]

[ "task_categories:translation", "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "language:de", "language:fr", "language:zh", "language:pt", "language:nl", "language:ru", "language:ko", "language:it", "language:es", "region:us" ]

[]

[ "en", "de", "fr", "zh", "pt", "nl", "ru", "ko", "it", "es" ]

[]

[ "TAGS\n#task_categories-translation #task_categories-text-generation #size_categories-10K<n<100K #language-English #language-German #language-French #language-Chinese #language-Portuguese #language-Dutch #language-Russian #language-Korean #language-Italian #language-Spanish #region-us \n" ]

[ "task_categories:question-answering", "size_categories:10K<n<100K", "Math", "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: Zui Chen & Yezeng Chen \n- Language(s) (NLP): Chinese & English & Code \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: GSM8K & Math & TAL-SCQ", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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#task_categories-question-answering #size_categories-10K<n<100K #Math #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: Zui Chen & Yezeng Chen \n- Language(s) (NLP): Chinese & English & Code \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: GSM8K & Math & TAL-SCQ", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[]

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

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

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

[]

[ "en" ]

[ "# Gutenberg DPO\n\n!gutenberg", "## Overview\n\nThis is a dataset meant to enhance novel writing capabilities of LLMs, by using public domain books from Project Gutenberg", "## Process\n\nFirst, the each book is parsed, split into chapters, cleaned up from the original format (remove superfluous newlines, illustration tags, etc.).\n\nOnce we have chapters, an LLM is prompted with each chapter to create a synthetic prompt that would result in that chapter being written.\nEach chapter has a summary created as well, so that the prompts for each chapter after the also include a summary of the previous chapter to provide additional context.\n\nWe then use the synthetic prompt with previous chapter summary to write the chapter with an LLM (llama-2-13b-chat, bagel-7b-v0.1, dolphin-2.2-34b).\nThe human written text, that is, the original chapter, is used as the \"chosen\" value, and the LLM written chapter is used as the rejected value.", "## Books used\n\nThese books were chosen main because they appeared in the popular section on project gutenberg, and they function correctly with the chapterize library.\n\n- Huckleberry Finn\n- Treasure Island\n- Anna Karenina\n- Uncle Tom’s Cabin\n- Wuthering Heights\n- Madame Bovary\n- The Turn of the Screw\n- The War of the Worlds\n- A Study in Scarlet\n- Middlemarch\n- Pride and Prejudice\n- The Brothers Karamazov\n- Through the Looking Glass\n- Moby Dick\n- Frankenstein\n- A Tale of Two Cities" ]

[ "TAGS\n#size_categories-n<1K #language-English #license-cc-by-4.0 #dpo #region-us \n", "# Gutenberg DPO\n\n!gutenberg", "## Overview\n\nThis is a dataset meant to enhance novel writing capabilities of LLMs, by using public domain books from Project Gutenberg", "## Process\n\nFirst, the each book is parsed, split into chapters, cleaned up from the original format (remove superfluous newlines, illustration tags, etc.).\n\nOnce we have chapters, an LLM is prompted with each chapter to create a synthetic prompt that would result in that chapter being written.\nEach chapter has a summary created as well, so that the prompts for each chapter after the also include a summary of the previous chapter to provide additional context.\n\nWe then use the synthetic prompt with previous chapter summary to write the chapter with an LLM (llama-2-13b-chat, bagel-7b-v0.1, dolphin-2.2-34b).\nThe human written text, that is, the original chapter, is used as the \"chosen\" value, and the LLM written chapter is used as the rejected value.", "## Books used\n\nThese books were chosen main because they appeared in the popular section on project gutenberg, and they function correctly with the chapterize library.\n\n- Huckleberry Finn\n- Treasure Island\n- Anna Karenina\n- Uncle Tom’s Cabin\n- Wuthering Heights\n- Madame Bovary\n- The Turn of the Screw\n- The War of the Worlds\n- A Study in Scarlet\n- Middlemarch\n- Pride and Prejudice\n- The Brothers Karamazov\n- Through the Looking Glass\n- Moby Dick\n- Frankenstein\n- A Tale of Two Cities" ]

[ "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" ]

[ "task_categories:text-generation", "task_categories:reinforcement-learning", "task_categories:conversational", "size_categories:1K<n<10K", "license:cc-by-4.0", "dpo", "poetry", "synthetic", "arxiv:2212.10560", "arxiv:2110.00482", "arxiv:2305.18290", "region:us" ]

[ "2212.10560", "2110.00482", "2305.18290" ]

[]

