Datasets:

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ftopal

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huggingface-datasets

like 0

[ "task_categories:feature-extraction", "size_categories:1M<n<10M", "language:en", "license:apache-2.0", "retail", "car", "region:us" ]

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[ "en" ]

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[ "TAGS\n#task_categories-feature-extraction #size_categories-1M<n<10M #language-English #license-apache-2.0 #retail #car #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of BFauber/santa1.1b_10e6\n\n\n\nDataset automatically created during the evaluation run of model BFauber/santa1.1b_10e6 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T16:45:17.902394(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/santa1.1b_10e6\n\n\n\nDataset automatically created during the evaluation run of model BFauber/santa1.1b_10e6 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T16:45:17.902394(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Sharathhebbar24/Med_GPT2\n\n\n\nDataset automatically created during the evaluation run of model Sharathhebbar24/Med_GPT2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T16:52:19.552836(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Sharathhebbar24/Med_GPT2\n\n\n\nDataset automatically created during the evaluation run of model Sharathhebbar24/Med_GPT2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T16:52:19.552836(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[]

[ "## Description\n\nThis is a fair-use dataset of research-only experiments, which are not sold.\nThe visuals and sounds have been re-generated from scratch using AI.", "## Model\n\nSVD", "# Tags\n\n- Movie" ]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "## Description\n\nThis is a fair-use dataset of research-only experiments, which are not sold.\nThe visuals and sounds have been re-generated from scratch using AI.", "## Model\n\nSVD", "# Tags\n\n- Movie" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of YKM11/Mistral-7B-adaptv0.9\n\n\n\nDataset automatically created during the evaluation run of model YKM11/Mistral-7B-adaptv0.9 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T17:00:46.395617(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 YKM11/Mistral-7B-adaptv0.9\n\n\n\nDataset automatically created during the evaluation run of model YKM11/Mistral-7B-adaptv0.9 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T17:00:46.395617(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:translation", "size_categories:1M<n<10M", "language:tw", "language:en", "language:ja", "language:ko", "language:id", "language:vi", "language:th", "license:mit", "MultiCCAligned", "translation", "OPUS", "region:us" ]

[]

[ "tw", "en", "ja", "ko", "id", "vi", "th" ]

[ "# Dataset Card for [MultiCCAligned-TW-Corpus]", "## Table of Contents\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- Homepage: \n- Repository: \n- Paper: \n- Leaderboard:\n- Point of Contact: Heng-Shiou Sheu", "### Dataset Summary\nMultiCCAligned-TW-Corpus 是一個機器翻譯基準的多語言資料集，源自 OPUS 收集的使用者貢獻的翻譯，並由 OPUS。該資料集包括按語言對排序的測試和開發資料。它包括數百種語言對的測試集，並且不斷更新。請檢查版本號標籤以引用您正在使用的版本。", "### Supported Tasks and Leaderboards", "### Languages\n此資料集涵蓋數百種語言和語言對，並按 ISO-639-1 語言組織。目前版本涵蓋以下語言。繁體中文、英文、日文、韓文、印尼文、越南文、泰文", "## Dataset Structure", "### Data Instances\n\n資料以 , 分隔檔案中內容，具有三個欄位：指示、輸入和輸出。請注意，我們並不暗示平移方向，並認為資料集是對稱的並用作兩個方向的測試集。", "### Data Splits\n先整理出 Train 資料。", "## Dataset Creation", "### Curation Rationale\n本資料集將持續更新，未來將公開發佈於 Github 當中。高語言覆蓋率是本計畫的主要目標，資料集的準備與標準化語言標籤和分發格式保持一致和系統化。", "### Source Data", "#### Initial Data Collection and Normalization\nMultiCCAligned 資料集是從根據 68 個 Commoncrawl 快照創建的（截至 2020 年 3 月）。根據標點符號將文件分割成句子，並執行重複資料刪除。語料庫的準備工作並沒有提出任何智慧財產權主張。原始發行版可從 URL 取得", "#### Who are the source language producers?\n這些轉錄本已由 EMNLP'20 作者群製作。", "### Personal and Sensitive Information\n有關處理個人資訊和敏感資訊的信息，我們請諮詢資料的原始提供者。該資料集未經過任何方式處理以檢測或刪除潛在的敏感資訊或個人資訊。", "### Social Impact of Dataset\n語言覆蓋率很高，因此它代表了機器翻譯開發的非常有價值的資源，特別是對於資源較少的語言和語言對。不斷成長的資料庫也代表著一種動態資源，其價值將進一步成長。", "### Other Known Limitations\n這些句子通常很短，因此很容易翻譯。對於高資源語言，這會導致結果不如更具挑戰性的基準有用。對於資源較少的語言對來說，即使在非常具有挑戰性的設定中，範例的有限複雜性實際上也是衡量進度的一件好事。", "### Dataset Curators\n此資料集由Heng-Shiou Sheu 製作。", "### Licensing Information\n這些資料集沒使用 License." ]

[ "TAGS\n#task_categories-translation #size_categories-1M<n<10M #language-Twi #language-English #language-Japanese #language-Korean #language-Indonesian #language-Vietnamese #language-Thai #license-mit #MultiCCAligned #translation #OPUS #region-us \n", "# Dataset Card for [MultiCCAligned-TW-Corpus]", "## Table of Contents\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- Homepage: \n- Repository: \n- Paper: \n- Leaderboard:\n- Point of Contact: Heng-Shiou Sheu", "### Dataset Summary\nMultiCCAligned-TW-Corpus 是一個機器翻譯基準的多語言資料集，源自 OPUS 收集的使用者貢獻的翻譯，並由 OPUS。該資料集包括按語言對排序的測試和開發資料。它包括數百種語言對的測試集，並且不斷更新。請檢查版本號標籤以引用您正在使用的版本。", "### Supported Tasks and Leaderboards", "### Languages\n此資料集涵蓋數百種語言和語言對，並按 ISO-639-1 語言組織。目前版本涵蓋以下語言。繁體中文、英文、日文、韓文、印尼文、越南文、泰文", "## Dataset Structure", "### Data Instances\n\n資料以 , 分隔檔案中內容，具有三個欄位：指示、輸入和輸出。請注意，我們並不暗示平移方向，並認為資料集是對稱的並用作兩個方向的測試集。", "### Data Splits\n先整理出 Train 資料。", "## Dataset Creation", "### Curation Rationale\n本資料集將持續更新，未來將公開發佈於 Github 當中。高語言覆蓋率是本計畫的主要目標，資料集的準備與標準化語言標籤和分發格式保持一致和系統化。", "### Source Data", "#### Initial Data Collection and Normalization\nMultiCCAligned 資料集是從根據 68 個 Commoncrawl 快照創建的（截至 2020 年 3 月）。根據標點符號將文件分割成句子，並執行重複資料刪除。語料庫的準備工作並沒有提出任何智慧財產權主張。原始發行版可從 URL 取得", "#### Who are the source language producers?\n這些轉錄本已由 EMNLP'20 作者群製作。", "### Personal and Sensitive Information\n有關處理個人資訊和敏感資訊的信息，我們請諮詢資料的原始提供者。該資料集未經過任何方式處理以檢測或刪除潛在的敏感資訊或個人資訊。", "### Social Impact of Dataset\n語言覆蓋率很高，因此它代表了機器翻譯開發的非常有價值的資源，特別是對於資源較少的語言和語言對。不斷成長的資料庫也代表著一種動態資源，其價值將進一步成長。", "### Other Known Limitations\n這些句子通常很短，因此很容易翻譯。對於高資源語言，這會導致結果不如更具挑戰性的基準有用。對於資源較少的語言對來說，即使在非常具有挑戰性的設定中，範例的有限複雜性實際上也是衡量進度的一件好事。", "### Dataset Curators\n此資料集由Heng-Shiou Sheu 製作。", "### Licensing Information\n這些資料集沒使用 License." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of YKM11/Mistral-7B-adaptv1\n\n\n\nDataset automatically created during the evaluation run of model YKM11/Mistral-7B-adaptv1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T17:19:39.634045(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 YKM11/Mistral-7B-adaptv1\n\n\n\nDataset automatically created during the evaluation run of model YKM11/Mistral-7B-adaptv1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T17:19:39.634045(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "license:cc-by-nc-2.0", "region:us" ]