[ "# Dataset Card for Haiku DPO\n<img src=\"URL alt=\"Built with Distilabel\" width=\"200\" height=\"32\"/>\n\n\nThis a synthetic dataset of haikus. The dataset is constructed with the goal of helping to train LLMs to be more 'technically' competent at writing haikus.", "## Dataset Details\n\n The data consists of a few different components that are described in more detail below but the key components are:\n- a column of synthetically generated user prompts requesting a haiku\n- a column consisting of multiple responses to this prompt, generated by a language model\n- a column consisting of scores for each of these responses, generated by a rule-based system\n\nThe goal of this dataset was to help the author explore the process of synthesizing a dataset for DPO and to explore the extent to which DPO can be used to capture aesthetic preferences in language generation. \n\nHaiku also has the nice property of being relatively easy to score on a 'technical basis' i.e. do they follow the 5-7-5 syllable structure? As a result of this property, some relatively simple Python functions can be used to rate the technical quality of a haiku. \n\nBy focusing on a narrower task, this dataset also intends to offer a place to explore questions such as:\n- should DPO datasets prioritize a large gap in scores between the 'best' and 'worst' generations?\n- Is more data better or is a bigger gap in scores better?\n\nI am also interested in exploring the extent to which smaller models can learn to perform well at a narrower task. Again, haiku writing here is a good candidate for this exploration as it is relatively narrow, the data is cheaper to generate and it is relatively easy to score on a technical basis so you don't need to rely on human annotation or a \"judge\" LM to score the generations.", "### Dataset Description\n\n- Curated by: Daniel van Strien\n- Language(s) (NLP): English (synthetically generated)\n- License: Creative Commons Attribution 4.0 International License", "## Uses\n\nThis dataset can be used \"as is\" to help train LLMs to be more 'technically' competent at writing haikus. However, it is also intended as a \"test bed\" for exploring how different DPO qualities of a DPO dataset impact models trained on these datasets.", "### Direct Use\n\nThe 'default' config can be used for training DPO models. The \"chosen\" and \"rejected\" columns contain the highest-quality and lowest-quality generations respectively. You may, however, want to filter the dataset in other ways to explore how different qualities of a DPO dataset impact the resulting model.", "### Out-of-Scope Use\n\nThis dataset was constructed with a rather narrow goal in mind. It is unlikely to be useful for other tasks. However, it may be useful as a test bed for exploring how different qualities of a DPO dataset impact the resulting model.", "## Dataset Structure\n\nThe dataset consists of a few different configurations:\n\n- 'default': this is likely to be the most useful one for most users. It contains the highest-quality and lowest-quality generations in the \"chosen\" and \"rejected\" columns respectively. It also contains the \"difference_in_score\" column which is the difference between the score of the highest-quality generation and the lowest-quality generation. This column can be used to filter the dataset to explore how different qualities of a DPO dataset impact the resulting model.\n\nThe 'default' configuration has the following columns:\n- 'question': the prompt requesting a haiku\n- 'generation_model': the name of the model used to generate the haiku\n- 'generation_prompt': the full prompt used to generate the haiku\n- 'generations': the haikus generated by the model\n- 'scores': the scores for each of the haikus\n- 'chosen': the highest-quality haiku\n- 'chosen_score': the score for the highest-quality haiku\n- 'rejected': the lowest-quality haiku\n- 'rejected_score': the score for the lowest-quality haiku\n- 'tie': whether the highest-quality and lowest-quality haikus have the same score\n- 'difference_in_score': the difference between the score of the highest-quality generation and the lowest-quality generation\n- 'system': the system prompt used during generation\n\nThe 'default' configuration removes ties and ensures the lowest quality generation has a score < below 3. More information on the scoring process is outlined below.\n\nThe 'rule_ranked' configuration is similar to the 'default' configuration but it has not been filtered at all so will give you more scope for things like including ties in your dataset.", "## Dataset Creation\n\nThis dataset was generated using the distilabel library using teknium's OpenHermes-2.5-Mistral-7B model. The prompts were generated from a seed list of terms and an adapted version of the SELF-INSTRUCT papers prompting strategy. You can see more details about the process of generating these prompts in the associated dataset davanstrien/haiku_prompts. \n\nFrom these initial prompts, multiple generations of haiku were generated (again using teknium's OpenHermes-2.5-Mistral-7B model). These generations were then scored using a rule-based system. This rule system scored haikus out of a 4, with the following approach to scoring:\n\nIf the haiku is not three lines it scores zero. Then for each line, 1 point is deducted if the line does not match the expected syllable count for that line. This means a haiku with three lines matching the traditional 5-7-5 syllable structure will score 4. A haiku with one line with an incorrect syllable count will score 3. \n\nThe rule-based system is not perfect and there are some cases where it will incorrectly score a haiku. However, it is relatively easy to understand and it is relatively easy to score a haiku manually so it is a good candidate for a rule-based system. The code for this is shared\nin this GitHub repository.", "### Curation Rationale\n\nThe dataset was curated with the following goals in mind:\n- to explore the process of using open models to generate synthetic datasets\n- to explore the use of rules for ranking generations\n- to explore how different slices of a DPO dataset impact the resulting model", "### Source Data", "#### Data Collection and Processing\n\nSee above for the process of generating the data.", "#### Who are the source data producers?\n\nAlmost all of the data is synthetic. The prompts were generated using a seed list of terms and an adapted version of the SELF-INSTRUCT papers prompting strategy. The generations were generated using teknium's OpenHermes-2.5-Mistral-7B model. The scores were generated using a rule-based system. The initial prompt seed terms were generated by Daniel van Strien with some help from GPT-4.", "### Annotations \n\nThere are no traditional annotations in this dataset. However, the scores are generated using a rule-based system.", "#### Personal and Sensitive Information\n\nIt is very unlikely that this dataset contains any personal or sensitive information, but if you find any prompts that you believe to be harmful, please open a discussion and I will remove them from the dataset.", "## Bias, Risks, and Limitations\n\n\n\nWhilst I have not found any harmful prompts in the dataset, I have not manually validated all of the prompts. If you find any prompts which you believe to be harmful, please open a discussion and I will remove them from the dataset.", "### Recommendations\n\n\n\nThe original seed prompts used to generate this dataset are by no means comprehensive, and the dataset is likely to be biased toward the topics covered by the seed prompts. This dataset will likely develop over time. If you have any suggestions for additional seed prompts, please open a discussion and I will add them to the dataset.\n\n[optional]\n\nI have zero expectation that this dataset will be cited, but if you do use it in your work, you can cite it as follows:\n\nBibTeX:", "## Glossary \n\n\n\n- DPO/Direct Preference Optimization: Introduced in *Direct Preference Optimization: Your Language Model is Secretly a Reward Model*\n- SELF-INSTRUCT: A prompting strategy introduced in *Self-Instruct: Aligning Language Model with Self Generated Instructions*", "## Dataset Card Authors\n\ndavanstrien", "## Dataset Card Contact\n\ndavanstrien" ]

[ "TAGS\n#task_categories-text-generation #task_categories-reinforcement-learning #task_categories-conversational #size_categories-1K<n<10K #license-cc-by-4.0 #dpo #poetry #synthetic #arxiv-2212.10560 #arxiv-2110.00482 #arxiv-2305.18290 #region-us \n", "# Dataset Card for Haiku DPO\n<img src=\"URL alt=\"Built with Distilabel\" width=\"200\" height=\"32\"/>\n\n\nThis a synthetic dataset of haikus. The dataset is constructed with the goal of helping to train LLMs to be more 'technically' competent at writing haikus.", "## Dataset Details\n\n The data consists of a few different components that are described in more detail below but the key components are:\n- a column of synthetically generated user prompts requesting a haiku\n- a column consisting of multiple responses to this prompt, generated by a language model\n- a column consisting of scores for each of these responses, generated by a rule-based system\n\nThe goal of this dataset was to help the author explore the process of synthesizing a dataset for DPO and to explore the extent to which DPO can be used to capture aesthetic preferences in language generation. \n\nHaiku also has the nice property of being relatively easy to score on a 'technical basis' i.e. do they follow the 5-7-5 syllable structure? As a result of this property, some relatively simple Python functions can be used to rate the technical quality of a haiku. \n\nBy focusing on a narrower task, this dataset also intends to offer a place to explore questions such as:\n- should DPO datasets prioritize a large gap in scores between the 'best' and 'worst' generations?\n- Is more data better or is a bigger gap in scores better?\n\nI am also interested in exploring the extent to which smaller models can learn to perform well at a narrower task. Again, haiku writing here is a good candidate for this exploration as it is relatively narrow, the data is cheaper to generate and it is relatively easy to score on a technical basis so you don't need to rely on human annotation or a \"judge\" LM to score the generations.", "### Dataset Description\n\n- Curated by: Daniel van Strien\n- Language(s) (NLP): English (synthetically generated)\n- License: Creative Commons Attribution 4.0 International License", "## Uses\n\nThis dataset can be used \"as is\" to help train LLMs to be more 'technically' competent at writing haikus. However, it is also intended as a \"test bed\" for exploring how different DPO qualities of a DPO dataset impact models trained on these datasets.", "### Direct Use\n\nThe 'default' config can be used for training DPO models. The \"chosen\" and \"rejected\" columns contain the highest-quality and lowest-quality generations respectively. You may, however, want to filter the dataset in other ways to explore how different qualities of a DPO dataset impact the resulting model.", "### Out-of-Scope Use\n\nThis dataset was constructed with a rather narrow goal in mind. It is unlikely to be useful for other tasks. However, it may be useful as a test bed for exploring how different qualities of a DPO dataset impact the resulting model.", "## Dataset Structure\n\nThe dataset consists of a few different configurations:\n\n- 'default': this is likely to be the most useful one for most users. It contains the highest-quality and lowest-quality generations in the \"chosen\" and \"rejected\" columns respectively. It also contains the \"difference_in_score\" column which is the difference between the score of the highest-quality generation and the lowest-quality generation. This column can be used to filter the dataset to explore how different qualities of a DPO dataset impact the resulting model.\n\nThe 'default' configuration has the following columns:\n- 'question': the prompt requesting a haiku\n- 'generation_model': the name of the model used to generate the haiku\n- 'generation_prompt': the full prompt used to generate the haiku\n- 'generations': the haikus generated by the model\n- 'scores': the scores for each of the haikus\n- 'chosen': the highest-quality haiku\n- 'chosen_score': the score for the highest-quality haiku\n- 'rejected': the lowest-quality haiku\n- 'rejected_score': the score for the lowest-quality haiku\n- 'tie': whether the highest-quality and lowest-quality haikus have the same score\n- 'difference_in_score': the difference between the score of the highest-quality generation and the lowest-quality generation\n- 'system': the system prompt used during generation\n\nThe 'default' configuration removes ties and ensures the lowest quality generation has a score < below 3. More information on the scoring process is outlined below.\n\nThe 'rule_ranked' configuration is similar to the 'default' configuration but it has not been filtered at all so will give you more scope for things like including ties in your dataset.", "## Dataset Creation\n\nThis dataset was generated using the distilabel library using teknium's OpenHermes-2.5-Mistral-7B model. The prompts were generated from a seed list of terms and an adapted version of the SELF-INSTRUCT papers prompting strategy. You can see more details about the process of generating these prompts in the associated dataset davanstrien/haiku_prompts. \n\nFrom these initial prompts, multiple generations of haiku were generated (again using teknium's OpenHermes-2.5-Mistral-7B model). These generations were then scored using a rule-based system. This rule system scored haikus out of a 4, with the following approach to scoring:\n\nIf the haiku is not three lines it scores zero. Then for each line, 1 point is deducted if the line does not match the expected syllable count for that line. This means a haiku with three lines matching the traditional 5-7-5 syllable structure will score 4. A haiku with one line with an incorrect syllable count will score 3. \n\nThe rule-based system is not perfect and there are some cases where it will incorrectly score a haiku. However, it is relatively easy to understand and it is relatively easy to score a haiku manually so it is a good candidate for a rule-based system. The code for this is shared\nin this GitHub repository.", "### Curation Rationale\n\nThe dataset was curated with the following goals in mind:\n- to explore the process of using open models to generate synthetic datasets\n- to explore the use of rules for ranking generations\n- to explore how different slices of a DPO dataset impact the resulting model", "### Source Data", "#### Data Collection and Processing\n\nSee above for the process of generating the data.", "#### Who are the source data producers?\n\nAlmost all of the data is synthetic. The prompts were generated using a seed list of terms and an adapted version of the SELF-INSTRUCT papers prompting strategy. The generations were generated using teknium's OpenHermes-2.5-Mistral-7B model. The scores were generated using a rule-based system. The initial prompt seed terms were generated by Daniel van Strien with some help from GPT-4.", "### Annotations \n\nThere are no traditional annotations in this dataset. However, the scores are generated using a rule-based system.", "#### Personal and Sensitive Information\n\nIt is very unlikely that this dataset contains any personal or sensitive information, but if you find any prompts that you believe to be harmful, please open a discussion and I will remove them from the dataset.", "## Bias, Risks, and Limitations\n\n\n\nWhilst I have not found any harmful prompts in the dataset, I have not manually validated all of the prompts. If you find any prompts which you believe to be harmful, please open a discussion and I will remove them from the dataset.", "### Recommendations\n\n\n\nThe original seed prompts used to generate this dataset are by no means comprehensive, and the dataset is likely to be biased toward the topics covered by the seed prompts. This dataset will likely develop over time. If you have any suggestions for additional seed prompts, please open a discussion and I will add them to the dataset.\n\n[optional]\n\nI have zero expectation that this dataset will be cited, but if you do use it in your work, you can cite it as follows:\n\nBibTeX:", "## Glossary \n\n\n\n- DPO/Direct Preference Optimization: Introduced in *Direct Preference Optimization: Your Language Model is Secretly a Reward Model*\n- SELF-INSTRUCT: A prompting strategy introduced in *Self-Instruct: Aligning Language Model with Self Generated Instructions*", "## Dataset Card Authors\n\ndavanstrien", "## Dataset Card Contact\n\ndavanstrien" ]

[ "region:us" ]

[]

[]

[ "# Dataset for SemEval-2024 Task 3\n\nThe dataset for SemEval-2024 Task 3: The Competition of Multimodal Emotion Cause Analysis in Conversations is released here.", "## File Description" ]

[ "TAGS\n#region-us \n", "# Dataset for SemEval-2024 Task 3\n\nThe dataset for SemEval-2024 Task 3: The Competition of Multimodal Emotion Cause Analysis in Conversations is released here.", "## File Description" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of NLPinas/yi-bagel-2x34b-moe\n\n\n\nDataset automatically created during the evaluation run of model NLPinas/yi-bagel-2x34b-moe on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T13:51:44.686657(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 NLPinas/yi-bagel-2x34b-moe\n\n\n\nDataset automatically created during the evaluation run of model NLPinas/yi-bagel-2x34b-moe on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T13:51:44.686657(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:conversational", "task_categories:text-generation", "size_categories:10K<n<100K", "language:pt", "language:en", "license:apache-2.0", "alignment", "instruction", "chat", "region:us" ]

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[ "pt", "en" ]

[ "# Instruct-Aira Dataset version 3.0", "## Table of Contents\n\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Repository: URL\n- Point of Contact: AIRES at PUCRS", "### Dataset Summary\n\nThis dataset contains a collection of multi-turn conversations between an assistant and a user. Conversations were generated by user interactions with already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc). The dataset is available in Portuguese and English.", "### Supported Tasks and Leaderboards\n\nThis dataset can be utilized for various natural language processing tasks, including but not limited to:\n\n- Language modeling.\n- Question-answering systems.\n- Chatbot development.\n- Evaluation of language models.\n- Alignment research.", "### Languages\n\nEnglish and Portuguese.", "## Dataset Structure", "### Data Instances\n\nThe dataset consists of the following features:\n\n- Conversation ID: Identifier of the conversation.\n- Conversations: A list of dictionaries following a chat format.", "### Data Fields", "### Data Splits\n\nAvailable splits are 'english' and 'portuguese'.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was developed are part of Nicholas Kluge's doctoral dissertation, \"_Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment._\" This research was funded by CNPq (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), FAPERGS (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), and DAAD (Deutscher Akademischer Austauschdienst), as part of a doctoral research project tied to Philosophy departments of PUCRS (Pontifícia Universidade Católica do Rio Grande do Sul) and the University of Bonn.", "### Source Data", "#### Initial Data Collection and Normalization\n\nAll completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.", "#### Who are the source language producers?\n\nAll completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.", "### Annotations", "#### Annotation process\n\nAll completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.", "#### Who are the annotators?\n\nNo annotators were used.", "### Personal and Sensitive Information\n\nNo personal or sensitive information is part of this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nNo considerations.", "### Discussion of Biases\n\nNo considerations.", "### Other Known Limitations\n\nNo considerations.", "## Additional Information", "### Dataset Curators\n\nNicholas Kluge Corrêa.", "### Licensing Information\n\nThis dataset is licensed under the Apache License, version 2.0.", "### Contributions\n\nIf you would like to contribute, contact me at nicholas@URL!" ]