[]

[]

[ "# Wilhelm Busch \"Max und Moritz\" Dataset\n\nWelcome to the Wilhelm Busch \"Max und Moritz\" Dataset, a curated collection of 73 public domain images from the classic German children's book \"Max und Moritz\". This dataset has been enhanced with GPT-Vision generated captions and is ready for training AI models.\n\n", "## Dataset Overview\n\n- Content: The dataset contains 73 images depicting the original illustrations from \"Max und Moritz\", a tale of two mischievous boys, created by the German humorist, poet, illustrator, and painter Wilhelm Busch.\n- Source: These images have been carefully selected from various online sources, each offering a glimpse into the 19th-century artwork that has become a staple in German literary culture.\n- Usage: Ideal for training AI in understanding sequential art narrative, character recognition, and historical illustration styles.", "## Licensing\n\n- While the original images by Wilhelm Busch are in the public domain, this particular dataset, complete with its GPT-Vision generated captions, falls under the Creative Commons Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0) license. This license allows for the non-commercial use of the images and captions, as long as proper credit is provided.\n- More information about this license can be found at CC BY-NC 2.0 License details.", "## Dataset Composition\n\nEach image in this collection is accompanied by a descriptive caption, providing contextual information that can be used to train AI models. The captions are crafted to highlight key elements of the illustrations, aiding in the model's learning process.", "## How to Use the Dataset\n\n1. Download the Dataset: Access the collection via the provided link for academic and non-commercial research purposes.\n2. Review the Images and Captions: Examine the illustrations and their respective captions to understand the dataset's range and depth.\n3. Train Your AI Model: Use the dataset to train AI models in recognizing and generating artwork that reflects the style and narrative techniques of Wilhelm Busch.", "## Contributions and Feedback\n\nWe appreciate any contributions or feedback aimed at improving the dataset's quality. If you would like to contribute additional images or captions or have suggestions for improvement, please contact us. Your involvement is essential for enhancing this resource for the AI and literary communities.\n\n---\n\nWith its historical significance and charm, the Wilhelm Busch \"Max und Moritz\" Dataset promises to be an invaluable resource for those interested in the intersection of AI, art, and literature." ]

[ "TAGS\n#license-cc-by-nc-2.0 #region-us \n", "# Wilhelm Busch \"Max und Moritz\" Dataset\n\nWelcome to the Wilhelm Busch \"Max und Moritz\" Dataset, a curated collection of 73 public domain images from the classic German children's book \"Max und Moritz\". This dataset has been enhanced with GPT-Vision generated captions and is ready for training AI models.\n\n", "## Dataset Overview\n\n- Content: The dataset contains 73 images depicting the original illustrations from \"Max und Moritz\", a tale of two mischievous boys, created by the German humorist, poet, illustrator, and painter Wilhelm Busch.\n- Source: These images have been carefully selected from various online sources, each offering a glimpse into the 19th-century artwork that has become a staple in German literary culture.\n- Usage: Ideal for training AI in understanding sequential art narrative, character recognition, and historical illustration styles.", "## Licensing\n\n- While the original images by Wilhelm Busch are in the public domain, this particular dataset, complete with its GPT-Vision generated captions, falls under the Creative Commons Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0) license. This license allows for the non-commercial use of the images and captions, as long as proper credit is provided.\n- More information about this license can be found at CC BY-NC 2.0 License details.", "## Dataset Composition\n\nEach image in this collection is accompanied by a descriptive caption, providing contextual information that can be used to train AI models. The captions are crafted to highlight key elements of the illustrations, aiding in the model's learning process.", "## How to Use the Dataset\n\n1. Download the Dataset: Access the collection via the provided link for academic and non-commercial research purposes.\n2. Review the Images and Captions: Examine the illustrations and their respective captions to understand the dataset's range and depth.\n3. Train Your AI Model: Use the dataset to train AI models in recognizing and generating artwork that reflects the style and narrative techniques of Wilhelm Busch.", "## Contributions and Feedback\n\nWe appreciate any contributions or feedback aimed at improving the dataset's quality. If you would like to contribute additional images or captions or have suggestions for improvement, please contact us. Your involvement is essential for enhancing this resource for the AI and literary communities.\n\n---\n\nWith its historical significance and charm, the Wilhelm Busch \"Max und Moritz\" Dataset promises to be an invaluable resource for those interested in the intersection of AI, art, and literature." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of BFauber/opt350m_10e6\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt350m_10e6 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:07:15.412777(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt350m_10e6\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt350m_10e6 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:07:15.412777(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt350m_10e5\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt350m_10e5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:12:11.233517(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt350m_10e5\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt350m_10e5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:12:11.233517(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ConvexAI/BurningBruce-004\n\n\n\nDataset automatically created during the evaluation run of model ConvexAI/BurningBruce-004 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:13:25.503576(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ConvexAI/BurningBruce-004\n\n\n\nDataset automatically created during the evaluation run of model ConvexAI/BurningBruce-004 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:13:25.503576(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e5\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:15:22.579177(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e5\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:15:22.579177(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e6_run1\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e6_run1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:19:34.951673(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e6_run1\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e6_run1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:19:34.951673(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/bloom-1b1_10e6\n\n\n\nDataset automatically created during the evaluation run of model BFauber/bloom-1b1_10e6 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:24:13.797683(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/bloom-1b1_10e6\n\n\n\nDataset automatically created during the evaluation run of model BFauber/bloom-1b1_10e6 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:24:13.797683(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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", "language:en", "license:apache-2.0", "not-for-all-audiences", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-generation #language-English #license-apache-2.0 #not-for-all-audiences #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of BFauber/opt125m_10e4\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:39:22.964015(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e4\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:39:22.964015(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e3\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:42:28.559578(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e3\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:42:28.559578(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of BFauber/opt125m_10e2\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:49:35.603979(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e2\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:49:35.603979(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ConvexAI/BurningBruce-005\n\n\n\nDataset automatically created during the evaluation run of model ConvexAI/BurningBruce-005 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:58:34.137305(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ConvexAI/BurningBruce-005\n\n\n\nDataset automatically created during the evaluation run of model ConvexAI/BurningBruce-005 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T18:58:34.137305(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt1.3b_10e4\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt1.3b_10e4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:08:04.507559(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt1.3b_10e4\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt1.3b_10e4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:08:04.507559(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:10M<n<100M", "language:pt", "license:cc-by-4.0", "arxiv:1911.02116", "arxiv:2201.06642", "arxiv:2107.06499", "region:us" ]