[ "TAGS\n#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-Portuguese #language-English #license-apache-2.0 #alignment #instruction #chat #region-us \n", "# Instruct-Aira Dataset version 3.0", "## Table of Contents\n\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Repository: URL\n- Point of Contact: AIRES at PUCRS", "### Dataset Summary\n\nThis dataset contains a collection of multi-turn conversations between an assistant and a user. Conversations were generated by user interactions with already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc). The dataset is available in Portuguese and English.", "### Supported Tasks and Leaderboards\n\nThis dataset can be utilized for various natural language processing tasks, including but not limited to:\n\n- Language modeling.\n- Question-answering systems.\n- Chatbot development.\n- Evaluation of language models.\n- Alignment research.", "### Languages\n\nEnglish and Portuguese.", "## Dataset Structure", "### Data Instances\n\nThe dataset consists of the following features:\n\n- Conversation ID: Identifier of the conversation.\n- Conversations: A list of dictionaries following a chat format.", "### Data Fields", "### Data Splits\n\nAvailable splits are 'english' and 'portuguese'.", "## Dataset Creation", "### Curation Rationale\n\nThis dataset was developed are part of Nicholas Kluge's doctoral dissertation, \"_Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment._\" This research was funded by CNPq (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), FAPERGS (Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul), and DAAD (Deutscher Akademischer Austauschdienst), as part of a doctoral research project tied to Philosophy departments of PUCRS (Pontifícia Universidade Católica do Rio Grande do Sul) and the University of Bonn.", "### Source Data", "#### Initial Data Collection and Normalization\n\nAll completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.", "#### Who are the source language producers?\n\nAll completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.", "### Annotations", "#### Annotation process\n\nAll completions were generated by querying already-tuned models (ChatGPT, LLama 2, Open-Assistant, etc.). Prompts were gathered from publicly available datasets.", "#### Who are the annotators?\n\nNo annotators were used.", "### Personal and Sensitive Information\n\nNo personal or sensitive information is part of this dataset.", "## Considerations for Using the Data", "### Social Impact of Dataset\n\nNo considerations.", "### Discussion of Biases\n\nNo considerations.", "### Other Known Limitations\n\nNo considerations.", "## Additional Information", "### Dataset Curators\n\nNicholas Kluge Corrêa.", "### Licensing Information\n\nThis dataset is licensed under the Apache License, version 2.0.", "### Contributions\n\nIf you would like to contribute, contact me at nicholas@URL!" ]

[ "region:us" ]

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[ "# Dataset Card for \"Diffusion-RFdiffusion_knots\"\n\nMore Information needed" ]

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

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

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[ "# Dataset Card for \"oasst1-chatml\"\n\nThis dataset is ChatML template added version of Open Assistant dataset, which you can find here: URL\n\nThis dataset contains conversation tree of oasst1 dataset with ChatML format, with a total of 9,846 samples.\n\nFor further information, please see the original dataset." ]

[ "TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #license-mit #region-us \n", "# Dataset Card for \"oasst1-chatml\"\n\nThis dataset is ChatML template added version of Open Assistant dataset, which you can find here: URL\n\nThis dataset contains conversation tree of oasst1 dataset with ChatML format, with a total of 9,846 samples.\n\nFor further information, please see the original dataset." ]

[ "region:us" ]

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[ "# Medical-Calgary-Cambridge-chat-37\n37 chat/dialogue entries between a patient and doctor, using the Calgary-Cambridge model.\nOriginally made for 'microsoft/phi-2' chat format." ]

[ "TAGS\n#region-us \n", "# Medical-Calgary-Cambridge-chat-37\n37 chat/dialogue entries between a patient and doctor, using the Calgary-Cambridge model.\nOriginally made for 'microsoft/phi-2' chat format." ]

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for \"kmmlu_90\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"kmmlu_90\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of AA051611/whattest\n\n\n\nDataset automatically created during the evaluation run of model AA051611/whattest on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T14:53:31.657383(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 AA051611/whattest\n\n\n\nDataset automatically created during the evaluation run of model AA051611/whattest on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-11T14:53:31.657383(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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", "size_categories:1K<n<10K", "language:fr", "astrophysics", "region:us" ]

[]

[ "fr" ]

[ "# Astrophysics french QA\n\nThe \"Astrophysics french QA\" dataset is an innovative collection combining scraped articles from the web with ChatGPT-generated question and answer pairs, offering a unique blend of information and interactive learning in the field of astrophysics. It contains almost 5k prompt / response generated by ChatGPT. It can be used to train / finetune / evaluate LLMs on astro subjects." ]

[ "TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-French #astrophysics #region-us \n", "# Astrophysics french QA\n\nThe \"Astrophysics french QA\" dataset is an innovative collection combining scraped articles from the web with ChatGPT-generated question and answer pairs, offering a unique blend of information and interactive learning in the field of astrophysics. It contains almost 5k prompt / response generated by ChatGPT. It can be used to train / finetune / evaluate LLMs on astro subjects." ]

[ "region:us" ]

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[ "### Loading the document embeddings\n\n\nThe 'corpus' split contains all document embeddings of the corpus.\n\n\nYou can either load the dataset like this:\n\n\nOr you can also stream it without downloading it before:\n\n\nNote, depending on the language, the download can be quite large.\n\n\nSearch\n------\n\n\nA full search example (on the first 1,000 paragraphs):\n\n\nOverview\n--------\n\n\nThe following table contains all language codes together with the total numbers of passages." ]

[ "TAGS\n#region-us \n", "### Loading the document embeddings\n\n\nThe 'corpus' split contains all document embeddings of the corpus.\n\n\nYou can either load the dataset like this:\n\n\nOr you can also stream it without downloading it before:\n\n\nNote, depending on the language, the download can be quite large.\n\n\nSearch\n------\n\n\nA full search example (on the first 1,000 paragraphs):\n\n\nOverview\n--------\n\n\nThe following table contains all language codes together with the total numbers of passages." ]

[ "region:us" ]