[ "1911.02116", "2201.06642", "2107.06499" ]

[ "pt" ]

[ "### Dataset Description\n* Curated by:\n* Funded by:\n* Language(s) (NLP): Brazilian Portuguese (pt-BR)\n* License: Creative Commons Attribution 4.0 International Public License", "### Dataset Sources\n\n\n* Repository: URL\n* Paper:\n\n\nDataset Structure\n-----------------\n\n\nData Collection and Processing\n------------------------------\n\n\nRaw corpora sizes in terms of billions of tokens and file size in GiB:\n\n\n\nCrawlPT is deduplicated using MinHash algorithm and Locality Sensitive Hashing, following the approach of Lee et al. (2022).\n\n\nWe used 5-grams and a signature of size 256, considering two documents to be identical if their Jaccard Similarity exceeded 0.7.\nDeduplicate rate found by the Minhash-LSH algorithm for the CrawlPT corpus:\n\n\n\nAcknowledgment\n--------------\n\n\nThis work has been supported by the AI Center of Excellence (Centro de Excelência em Inteligência Artificial – CEIA) of the Institute of Informatics at the Federal University of Goiás (INF-UFG)." ]

[ "TAGS\n#task_categories-text-generation #size_categories-10M<n<100M #language-Portuguese #license-cc-by-4.0 #arxiv-1911.02116 #arxiv-2201.06642 #arxiv-2107.06499 #region-us \n", "### Dataset Description\n* Curated by:\n* Funded by:\n* Language(s) (NLP): Brazilian Portuguese (pt-BR)\n* License: Creative Commons Attribution 4.0 International Public License", "### Dataset Sources\n\n\n* Repository: URL\n* Paper:\n\n\nDataset Structure\n-----------------\n\n\nData Collection and Processing\n------------------------------\n\n\nRaw corpora sizes in terms of billions of tokens and file size in GiB:\n\n\n\nCrawlPT is deduplicated using MinHash algorithm and Locality Sensitive Hashing, following the approach of Lee et al. (2022).\n\n\nWe used 5-grams and a signature of size 256, considering two documents to be identical if their Jaccard Similarity exceeded 0.7.\nDeduplicate rate found by the Minhash-LSH algorithm for the CrawlPT corpus:\n\n\n\nAcknowledgment\n--------------\n\n\nThis work has been supported by the AI Center of Excellence (Centro de Excelência em Inteligência Artificial – CEIA) of the Institute of Informatics at the Federal University of Goiás (INF-UFG)." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of BFauber/opt125m_10e5_1ep\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5_1ep on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:20:39.939834(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e5_1ep\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5_1ep on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:20:39.939834(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt1.3b_10e6\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt1.3b_10e6 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:21:41.324363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt1.3b_10e6\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt1.3b_10e6 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:21:41.324363(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt1.3b_10e5\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt1.3b_10e5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:22:53.886717(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt1.3b_10e5\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt1.3b_10e5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:22:53.886717(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e5_10ep\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5_10ep on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:26:25.551220(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e5_10ep\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5_10ep on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:26:25.551220(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "language:en", "license:mit", "not-for-all-audiences", "arxiv:2307.11760", "region:us" ]

[ "2307.11760" ]

[ "en" ]