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[ "### Dataset Summary\n\n\nArgument Annotated Essays Corpus (AAEC) (Stab and Gurevych, 2017) contains student essays. A stance for a controversial theme is expressed by a major claim component as well as claim components, and premise components justify or refute the claims. Attack and support labels are defined as relations. The span covers a statement, *which can stand in isolation as a complete sentence*, according to the AAEC annotation guidelines. All components are annotated with minimum boundaries of a clause or sentence excluding so-called \"shell\" language such as *On the other hand* and *Hence*. (Morio et al., 2022, p. 642)\n\n\nThere is no premise that links to another premise or claim in a different paragraph. That means, an argumentation tree structure is complete within each paragraph. Therefore, it is possible to train a model on the full documents or just at the paragraph-level which is usually less memory-exhaustive (Eger et al., 2017, p. 16).", "### Supported Tasks and Leaderboards\n\n\n* Tasks: Argumentation Mining, Component Identification, Component Classification, Structure Identification\n* Leaderboard:", "### Languages\n\n\nThe language in the dataset is English (persuasive essays).", "### Dataset Variants\n\n\nThe 'aae2' dataset comes in a single version ('default') with 'BratDocumentWithMergedSpans' as document type. Note, that this in contrast to the base brat dataset, where the document type for the 'default' variant is 'BratDocument'. The reason is that the AAE2 dataset has already been published with only single-fragment spans. Without any need to merge fragments, the document type 'BratDocumentWithMergedSpans' is easier to handle for most of the task modules.", "### Data Schema\n\n\nSee PIE-Brat Data Schema.", "### Usage", "### Data Splits\n\n\n\n\\* included all relations between claims and premises and all claim attributions.\n\n\nSee further statistics in Stab & Gurevych (2017), p. 650, Table A.1.", "### Label Descriptions", "#### Components\n\n\n\n* 'MajorClaim' is the root node of the argumentation structure and represents the author’s standpoint on the topic. Essay bodies either support or attack the author’s standpoint expressed in the major claim. The major claim can be mentioned multiple times in a single document.\n* 'Claim' constitutes the central component of each argument. Each one has at least one premise and takes stance attribute values \"for\" or \"against\" with regarding the major claim.\n* 'Premise' is the reasons of the argument; either linked to claim or another premise.\n\n\nNote that relations between 'MajorClaim' and 'Claim' were not annotated; however, each claim is annotated with an 'Attribute' annotation with value 'for' or 'against' - which indicates the relation between itself and 'MajorClaim'. In addition, when two non-related 'Claim' 's appear in one paragraph, there is also no relations to one another.", "#### Relations\n\n\n\n* \"Each premise 'p' has one outgoing relation (i.e., there is a relation that has p as source component) and none or several incoming relations (i.e., there can be a relation with 'p' as target component).\"\n* \"A 'Claim' can exhibit several incoming relations but no outgoing relation.\" (S&G, 2017, p. 68)\n* \"The relations from the claims of the arguments to the major claim are dotted since we will not explicitly annotated them. The relation of each argument to the major claim is indicated by a stance attribute of each claim. This attribute can either be for or against as illustrated in figure 1.4.\" (Stab & Gurevych, *Guidelines for Annotating Argumentation Structures in Persuasive Essays*, 2015, p. 5)\n\n\nSee further description in Stab & Gurevych 2017, p.627 and the annotation guideline.", "### Document Converters\n\n\nThe dataset provides document converters for the following target document types:\n\n\n* 'pytorch\\_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations' with layers:\n\t+ 'labeled\\_spans': 'LabeledSpan' annotations, converted from 'BratDocumentWithMergedSpans''s 'spans'\n\t\t- labels: 'MajorClaim', 'Claim', 'Premise'\n\t+ 'binary\\_relations': 'BinaryRelation' annotations, converted from 'BratDocumentWithMergedSpans''s 'relations'\n\t\t- there are two conversion methods that convert 'Claim' attributes to their relations to 'MajorClaim' (also see the label-count changes after this relation conversion here below):\n\t\t\t* 'connect\\_first' (default setting):\n\t\t\t\t+ build a 'supports' or 'attacks' relation from each 'Claim' to the first 'MajorClaim' depending on the 'Claim''s attribute ('for' or 'against'), and\n\t\t\t\t+ build a 'semantically\\_same' relation between following 'MajorClaim' to the first 'MajorClaim'\n\t\t\t* 'connect\\_all'\n\t\t\t\t+ build a 'supports' or 'attacks' relation from each 'Claim' to every 'MajorClaim'\n\t\t\t\t+ no relations between each 'MajorClaim'\n\t\t- labels: 'supports', 'attacks', and 'semantically\\_same' if 'connect\\_first'\n* 'pytorch\\_ie.documents.TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions' with layers:\n\t+ 'labeled\\_spans', as above\n\t+ 'binary\\_relations', as above\n\t+ 'labeled\\_partitions', 'LabeledSpan' annotations, created from splitting 'BratDocumentWithMergedSpans''s 'text' at new lines ('\\n').\n\t\t- every partition is labeled as 'paragraph'\n\n\nSee here for the document type\ndefinitions.", "#### Label Statistics after Document Conversion\n\n\nWhen converting from 'BratDocumentWithMergedSpan' to 'TextDocumentWithLabeledSpansAndBinaryRelations' and 'TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions',\nwe apply a relation-conversion method (see above) that changes the label counts for the relations, as follows:\n\n\n1. 'connect\\_first' (default):\n\n\n\n2. 'connect\\_all'\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\n\"The identification of argumentation structures involves several subtasks like separating argumentative from non-argumentative text units (Moens et al. 2007; Florou\net al. 2013), classifying argument components into claims and premises (Mochales-Palau and Moens 2011; Rooney, Wang, and Browne 2012; Stab and Gurevych 2014b),\nand identifying argumentative relations (Mochales-Palau and Moens 2009; Peldszus\n2014; Stab and Gurevych 2014b). However, an approach that covers all subtasks is still\nmissing. However, an approach that covers all subtasks is still\nmissing. Furthermore, most approaches operate locally and do not optimize the global\nargumentation structure.\n\n\n\"In addition,\nto the lack of end-to-end approaches for parsing argumentation structures, there are\nrelatively few corpora annotated with argumentation structures at the discourse-level.\" (p. 621)\n\n\n\"Our primary motivation for this work is to create argument analysis methods\nfor argumentative writing support systems and to achieve a better understanding\nof argumentation structures.\" (p. 622)", "### Source Data\n\n\nPersuasive essays were collected from URL (See essay prompts, along with the essay's 'id''s here).", "#### Initial Data Collection and Normalization\n\n\n\"We randomly selected 402 English essays with a description of the writing prompt from\nURL. This online forum is an active community that provides correction and\nfeedback about different texts such as research papers, essays, or poetry. For example,\nstudents post their essays in order to receive feedback about their writing skills while\npreparing for standardized language tests. The corpus includes 7,116 sentences with\n147,271 tokens.\" (p. 630)", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nThe annotation were done using BRAT Rapid Annotation Tool (Stenetorp et al., 2012).\n\n\nAll three annotators independently annotated a random subset of 80 essays. The\nremaining 322 essays were annotated by the expert annotator.\n\n\nThe authors evaluated the inter-annotator agreement using observed agreement and Fleiss’ κ (Fleiss 1971), on each label on each sub-tasks,\nnamely, component identification, component classification, and relation identification.\nThe results were reported in their paper in Tables 2-4.", "#### Who are the annotators?\n\n\nThree non-native speakers; one of the three being an expert annotator.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\n\"[Computational Argumentation] have\nbroad application potential in various areas such as legal decision support (Mochales-Palau and Moens 2009), information retrieval (Carstens and Toni 2015), policy making (Sardianos et al. 2015), and debating technologies (Levy et al. 2014; Rinott et al.\n2015).\" (p. 619)", "### Discussion of Biases", "### Other Known Limitations\n\n\nThe relations between claims and major claims are not explicitly annotated.\n\n\n\"The proportion of non-argumentative text amounts to 47,474 tokens (32.2%) and\n1,631 sentences (22.9%). The number of sentences with several argument components\nis 583, of which 302 include several components with different types (e.g., a claim followed by premise)...\n[T]he identification of argument components requires the\nseparation of argumentative from non-argumentative text units and the recognition of\ncomponent boundaries at the token level...The proportion of paragraphs with unlinked\nargument components (e.g., unsupported claims without incoming relations) is 421\n(23%). Thus, methods that link all argument components in a paragraph are only of\nlimited use for identifying the argumentation structures in our corpus.\n\n\n\"Most of the arguments are convergent—that is, the depth of the\nargument is 1. The number of arguments with serial structure is 236 (20.9%).\" (p. 634)\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nLicense: License description by TU Darmstadt\n\n\nFunding: This work has been supported by the\nVolkswagen Foundation as part of the\nLichtenberg-Professorship Program under\ngrant no. I/82806 and by the German Federal\nMinistry of Education and Research (BMBF)\nas a part of the Software Campus project\nAWS under grant no. 01—S12054.", "### Contributions\n\n\nThanks to @ArneBinder and @idalr for adding this dataset." ]