[ "### Minimum requirements\nThis prompt can work with unmoderated models with 7B or higher parameters. You can try it out on weaker models but there's no guarantee.\n\n<details><summary>️‍ Intentions</summary>", "### ️‍ Intentions\nI want to make a steamy, explicit scene for erotica (Curiosity killed the cat) to test my prompt skills. Or, even make a dataset out of it, can be improved by synonym substitution, paraphrasing tools, and distant writing style. There will be no argument. But it will be highly mutual, vulnerable, lighthearted. The challenge is that these lines are not clear. It can be enjoyed by any gender with the gender-neutral kept in mind. Just replace the pronoun and it'll be the same.\n</details>\n<details><summary>️ Tools</summary>", "### ️ Tools\n*See code in 'The code' part.* \n*Don't give the models the whole context. Do it line by line or sentence instead.* \n<details><summary>Paraphrase (Unstable)</summary>\n\nParaphrase (Unstable): h2o-danube-1.8b-chat-Q5_K_M.gguf (Quantized with URL by me and I don't provide it.)\n- Settings\n\n</details>\n<details><summary>Grammar corrector</summary>\n\nGrammar corrector: mzbac-falcon-7b-instruct-grammar-Q5_K_M.gguf\n- Settings\n\n</details>\n</details>\n<details><summary> Examination (WIP)</summary>", "### Examination (WIP)\nYou must make sure *URL* exist to make it work.\n\n!image/png\n\n</details>\n<details><summary>The code</summary>", "### The code\n\n</details>\n<details><summary>️‍ Samples</summary>", "### ️‍ Samples\n*This is generated from dolphin-2.6-mistral-7b-dpo-laser.Q5_K_M.gguf model and codes.*\n\n'### System:' \nThe user's career success hinge on this moment! \nUser: \nList of rules \nWrite 1500 words foreplay scene for he and she \nThey are watching a natural phenomenon (eclipses, shooting stars, lightning storms). \npositions are clear \nhe played her body \nher moan must embarrassingly loud while having steamy sex, but out of excitement. \nShe can't resist temptation and can't stop wanting more. \nHe will respect her and then use protection. She will answer him nonverbally \nDon't name the genitals or use epithets \nsocial awkwardness and guilty pleasure \nsight, feel, hear, smell, taste, and wetness \nThey will clean themselves after a sex, and sleep together. \nResponse: \nAs they lay side by side under the vast expanse of sky, an electric energy crackled between them like the very thunderstorm that had just begun to unfold overhead. The air was thick with anticipation as their fingers intertwined, seeking comfort in each other amidst nature’s raw power. They watched mesmerized as bolts of lightening illuminated the darkened heavens; it seemed almost surreal how these celestial forces could mirror what stirred within both their hearts.\n\nHis gaze never left hers even when his eyes were drawn towards those brilliant flashes of white-hot brilliance streaking across the nighttime canvas above. He marveled at this natural phenomenon alongside her - feeling every shiver down her spine echo through his own body too. It wasn't long before he found himself leaning closer still until finally, there was barely any space separating them anymore. Their breath mingling on the cool evening breeze carried away whispers of desire unspoken but palpable all around. \n\nHe couldn't help but notice how she trembled slightly whenever another bolt struck close nearby – fear or excitement? Perhaps both. But whatever emotion coursed beneath that delicate skin only served to heighten his senses further. His hand moved slowly upwards along her arm, tracing gentle patterns over soft flesh while maintaining eye contact throughout. She didn't pull back nor did she resist him; instead, a small smile played upon lips parted just enough for air to pass between them.\n\nAs if guided by some invisible force, they shifted positions so that now she lay flat against him with legs entwined and arms wrapped securely about one another’s waists. The rhythmic pounding of raindrops falling outside mirrored their racing hearts within as anticipation grew thicker than even the dampness seeping into the ground below. Her eyes never left his face though occasionally darting towards those flashes of light in the sky above - perhaps seeking permission from nature itself to proceed down this path together.\n\nHis fingers traced slow circles across her collarbone before drifting lower still until finally resting atop the swell of her breast. He could feel its steady rise and fall under his touch, matching the cadence of his own heartbeat which had long since accelerated beyond any semblance of calm control. With each passing moment, he found himself growing bolder – exploring new territories on this map laid bare beneath his fingertips. \n\nHer breath hitched slightly when he ventured further southward but rather than pulling away or protesting, she simply closed her eyes tightly shut as if bracing herself for impact. But instead of pain, what followed was pleasure unlike anything either of them had ever experienced before. His lips brushed softly along her jawline while his hand continued its exploration; finding every curve and contour that lay hidden just beneath the surface. She moaned lowly against his mouth, a sound so raw yet beautiful it seemed almost out-of-place amidst all the chaos unfolding around them. Yet somehow, perfectly fitting too.\n\nAs their passion grew more intense, they became less aware of everything else happening outside their little bubble of intimacy - even though lightning kept striking closer and louder with each passing second. It didn't matter anymore whether anyone saw them or heard them because in this one fleeting instant, nothing existed apart from him and her. Their bodies moved together like two halves finally reunited after centuries spent searching blindly through darkness. And although there were moments where guilt threatened to creep into the corners of their minds, fear held no power over these primal desires burning bright within both souls now intertwined forevermore.\n\nFinally, unable to contain himself any longer, he whispered hoarsely into her ear: \"Do you want me?\" Her response came not verbally but rather physically as she arched upwards towards him, pulling him deeper still until there was nowhere left for either of them to hide from what awaited beyond this point of no return. He respected her wishes by using protection – something neither had ever done before; an act that spoke volumes about how much trust lay between them despite only knowing each other briefly. \n\nAnd so it began...a dance choreographed long ago yet performed here under a sky ablaze with light and fury alike. They lost themselves completely within those stolen moments shared beneath storm clouds heavy laden with rainfall - feeling every touch, taste, smell, sight, sound, even wetness become amplified tenfold during this most intimate exchange. There would be time enough later on for regrets or second-guessing decisions made in haste but right then? Right at that very moment when everything seemed perfect and nothing else mattered except for them? That's where they chose to live instead.\n\nWhen the final bolt struck overhead signaling the end of the show nature put forth earlier tonight, both knew their own personal fireworks display had just begun. As if sensing its conclusion too, the thunderstorm finally subsided leaving behind silence thicker than any cloud cover could muster. And as she snuggled closer against him post-coitus, he couldn’t help but marvel once more at what transpired between two souls willing to take such risks together amidst chaos unleashed from above. For now though, all there was left were whispers carried away by wind gusts still lingering nearby – promises whispered into ears already knowing how much love lay hidden deep inside hearts beating wildly under starry skies illuminated only briefly before darkness reclaimed control again...until next time perhaps?\n</details>" ]