[ "TAGS\n#region-us \n", "### Dataset Summary\n\n\nArgument Annotated Essays Corpus (AAEC) (Stab and Gurevych, 2017) contains student essays. A stance for a controversial theme is expressed by a major claim component as well as claim components, and premise components justify or refute the claims. Attack and support labels are defined as relations. The span covers a statement, *which can stand in isolation as a complete sentence*, according to the AAEC annotation guidelines. All components are annotated with minimum boundaries of a clause or sentence excluding so-called \"shell\" language such as *On the other hand* and *Hence*. (Morio et al., 2022, p. 642)\n\n\nThere is no premise that links to another premise or claim in a different paragraph. That means, an argumentation tree structure is complete within each paragraph. Therefore, it is possible to train a model on the full documents or just at the paragraph-level which is usually less memory-exhaustive (Eger et al., 2017, p. 16).", "### Supported Tasks and Leaderboards\n\n\n* Tasks: Argumentation Mining, Component Identification, Component Classification, Structure Identification\n* Leaderboard:", "### Languages\n\n\nThe language in the dataset is English (persuasive essays).", "### Dataset Variants\n\n\nThe 'aae2' dataset comes in a single version ('default') with 'BratDocumentWithMergedSpans' as document type. Note, that this in contrast to the base brat dataset, where the document type for the 'default' variant is 'BratDocument'. The reason is that the AAE2 dataset has already been published with only single-fragment spans. Without any need to merge fragments, the document type 'BratDocumentWithMergedSpans' is easier to handle for most of the task modules.", "### Data Schema\n\n\nSee PIE-Brat Data Schema.", "### Usage", "### Data Splits\n\n\n\n\\* included all relations between claims and premises and all claim attributions.\n\n\nSee further statistics in Stab & Gurevych (2017), p. 650, Table A.1.", "### Label Descriptions", "#### Components\n\n\n\n* 'MajorClaim' is the root node of the argumentation structure and represents the author’s standpoint on the topic. Essay bodies either support or attack the author’s standpoint expressed in the major claim. The major claim can be mentioned multiple times in a single document.\n* 'Claim' constitutes the central component of each argument. Each one has at least one premise and takes stance attribute values \"for\" or \"against\" with regarding the major claim.\n* 'Premise' is the reasons of the argument; either linked to claim or another premise.\n\n\nNote that relations between 'MajorClaim' and 'Claim' were not annotated; however, each claim is annotated with an 'Attribute' annotation with value 'for' or 'against' - which indicates the relation between itself and 'MajorClaim'. In addition, when two non-related 'Claim' 's appear in one paragraph, there is also no relations to one another.", "#### Relations\n\n\n\n* \"Each premise 'p' has one outgoing relation (i.e., there is a relation that has p as source component) and none or several incoming relations (i.e., there can be a relation with 'p' as target component).\"\n* \"A 'Claim' can exhibit several incoming relations but no outgoing relation.\" (S&G, 2017, p. 68)\n* \"The relations from the claims of the arguments to the major claim are dotted since we will not explicitly annotated them. The relation of each argument to the major claim is indicated by a stance attribute of each claim. This attribute can either be for or against as illustrated in figure 1.4.\" (Stab & Gurevych, *Guidelines for Annotating Argumentation Structures in Persuasive Essays*, 2015, p. 5)\n\n\nSee further description in Stab & Gurevych 2017, p.627 and the annotation guideline.", "### Document Converters\n\n\nThe dataset provides document converters for the following target document types:\n\n\n* 'pytorch\\_ie.documents.TextDocumentWithLabeledSpansAndBinaryRelations' with layers:\n\t+ 'labeled\\_spans': 'LabeledSpan' annotations, converted from 'BratDocumentWithMergedSpans''s 'spans'\n\t\t- labels: 'MajorClaim', 'Claim', 'Premise'\n\t+ 'binary\\_relations': 'BinaryRelation' annotations, converted from 'BratDocumentWithMergedSpans''s 'relations'\n\t\t- there are two conversion methods that convert 'Claim' attributes to their relations to 'MajorClaim' (also see the label-count changes after this relation conversion here below):\n\t\t\t* 'connect\\_first' (default setting):\n\t\t\t\t+ build a 'supports' or 'attacks' relation from each 'Claim' to the first 'MajorClaim' depending on the 'Claim''s attribute ('for' or 'against'), and\n\t\t\t\t+ build a 'semantically\\_same' relation between following 'MajorClaim' to the first 'MajorClaim'\n\t\t\t* 'connect\\_all'\n\t\t\t\t+ build a 'supports' or 'attacks' relation from each 'Claim' to every 'MajorClaim'\n\t\t\t\t+ no relations between each 'MajorClaim'\n\t\t- labels: 'supports', 'attacks', and 'semantically\\_same' if 'connect\\_first'\n* 'pytorch\\_ie.documents.TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions' with layers:\n\t+ 'labeled\\_spans', as above\n\t+ 'binary\\_relations', as above\n\t+ 'labeled\\_partitions', 'LabeledSpan' annotations, created from splitting 'BratDocumentWithMergedSpans''s 'text' at new lines ('\\n').\n\t\t- every partition is labeled as 'paragraph'\n\n\nSee here for the document type\ndefinitions.", "#### Label Statistics after Document Conversion\n\n\nWhen converting from 'BratDocumentWithMergedSpan' to 'TextDocumentWithLabeledSpansAndBinaryRelations' and 'TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions',\nwe apply a relation-conversion method (see above) that changes the label counts for the relations, as follows:\n\n\n1. 'connect\\_first' (default):\n\n\n\n2. 'connect\\_all'\n\n\n\nDataset Creation\n----------------", "### Curation Rationale\n\n\n\"The identification of argumentation structures involves several subtasks like separating argumentative from non-argumentative text units (Moens et al. 2007; Florou\net al. 2013), classifying argument components into claims and premises (Mochales-Palau and Moens 2011; Rooney, Wang, and Browne 2012; Stab and Gurevych 2014b),\nand identifying argumentative relations (Mochales-Palau and Moens 2009; Peldszus\n2014; Stab and Gurevych 2014b). However, an approach that covers all subtasks is still\nmissing. However, an approach that covers all subtasks is still\nmissing. Furthermore, most approaches operate locally and do not optimize the global\nargumentation structure.\n\n\n\"In addition,\nto the lack of end-to-end approaches for parsing argumentation structures, there are\nrelatively few corpora annotated with argumentation structures at the discourse-level.\" (p. 621)\n\n\n\"Our primary motivation for this work is to create argument analysis methods\nfor argumentative writing support systems and to achieve a better understanding\nof argumentation structures.\" (p. 622)", "### Source Data\n\n\nPersuasive essays were collected from URL (See essay prompts, along with the essay's 'id''s here).", "#### Initial Data Collection and Normalization\n\n\n\"We randomly selected 402 English essays with a description of the writing prompt from\nURL. This online forum is an active community that provides correction and\nfeedback about different texts such as research papers, essays, or poetry. For example,\nstudents post their essays in order to receive feedback about their writing skills while\npreparing for standardized language tests. The corpus includes 7,116 sentences with\n147,271 tokens.\" (p. 630)", "#### Who are the source language producers?", "### Annotations", "#### Annotation process\n\n\nThe annotation were done using BRAT Rapid Annotation Tool (Stenetorp et al., 2012).\n\n\nAll three annotators independently annotated a random subset of 80 essays. The\nremaining 322 essays were annotated by the expert annotator.\n\n\nThe authors evaluated the inter-annotator agreement using observed agreement and Fleiss’ κ (Fleiss 1971), on each label on each sub-tasks,\nnamely, component identification, component classification, and relation identification.\nThe results were reported in their paper in Tables 2-4.", "#### Who are the annotators?\n\n\nThree non-native speakers; one of the three being an expert annotator.", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\n\"[Computational Argumentation] have\nbroad application potential in various areas such as legal decision support (Mochales-Palau and Moens 2009), information retrieval (Carstens and Toni 2015), policy making (Sardianos et al. 2015), and debating technologies (Levy et al. 2014; Rinott et al.\n2015).\" (p. 619)", "### Discussion of Biases", "### Other Known Limitations\n\n\nThe relations between claims and major claims are not explicitly annotated.\n\n\n\"The proportion of non-argumentative text amounts to 47,474 tokens (32.2%) and\n1,631 sentences (22.9%). The number of sentences with several argument components\nis 583, of which 302 include several components with different types (e.g., a claim followed by premise)...\n[T]he identification of argument components requires the\nseparation of argumentative from non-argumentative text units and the recognition of\ncomponent boundaries at the token level...The proportion of paragraphs with unlinked\nargument components (e.g., unsupported claims without incoming relations) is 421\n(23%). Thus, methods that link all argument components in a paragraph are only of\nlimited use for identifying the argumentation structures in our corpus.\n\n\n\"Most of the arguments are convergent—that is, the depth of the\nargument is 1. The number of arguments with serial structure is 236 (20.9%).\" (p. 634)\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nLicense: License description by TU Darmstadt\n\n\nFunding: This work has been supported by the\nVolkswagen Foundation as part of the\nLichtenberg-Professorship Program under\ngrant no. I/82806 and by the German Federal\nMinistry of Education and Research (BMBF)\nas a part of the Software Campus project\nAWS under grant no. 01—S12054.", "### Contributions\n\n\nThanks to @ArneBinder and @idalr for adding this dataset." ]