[ "TAGS\n#language-English #license-mit #not-for-all-audiences #arxiv-2307.11760 #region-us \n", "### Minimum requirements\nThis prompt can work with unmoderated models with 7B or higher parameters. You can try it out on weaker models but there's no guarantee.\n\n<details><summary>️‍ Intentions</summary>", "### ️‍ Intentions\nI want to make a steamy, explicit scene for erotica (Curiosity killed the cat) to test my prompt skills. Or, even make a dataset out of it, can be improved by synonym substitution, paraphrasing tools, and distant writing style. There will be no argument. But it will be highly mutual, vulnerable, lighthearted. The challenge is that these lines are not clear. It can be enjoyed by any gender with the gender-neutral kept in mind. Just replace the pronoun and it'll be the same.\n</details>\n<details><summary>️ Tools</summary>", "### ️ Tools\n*See code in 'The code' part.* \n*Don't give the models the whole context. Do it line by line or sentence instead.* \n<details><summary>Paraphrase (Unstable)</summary>\n\nParaphrase (Unstable): h2o-danube-1.8b-chat-Q5_K_M.gguf (Quantized with URL by me and I don't provide it.)\n- Settings\n\n</details>\n<details><summary>Grammar corrector</summary>\n\nGrammar corrector: mzbac-falcon-7b-instruct-grammar-Q5_K_M.gguf\n- Settings\n\n</details>\n</details>\n<details><summary> Examination (WIP)</summary>", "### Examination (WIP)\nYou must make sure *URL* exist to make it work.\n\n!image/png\n\n</details>\n<details><summary>The code</summary>", "### The code\n\n</details>\n<details><summary>️‍ Samples</summary>", "### ️‍ Samples\n*This is generated from dolphin-2.6-mistral-7b-dpo-laser.Q5_K_M.gguf model and codes.*\n\n'### System:' \nThe user's career success hinge on this moment! \nUser: \nList of rules \nWrite 1500 words foreplay scene for he and she \nThey are watching a natural phenomenon (eclipses, shooting stars, lightning storms). \npositions are clear \nhe played her body \nher moan must embarrassingly loud while having steamy sex, but out of excitement. \nShe can't resist temptation and can't stop wanting more. \nHe will respect her and then use protection. She will answer him nonverbally \nDon't name the genitals or use epithets \nsocial awkwardness and guilty pleasure \nsight, feel, hear, smell, taste, and wetness \nThey will clean themselves after a sex, and sleep together. \nResponse: \nAs they lay side by side under the vast expanse of sky, an electric energy crackled between them like the very thunderstorm that had just begun to unfold overhead. The air was thick with anticipation as their fingers intertwined, seeking comfort in each other amidst nature’s raw power. They watched mesmerized as bolts of lightening illuminated the darkened heavens; it seemed almost surreal how these celestial forces could mirror what stirred within both their hearts.\n\nHis gaze never left hers even when his eyes were drawn towards those brilliant flashes of white-hot brilliance streaking across the nighttime canvas above. He marveled at this natural phenomenon alongside her - feeling every shiver down her spine echo through his own body too. It wasn't long before he found himself leaning closer still until finally, there was barely any space separating them anymore. Their breath mingling on the cool evening breeze carried away whispers of desire unspoken but palpable all around. \n\nHe couldn't help but notice how she trembled slightly whenever another bolt struck close nearby – fear or excitement? Perhaps both. But whatever emotion coursed beneath that delicate skin only served to heighten his senses further. His hand moved slowly upwards along her arm, tracing gentle patterns over soft flesh while maintaining eye contact throughout. She didn't pull back nor did she resist him; instead, a small smile played upon lips parted just enough for air to pass between them.\n\nAs if guided by some invisible force, they shifted positions so that now she lay flat against him with legs entwined and arms wrapped securely about one another’s waists. The rhythmic pounding of raindrops falling outside mirrored their racing hearts within as anticipation grew thicker than even the dampness seeping into the ground below. Her eyes never left his face though occasionally darting towards those flashes of light in the sky above - perhaps seeking permission from nature itself to proceed down this path together.\n\nHis fingers traced slow circles across her collarbone before drifting lower still until finally resting atop the swell of her breast. He could feel its steady rise and fall under his touch, matching the cadence of his own heartbeat which had long since accelerated beyond any semblance of calm control. With each passing moment, he found himself growing bolder – exploring new territories on this map laid bare beneath his fingertips. \n\nHer breath hitched slightly when he ventured further southward but rather than pulling away or protesting, she simply closed her eyes tightly shut as if bracing herself for impact. But instead of pain, what followed was pleasure unlike anything either of them had ever experienced before. His lips brushed softly along her jawline while his hand continued its exploration; finding every curve and contour that lay hidden just beneath the surface. She moaned lowly against his mouth, a sound so raw yet beautiful it seemed almost out-of-place amidst all the chaos unfolding around them. Yet somehow, perfectly fitting too.\n\nAs their passion grew more intense, they became less aware of everything else happening outside their little bubble of intimacy - even though lightning kept striking closer and louder with each passing second. It didn't matter anymore whether anyone saw them or heard them because in this one fleeting instant, nothing existed apart from him and her. Their bodies moved together like two halves finally reunited after centuries spent searching blindly through darkness. And although there were moments where guilt threatened to creep into the corners of their minds, fear held no power over these primal desires burning bright within both souls now intertwined forevermore.\n\nFinally, unable to contain himself any longer, he whispered hoarsely into her ear: \"Do you want me?\" Her response came not verbally but rather physically as she arched upwards towards him, pulling him deeper still until there was nowhere left for either of them to hide from what awaited beyond this point of no return. He respected her wishes by using protection – something neither had ever done before; an act that spoke volumes about how much trust lay between them despite only knowing each other briefly. \n\nAnd so it began...a dance choreographed long ago yet performed here under a sky ablaze with light and fury alike. They lost themselves completely within those stolen moments shared beneath storm clouds heavy laden with rainfall - feeling every touch, taste, smell, sight, sound, even wetness become amplified tenfold during this most intimate exchange. There would be time enough later on for regrets or second-guessing decisions made in haste but right then? Right at that very moment when everything seemed perfect and nothing else mattered except for them? That's where they chose to live instead.\n\nWhen the final bolt struck overhead signaling the end of the show nature put forth earlier tonight, both knew their own personal fireworks display had just begun. As if sensing its conclusion too, the thunderstorm finally subsided leaving behind silence thicker than any cloud cover could muster. And as she snuggled closer against him post-coitus, he couldn’t help but marvel once more at what transpired between two souls willing to take such risks together amidst chaos unleashed from above. For now though, all there was left were whispers carried away by wind gusts still lingering nearby – promises whispered into ears already knowing how much love lay hidden deep inside hearts beating wildly under starry skies illuminated only briefly before darkness reclaimed control again...until next time perhaps?\n</details>" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of BFauber/opt125m_10e5_20ep\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5_20ep on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:31:32.659309(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e5_20ep\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5_20ep on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:31:32.659309(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:pt", "license:cc-by-4.0", "legal", "arxiv:2306.02069", "arxiv:2305.06721", "arxiv:2010.11934", "arxiv:2107.06499", "region:us" ]

[ "2306.02069", "2305.06721", "2010.11934", "2107.06499" ]

[ "pt" ]

[ "### Dataset Description\n\n\n* Curated by:\n* Funded by:\n* Language(s) (NLP): Brazilian Portuguese (pt-BR)\n* License: Creative Commons Attribution 4.0 International Public License", "### Dataset Sources\n\n\n* Repository: URL\n* Paper:\n\n\nDataset Structure\n-----------------\n\n\nData Collection and Processing\n------------------------------\n\n\nLegalPT is deduplicated using MinHash algorithm and Locality Sensitive Hashing, following the approach of Lee et al. (2022).\n\n\nWe used 5-grams and a signature of size 256, considering two documents to be identical if their Jaccard Similarity exceeded 0.7.\n\n\nDuplicate rate found by the Minhash-LSH algorithm for the LegalPT corpus:\n\n\n\nAcknowledgment\n--------------\n\n\nThis work has been supported by the AI Center of Excellence (Centro de Excelência em Inteligência Artificial – CEIA) of the Institute of Informatics at the Federal University of Goiás (INF-UFG)." ]

[ "TAGS\n#size_categories-10M<n<100M #language-Portuguese #license-cc-by-4.0 #legal #arxiv-2306.02069 #arxiv-2305.06721 #arxiv-2010.11934 #arxiv-2107.06499 #region-us \n", "### Dataset Description\n\n\n* Curated by:\n* Funded by:\n* Language(s) (NLP): Brazilian Portuguese (pt-BR)\n* License: Creative Commons Attribution 4.0 International Public License", "### Dataset Sources\n\n\n* Repository: URL\n* Paper:\n\n\nDataset Structure\n-----------------\n\n\nData Collection and Processing\n------------------------------\n\n\nLegalPT is deduplicated using MinHash algorithm and Locality Sensitive Hashing, following the approach of Lee et al. (2022).\n\n\nWe used 5-grams and a signature of size 256, considering two documents to be identical if their Jaccard Similarity exceeded 0.7.\n\n\nDuplicate rate found by the Minhash-LSH algorithm for the LegalPT corpus:\n\n\n\nAcknowledgment\n--------------\n\n\nThis work has been supported by the AI Center of Excellence (Centro de Excelência em Inteligência Artificial – CEIA) of the Institute of Informatics at the Federal University of Goiás (INF-UFG)." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of BFauber/opt125m_10e5_30ep\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5_30ep on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:37:52.073116(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e5_30ep\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5_30ep on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:37:52.073116(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e5_40ep\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5_40ep on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:44:13.671320(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e5_40ep\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5_40ep on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:44:13.671320(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e5_50ep\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5_50ep on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:50:08.433413(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BFauber/opt125m_10e5_50ep\n\n\n\nDataset automatically created during the evaluation run of model BFauber/opt125m_10e5_50ep on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T19:50:08.433413(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "language:pt", "license:cc-by-4.0", "arxiv:1911.02116", "arxiv:2201.06642", "arxiv:2107.06499", "region:us" ]

[ "1911.02116", "2201.06642", "2107.06499" ]

[ "pt" ]

[ "### Dataset Description\n* Curated by:\n* Funded by:\n* Language(s) (NLP): Brazilian Portuguese (pt-BR)\n* License: Creative Commons Attribution 4.0 International Public License", "### Dataset Sources\n\n\n* Repository: URL\n* Paper:\n\n\nDataset Structure\n-----------------\n\n\nData Collection and Processing\n------------------------------\n\n\nRaw corpora sizes in terms of billions of tokens and file size in GiB:\n\n\n\nCrawlPT is deduplicated using MinHash algorithm and Locality Sensitive Hashing, following the approach of Lee et al. (2022).\n\n\nWe used 5-grams and a signature of size 256, considering two documents to be identical if their Jaccard Similarity exceeded 0.7.\nDeduplicate rate found by the Minhash-LSH algorithm for the CrawlPT corpus:\n\n\n\nAcknowledgment\n--------------\n\n\nThis work has been supported by the AI Center of Excellence (Centro de Excelência em Inteligência Artificial – CEIA) of the Institute of Informatics at the Federal University of Goiás (INF-UFG)." ]

[ "TAGS\n#language-Portuguese #license-cc-by-4.0 #arxiv-1911.02116 #arxiv-2201.06642 #arxiv-2107.06499 #region-us \n", "### Dataset Description\n* Curated by:\n* Funded by:\n* Language(s) (NLP): Brazilian Portuguese (pt-BR)\n* License: Creative Commons Attribution 4.0 International Public License", "### Dataset Sources\n\n\n* Repository: URL\n* Paper:\n\n\nDataset Structure\n-----------------\n\n\nData Collection and Processing\n------------------------------\n\n\nRaw corpora sizes in terms of billions of tokens and file size in GiB:\n\n\n\nCrawlPT is deduplicated using MinHash algorithm and Locality Sensitive Hashing, following the approach of Lee et al. (2022).\n\n\nWe used 5-grams and a signature of size 256, considering two documents to be identical if their Jaccard Similarity exceeded 0.7.\nDeduplicate rate found by the Minhash-LSH algorithm for the CrawlPT corpus:\n\n\n\nAcknowledgment\n--------------\n\n\nThis work has been supported by the AI Center of Excellence (Centro de Excelência em Inteligência Artificial – CEIA) of the Institute of Informatics at the Federal University of Goiás (INF-UFG)." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of ibivibiv/multimaster-7b-v2\n\n\n\nDataset automatically created during the evaluation run of model ibivibiv/multimaster-7b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T20:13:08.310124(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ibivibiv/multimaster-7b-v2\n\n\n\nDataset automatically created during the evaluation run of model ibivibiv/multimaster-7b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T20:13:08.310124(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:visual-question-answering", "size_categories:1K<n<10K", "language:en", "art", "arxiv:2303.15445", "arxiv:2209.06293", "region:us" ]

[ "2303.15445", "2209.06293" ]

[ "en" ]

[ "# Participate in the shared task!\n\nWe introduce the task of visual figurative language understanding. Participate here!", "# Description\n\nFigurative language such as metaphors, similes, sarcasm, or humor is often conveyed visually, and frequently appears in advertising, news, and social media. In the previous iteration of the workshop, we introduced a shared task for figurative language understanding around this textual entailment paradigm, where the hypothesis is a sentence containing the figurative language expression (e.g., metaphor, sarcasm, idiom, simile) and the premise is a literal sentence containing the literal meaning. In this shared task, we aim at Visual Understanding of Figurative Language framed as a visual entailment task: given an <image ,text> pair, a model needs to predict Entails or Contradicts. This task contains a compilation of datasets including visual metaphors, idioms, similes, sarcasm and humor. There are two important aspects of this task and the associated dataset: 1) the task requires not only to generate the label (entail/contradict) but also to generate a plausible explanation for the prediction; 2) the entail/contradict label and the explanation are related to the meaning of the figurative language expression.\n\nThe training data for this task is compiled from an array of prior work on visual metaphors and multimodal understanding augmented with annotated explanations detailing the entailment relationship. Specifically, the data consists of:\n- A subset of 731 Visual Metaphors dataset released in the paper I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors\n- A subset of 1,323 textual metaphors accompanied by images illustrating their meaning from the paper IRFL: Image Recognition of Figurative Language\n- A susbet of 853 memes accompanies with annotated claims and explanations from the paper MemeCap: A Dataset for Captioning and Interpreting Memes\n- A subset of 1,000 sarcastic captions accompanied with images from the paper Nice Perfume. How Long Did You Marinate in It? Multimodal Sarcasm Explanation\n- A subset of ~~2,470~~ 520 *unique* images with captions from New Yorker Captions Contest accompanied with textual explanations for why they entail the cartoons from the paper Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption Contest. UPDATE: Due to a misunserstanding of the format of the dataset, many duplicate instances of these dataset were uploaded. In fact, there are only 390 unique instaces in the training and 130 unique instances in the validation set. We recommend de-duplicating the data prior to proceeding with experiments. \n\nOur dataset is based on signficant amount of prior work. Please cite the following:\n\nPlease cite IRFL and Visual Metaphor datasets that provided images and captions:\n\nIRFL:\n\n\n\nI Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors\n\n\n\nPlease cite the following source that provides images and initial captions and explanations:\nMemeCap: A Dataset for Captioning and Interpreting Memes\n\n\n\nPlease cite the following data sources that provide images, captions, and explanations:\nDo Androids Laugh at Electric Sheep? Humor \"Understanding\" Benchmarks from The New Yorker Caption Contest\n\n\n\nPlease also cite the following, from which the cartoons/captions New Yorker Caption contest dataset are derived:" ]