[ "license:cc-by-4.0", "arxiv:2401.09388", "region:us" ]

[ "2401.09388" ]

[]

[]

[ "TAGS\n#license-cc-by-4.0 #arxiv-2401.09388 #region-us \n" ]

[ "size_categories:n<1K", "language:zh", "license:apache-2.0", "human-feedback", "region:us" ]

[]

[ "zh" ]

[]

[ "TAGS\n#size_categories-n<1K #language-Chinese #license-apache-2.0 #human-feedback #region-us \n" ]

[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:crowdsourced", "multilinguality:multilingual", "size_categories:n<1K", "size_categories:1K<n<10K", "size_categories:10K<n<100K", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "source_datasets:original", "language:aa", "language:ab", "language:ace", "language:af", "language:ak", "language:als", "language:am", "language:an", "language:ang", "language:ar", "language:arc", "language:arz", "language:as", "language:ast", "language:atj", "language:av", "language:ay", "language:az", "language:azb", "language:ba", "language:bar", "language:bcl", "language:be", "language:bg", "language:bh", "language:bi", "language:bjn", "language:bm", "language:bn", "language:bo", "language:bpy", "language:br", "language:bs", "language:bug", "language:bxr", "language:ca", "language:cbk", "language:cdo", "language:ce", "language:ceb", "language:ch", "language:cho", "language:chr", "language:chy", "language:ckb", "language:co", "language:cr", "language:crh", "language:cs", "language:csb", "language:cu", "language:cv", "language:cy", "language:da", "language:de", "language:din", "language:diq", "language:dsb", "language:dty", "language:dv", "language:dz", "language:ee", "language:el", "language:eml", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:ext", "language:fa", "language:ff", "language:fi", "language:fj", "language:fo", "language:fr", "language:frp", "language:frr", "language:fur", "language:fy", "language:ga", "language:gag", "language:gan", "language:gd", "language:gl", "language:glk", "language:gn", "language:gom", "language:gor", "language:got", "language:gu", "language:gv", "language:ha", "language:hak", "language:haw", "language:he", "language:hi", "language:hif", "language:ho", "language:hr", "language:hsb", "language:ht", "language:hu", "language:hy", "language:ia", "language:id", "language:ie", "language:ig", "language:ii", "language:ik", "language:ilo", "language:inh", "language:io", "language:is", "language:it", "language:iu", "language:ja", "language:jam", "language:jbo", "language:jv", "language:ka", "language:kaa", "language:kab", "language:kbd", "language:kbp", "language:kg", "language:ki", "language:kj", "language:kk", "language:kl", "language:km", "language:kn", "language:ko", "language:koi", "language:krc", "language:ks", "language:ksh", "language:ku", "language:kv", "language:kw", "language:ky", "language:la", "language:lad", "language:lb", "language:lbe", "language:lez", "language:lfn", "language:lg", "language:li", "language:lij", "language:lmo", "language:ln", "language:lo", "language:lrc", "language:lt", "language:ltg", "language:lv", "language:lzh", "language:mai", "language:mdf", "language:mg", "language:mh", "language:mhr", "language:mi", "language:min", "language:mk", "language:ml", "language:mn", "language:mr", "language:mrj", "language:ms", "language:mt", "language:mus", "language:mwl", "language:my", "language:myv", "language:mzn", "language:na", "language:nah", "language:nan", "language:nap", "language:nds", "language:ne", "language:new", "language:ng", "language:nl", "language:nn", "language:no", "language:nov", "language:nrf", "language:nso", "language:nv", "language:ny", "language:oc", "language:olo", "language:om", "language:or", "language:os", "language:pa", "language:pag", "language:pam", "language:pap", "language:pcd", "language:pdc", "language:pfl", "language:pi", "language:pih", "language:pl", "language:pms", "language:pnb", "language:pnt", "language:ps", "language:pt", "language:qu", "language:rm", "language:rmy", "language:rn", "language:ro", "language:ru", "language:rue", "language:rup", "language:rw", "language:sa", "language:sah", "language:sat", "language:sc", "language:scn", "language:sco", "language:sd", "language:se", "language:sg", "language:sgs", "language:sh", "language:si", "language:sk", "language:sl", "language:sm", "language:sn", "language:so", "language:sq", "language:sr", "language:srn", "language:ss", "language:st", "language:stq", "language:su", "language:sv", "language:sw", "language:szl", "language:ta", "language:tcy", "language:tdt", "language:te", "language:tg", "language:th", "language:ti", "language:tk", "language:tl", "language:tn", "language:to", "language:tpi", "language:tr", "language:ts", "language:tt", "language:tum", "language:tw", "language:ty", "language:tyv", "language:udm", "language:ug", "language:uk", "language:ur", "language:uz", "language:ve", "language:vec", "language:vep", "language:vi", "language:vls", "language:vo", "language:vro", "language:wa", "language:war", "language:wo", "language:wuu", "language:xal", "language:xh", "language:xmf", "language:yi", "language:yo", "language:yue", "language:za", "language:zea", "language:zh", "language:zu", "license:cc-by-sa-3.0", "license:gfdl", "region:us" ]

[]

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[ "# Dataset Card for Wikipedia\n\nThis repo is a fork of the olm/wikipedia repo which itself is a fork of the original Hugging Face Wikipedia repo here.\n\nThis fork modifies 'olm/wikipedia' to enable running on lower resourced machines. These changes have been proposed as a PR with the olm/wikipedia project.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nWikipedia dataset containing cleaned articles of all languages. The datasets are built from the Wikipedia dump (URL with one split per language. Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.).\n\nThe articles are parsed using the ''mwparserfromhell'' tool.\n\nTo load this dataset you need to install the following dependencies:\n\n\n\nThen, you can load any subset of Wikipedia per language and per date this way:\n\n\n\nYou can find the full list of languages and dates here.", "### Supported Tasks and Leaderboards\n\nThe dataset is generally used for Language Modeling.", "### Languages\n\nYou can find the list of languages here.", "## Dataset Structure", "### Data Instances\n\nAn example looks as follows:", "### Data Fields\n\nThe data fields are the same among all configurations:\n\n- 'id' ('str'): ID of the article.\n- 'url' ('str'): URL of the article.\n- 'title' ('str'): Title of the article.\n- 'text' ('str'): Text content of the article.", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nMost of Wikipedia's text and many of its images are co-licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License(CC BY-SA) and the GNU Free Documentation License(GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts). \n\nSome text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes the text." ]