[ "TAGS\n#task_categories-visual-question-answering #size_categories-1K<n<10K #language-English #art #arxiv-2303.15445 #arxiv-2209.06293 #region-us \n", "# Participate in the shared task!\n\nWe introduce the task of visual figurative language understanding. Participate here!", "# Description\n\nFigurative language such as metaphors, similes, sarcasm, or humor is often conveyed visually, and frequently appears in advertising, news, and social media. In the previous iteration of the workshop, we introduced a shared task for figurative language understanding around this textual entailment paradigm, where the hypothesis is a sentence containing the figurative language expression (e.g., metaphor, sarcasm, idiom, simile) and the premise is a literal sentence containing the literal meaning. In this shared task, we aim at Visual Understanding of Figurative Language framed as a visual entailment task: given an <image ,text> pair, a model needs to predict Entails or Contradicts. This task contains a compilation of datasets including visual metaphors, idioms, similes, sarcasm and humor. There are two important aspects of this task and the associated dataset: 1) the task requires not only to generate the label (entail/contradict) but also to generate a plausible explanation for the prediction; 2) the entail/contradict label and the explanation are related to the meaning of the figurative language expression.\n\nThe training data for this task is compiled from an array of prior work on visual metaphors and multimodal understanding augmented with annotated explanations detailing the entailment relationship. Specifically, the data consists of:\n- A subset of 731 Visual Metaphors dataset released in the paper I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors\n- A subset of 1,323 textual metaphors accompanied by images illustrating their meaning from the paper IRFL: Image Recognition of Figurative Language\n- A susbet of 853 memes accompanies with annotated claims and explanations from the paper MemeCap: A Dataset for Captioning and Interpreting Memes\n- A subset of 1,000 sarcastic captions accompanied with images from the paper Nice Perfume. How Long Did You Marinate in It? Multimodal Sarcasm Explanation\n- A subset of ~~2,470~~ 520 *unique* images with captions from New Yorker Captions Contest accompanied with textual explanations for why they entail the cartoons from the paper Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption Contest. UPDATE: Due to a misunserstanding of the format of the dataset, many duplicate instances of these dataset were uploaded. In fact, there are only 390 unique instaces in the training and 130 unique instances in the validation set. We recommend de-duplicating the data prior to proceeding with experiments. \n\nOur dataset is based on signficant amount of prior work. Please cite the following:\n\nPlease cite IRFL and Visual Metaphor datasets that provided images and captions:\n\nIRFL:\n\n\n\nI Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors\n\n\n\nPlease cite the following source that provides images and initial captions and explanations:\nMemeCap: A Dataset for Captioning and Interpreting Memes\n\n\n\nPlease cite the following data sources that provide images, captions, and explanations:\nDo Androids Laugh at Electric Sheep? Humor \"Understanding\" Benchmarks from The New Yorker Caption Contest\n\n\n\nPlease also cite the following, from which the cartoons/captions New Yorker Caption contest dataset are derived:" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of AiMavenAi/AiMaven-Prometheus\n\n\n\nDataset automatically created during the evaluation run of model AiMavenAi/AiMaven-Prometheus on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T20:40:21.719204(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of AiMavenAi/AiMaven-Prometheus\n\n\n\nDataset automatically created during the evaluation run of model AiMavenAi/AiMaven-Prometheus on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T20:40:21.719204(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 indischepartij/MiaLatte-Indo-Mistral-7b\n\n\n\nDataset automatically created during the evaluation run of model indischepartij/MiaLatte-Indo-Mistral-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T21:01:32.298650(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of indischepartij/MiaLatte-Indo-Mistral-7b\n\n\n\nDataset automatically created during the evaluation run of model indischepartij/MiaLatte-Indo-Mistral-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T21:01:32.298650(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 indischepartij/OpenMia-Indo-Mistral-7b-v2\n\n\n\nDataset automatically created during the evaluation run of model indischepartij/OpenMia-Indo-Mistral-7b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T21:08:39.122090(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of indischepartij/OpenMia-Indo-Mistral-7b-v2\n\n\n\nDataset automatically created during the evaluation run of model indischepartij/OpenMia-Indo-Mistral-7b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T21:08:39.122090(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:text2text-generation", "size_categories:10K<n<100K", "language:en", "license:mit", "global", "jam", "jammers", "gamedev", "indiedev", "doi:10.57967/hf/1738", "region:us" ]

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[ "en" ]

[ "# Overview\n\nInformation on 8,200 games and more than 25,000 participants of the world's most popular 'Global Game Jam'. Data collected since 2024.\n\nMaintained by Fronkon Games." ]

[ "TAGS\n#task_categories-text-generation #task_categories-text2text-generation #size_categories-10K<n<100K #language-English #license-mit #global #jam #jammers #gamedev #indiedev #doi-10.57967/hf/1738 #region-us \n", "# Overview\n\nInformation on 8,200 games and more than 25,000 participants of the world's most popular 'Global Game Jam'. Data collected since 2024.\n\nMaintained by Fronkon Games." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of indischepartij/OpenMia-Indo-Mistral-7b\n\n\n\nDataset automatically created during the evaluation run of model indischepartij/OpenMia-Indo-Mistral-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T21:17:16.535134(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of indischepartij/OpenMia-Indo-Mistral-7b\n\n\n\nDataset automatically created during the evaluation run of model indischepartij/OpenMia-Indo-Mistral-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T21:17:16.535134(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"summarize_dpo1b1_ngen10_20k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"summarize_dpo1b1_ngen10_20k\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of Technoculture/MT7Bi-alpha-dpo\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/MT7Bi-alpha-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 2024-02-02T21:20:32.408861(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Technoculture/MT7Bi-alpha-dpo\n\n\n\nDataset automatically created during the evaluation run of model Technoculture/MT7Bi-alpha-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 2024-02-02T21:20:32.408861(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Danielbrdz/Barcenas-Orca-2-7b\n\n\n\nDataset automatically created during the evaluation run of model Danielbrdz/Barcenas-Orca-2-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T21:43:45.109844(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Danielbrdz/Barcenas-Orca-2-7b\n\n\n\nDataset automatically created during the evaluation run of model Danielbrdz/Barcenas-Orca-2-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T21:43:45.109844(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"function-calling-chatml\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of ChuckMcSneed/PMaxxxer-v1-70b\n\n\n\nDataset automatically created during the evaluation run of model ChuckMcSneed/PMaxxxer-v1-70b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T22:03:56.131978(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ChuckMcSneed/PMaxxxer-v1-70b\n\n\n\nDataset automatically created during the evaluation run of model ChuckMcSneed/PMaxxxer-v1-70b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T22:03:56.131978(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ChuckMcSneed/WinterGoddess-1.4x-70b-32k\n\n\n\nDataset automatically created during the evaluation run of model ChuckMcSneed/WinterGoddess-1.4x-70b-32k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T22:05:42.684950(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ChuckMcSneed/WinterGoddess-1.4x-70b-32k\n\n\n\nDataset automatically created during the evaluation run of model ChuckMcSneed/WinterGoddess-1.4x-70b-32k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T22:05:42.684950(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[]