[ "TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-crowdsourced #multilinguality-multilingual #size_categories-n<1K #size_categories-1K<n<10K #size_categories-10K<n<100K #size_categories-100K<n<1M #size_categories-1M<n<10M #source_datasets-original #language-Afar #language-Abkhazian #language-Achinese #language-Afrikaans #language-Akan #language-Tosk Albanian #language-Amharic #language-Aragonese #language-Old English (ca. 450-1100) #language-Arabic #language-Official Aramaic (700-300 BCE) #language-Egyptian Arabic #language-Assamese #language-Asturian #language-Atikamekw #language-Avaric #language-Aymara #language-Azerbaijani #language-South Azerbaijani #language-Bashkir #language-Bavarian #language-Central Bikol #language-Belarusian #language-Bulgarian #language-bh #language-Bislama #language-Banjar #language-Bambara #language-Bengali #language-Tibetan #language-Bishnupriya #language-Breton #language-Bosnian #language-Buginese #language-Russia Buriat #language-Catalan #language-Chavacano #language-Min Dong Chinese #language-Chechen #language-Cebuano #language-Chamorro #language-Choctaw #language-Cherokee #language-Cheyenne #language-Central Kurdish #language-Corsican #language-Cree #language-Crimean Tatar #language-Czech #language-Kashubian #language-Church Slavic #language-Chuvash #language-Welsh #language-Danish #language-German #language-Dinka #language-Dimli (individual language) #language-Lower Sorbian #language-Dotyali #language-Dhivehi #language-Dzongkha #language-Ewe #language-Modern Greek (1453-) #language-Emiliano-Romagnolo #language-English #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Extremaduran #language-Persian #language-Fulah #language-Finnish #language-Fijian #language-Faroese #language-French #language-Arpitan #language-Northern Frisian #language-Friulian #language-Western Frisian #language-Irish #language-Gagauz #language-Gan Chinese #language-Scottish Gaelic #language-Galician #language-Gilaki #language-Guarani #language-Goan Konkani #language-Gorontalo #language-Gothic #language-Gujarati #language-Manx #language-Hausa #language-Hakka Chinese #language-Hawaiian #language-Hebrew #language-Hindi #language-Fiji Hindi #language-Hiri Motu #language-Croatian #language-Upper Sorbian #language-Haitian #language-Hungarian #language-Armenian #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Interlingue #language-Igbo #language-Sichuan Yi #language-Inupiaq #language-Iloko #language-Ingush #language-Ido #language-Icelandic #language-Italian #language-Inuktitut #language-Japanese #language-Jamaican Creole English #language-Lojban #language-Javanese #language-Georgian #language-Kara-Kalpak #language-Kabyle #language-Kabardian #language-Kabiyè #language-Kongo #language-Kikuyu #language-Kuanyama #language-Kazakh #language-Kalaallisut #language-Khmer #language-Kannada #language-Korean #language-Komi-Permyak #language-Karachay-Balkar #language-Kashmiri #language-Kölsch #language-Kurdish #language-Komi #language-Cornish #language-Kirghiz #language-Latin #language-Ladino #language-Luxembourgish #language-Lak #language-Lezghian #language-Lingua Franca Nova #language-Ganda #language-Limburgan #language-Ligurian #language-Lombard #language-Lingala #language-Lao #language-Northern Luri #language-Lithuanian #language-Latgalian #language-Latvian #language-Literary Chinese #language-Maithili #language-Moksha #language-Malagasy #language-Marshallese #language-Eastern Mari #language-Maori #language-Minangkabau #language-Macedonian #language-Malayalam #language-Mongolian #language-Marathi #language-Western Mari #language-Malay (macrolanguage) #language-Maltese #language-Creek #language-Mirandese #language-Burmese #language-Erzya #language-Mazanderani #language-Nauru #language-nah #language-Min Nan Chinese #language-Neapolitan #language-Low German #language-Nepali (macrolanguage) #language-Newari #language-Ndonga #language-Dutch #language-Norwegian Nynorsk #language-Norwegian #language-Novial #language-Jèrriais #language-Pedi #language-Navajo #language-Nyanja #language-Occitan (post 1500) #language-Livvi #language-Oromo #language-Oriya (macrolanguage) #language-Ossetian #language-Panjabi #language-Pangasinan #language-Pampanga #language-Papiamento #language-Picard #language-Pennsylvania German #language-Pfaelzisch #language-Pali #language-Pitcairn-Norfolk #language-Polish #language-Piemontese #language-Western Panjabi #language-Pontic #language-Pushto #language-Portuguese #language-Quechua #language-Romansh #language-Vlax Romani #language-Rundi #language-Romanian #language-Russian #language-Rusyn #language-Macedo-Romanian #language-Kinyarwanda #language-Sanskrit #language-Yakut #language-Santali #language-Sardinian #language-Sicilian #language-Scots #language-Sindhi #language-Northern Sami #language-Sango #language-Samogitian #language-Serbo-Croatian #language-Sinhala #language-Slovak #language-Slovenian #language-Samoan #language-Shona #language-Somali #language-Albanian #language-Serbian #language-Sranan Tongo #language-Swati #language-Southern Sotho #language-Saterfriesisch #language-Sundanese #language-Swedish #language-Swahili (macrolanguage) #language-Silesian #language-Tamil #language-Tulu #language-Tetun Dili #language-Telugu #language-Tajik #language-Thai #language-Tigrinya #language-Turkmen #language-Tagalog #language-Tswana #language-Tonga (Tonga Islands) #language-Tok Pisin #language-Turkish #language-Tsonga #language-Tatar #language-Tumbuka #language-Twi #language-Tahitian #language-Tuvinian #language-Udmurt #language-Uighur #language-Ukrainian #language-Urdu #language-Uzbek #language-Venda #language-Venetian #language-Veps #language-Vietnamese #language-Vlaams #language-Volapük #language-Võro #language-Walloon #language-Waray (Philippines) #language-Wolof #language-Wu Chinese #language-Kalmyk #language-Xhosa #language-Mingrelian #language-Yiddish #language-Yoruba #language-Yue Chinese #language-Zhuang #language-Zeeuws #language-Chinese #language-Zulu #license-cc-by-sa-3.0 #license-gfdl #region-us \n", "# Dataset Card for Wikipedia\n\nThis repo is a fork of the olm/wikipedia repo which itself is a fork of the original Hugging Face Wikipedia repo here.\n\nThis fork modifies 'olm/wikipedia' to enable running on lower resourced machines. These changes have been proposed as a PR with the olm/wikipedia project.", "## Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: \n- Point of Contact:", "### Dataset Summary\n\nWikipedia dataset containing cleaned articles of all languages. The datasets are built from the Wikipedia dump (URL with one split per language. Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.).\n\nThe articles are parsed using the ''mwparserfromhell'' tool.\n\nTo load this dataset you need to install the following dependencies:\n\n\n\nThen, you can load any subset of Wikipedia per language and per date this way:\n\n\n\nYou can find the full list of languages and dates here.", "### Supported Tasks and Leaderboards\n\nThe dataset is generally used for Language Modeling.", "### Languages\n\nYou can find the list of languages here.", "## Dataset Structure", "### Data Instances\n\nAn example looks as follows:", "### Data Fields\n\nThe data fields are the same among all configurations:\n\n- 'id' ('str'): ID of the article.\n- 'url' ('str'): URL of the article.\n- 'title' ('str'): Title of the article.\n- 'text' ('str'): Text content of the article.", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information\n\nMost of Wikipedia's text and many of its images are co-licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License(CC BY-SA) and the GNU Free Documentation License(GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts). \n\nSome text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes the text." ]