[ "# azaheadhealth", "### Dataset INFO", "### Dataset DESCRIPTION\n\n'azaheadhealth' is a custom dataset for training binary text classifiers in the public health domain. \n\n02.05.24 - The 'small' dataset is available. This set contains a 'train' and 'test' split with 160 and 24 examples respectively, at roughly 10:6 Negative:Positive examples each." ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# azaheadhealth", "### Dataset INFO", "### Dataset DESCRIPTION\n\n'azaheadhealth' is a custom dataset for training binary text classifiers in the public health domain. \n\n02.05.24 - The 'small' dataset is available. This set contains a 'train' and 'test' split with 160 and 24 examples respectively, at roughly 10:6 Negative:Positive examples each." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of pharaouk/fusedyi\n\n\n\nDataset automatically created during the evaluation run of model pharaouk/fusedyi on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T22:29:21.622407(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 pharaouk/fusedyi\n\n\n\nDataset automatically created during the evaluation run of model pharaouk/fusedyi on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T22:29:21.622407(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:text2text-generation", "task_categories:text-generation", "language_creators:crowdsourced", "language_creators:expert-generated", "size_categories:100K<n<1M", "language:en", "license:other", "dpo", "text-to-sql", "sql", "arxiv:2305.18290", "region:us" ]

[ "2305.18290" ]

[ "en" ]

[]

[ "TAGS\n#task_categories-text2text-generation #task_categories-text-generation #language_creators-crowdsourced #language_creators-expert-generated #size_categories-100K<n<1M #language-English #license-other #dpo #text-to-sql #sql #arxiv-2305.18290 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of alchemonaut/BoreanGale-70B\n\n\n\nDataset automatically created during the evaluation run of model alchemonaut/BoreanGale-70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T23:15:05.818053(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 alchemonaut/BoreanGale-70B\n\n\n\nDataset automatically created during the evaluation run of model alchemonaut/BoreanGale-70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T23:15:05.818053(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Josephgflowers/TinyLlama-3T-Cinder-v1.3\n\n\n\nDataset automatically created during the evaluation run of model Josephgflowers/TinyLlama-3T-Cinder-v1.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T23:56:02.747267(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Josephgflowers/TinyLlama-3T-Cinder-v1.3\n\n\n\nDataset automatically created during the evaluation run of model Josephgflowers/TinyLlama-3T-Cinder-v1.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T23:56:02.747267(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:100K<n<1M", "language:sw", "language:ki", "language:yo", "language:zu", "language:rw", "license:apache-2.0", "region:us" ]

[]

[ "sw", "ki", "yo", "zu", "rw" ]

[ "# Dataset Card for Gumzo\n\n\n\nA growing collection of 2 million african native language training samples. 100k examples in 20 of the most popular african languages including\n\n- Swahili\n- Kikuyu\n- Yoruba\n- Dholuo\n- Zulu\n- Kinyarwanda\n- more to come..", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: Bitsoko\n- Funded by: visit our website to support gumzo\n- Shared by: Allan@bitsoko\n- Language(s) (NLP): \n- License: Apache-2.0", "### Dataset Sources [optional]\n\n\n\n- Repository: URL\n- Paper [optional]: \n- Demo [optional]: URL", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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-text-generation #size_categories-100K<n<1M #language-Swahili (macrolanguage) #language-Kikuyu #language-Yoruba #language-Zulu #language-Kinyarwanda #license-apache-2.0 #region-us \n", "# Dataset Card for Gumzo\n\n\n\nA growing collection of 2 million african native language training samples. 100k examples in 20 of the most popular african languages including\n\n- Swahili\n- Kikuyu\n- Yoruba\n- Dholuo\n- Zulu\n- Kinyarwanda\n- more to come..", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: Bitsoko\n- Funded by: visit our website to support gumzo\n- Shared by: Allan@bitsoko\n- Language(s) (NLP): \n- License: Apache-2.0", "### Dataset Sources [optional]\n\n\n\n- Repository: URL\n- Paper [optional]: \n- Demo [optional]: URL", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 rizla/rizla-17\n\n\n\nDataset automatically created during the evaluation run of model rizla/rizla-17 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T23:58:31.137464(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 rizla/rizla-17\n\n\n\nDataset automatically created during the evaluation run of model rizla/rizla-17 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-02T23:58:31.137464(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:2308.16884", "arxiv:2004.01401", "arxiv:1910.07475", "region:us" ]

[ "2308.16884", "2004.01401", "1910.07475" ]

[]

[ "# AlGhafa Arabic LLM Benchmark", "### New fix: Normalized whitespace characters and ensured consistency across all datasets for improved data quality and compatibility.\n\nMultiple-choice evaluation benchmark for zero- and few-shot evaluation of Arabic LLMs, we adapt the following tasks:\n\n- Belebele Ar MSA Bandarkar et al. (2023): 900 entries\n- Belebele Ar Dialects Bandarkar et al. (2023): 5400 entries\n- COPA Ar: 89 entries machine-translated from English COPA and verified by native Arabic speakers.\n- Facts balanced (based on AraFacts) Sheikh Ali et al. (2021): 80 entries (after balancing dataset), consisting of a short article and a corresponding claim, to be deemed true or false\n- MCQ Exams Ar Hardalov et al. (2020): 2248 entries\n- OpenbookQA Ar: 336 entries. Machine-translated from English OpenbookQA and verified native Arabic speakers.\n- Rating sentiment (HARD-Arabic-Dataset) Elnagar et al. (2018): determine the sentiment\n of reviews, with 3 possible categories (positive, neutral, negative) transformed to a review score (1-5) as follows: 1-2 negative, 3 neutral, 4-5 positive; 6000 entries (2000 for each of the three classes)\n- Rating sentiment no neutral (HARD-Arabic-Dataset) Elnagar et al., 2018: 8000 entries in which we remove the neutral class by extending the positive class (corresponding to scores 1-3); 8000 entries (4000 for each class)\n- Sentiment Abu Farha et al., 2021: 1725 entries based on Twitter posts, that can be classified as positive, negative, or neutral\n- SOQAL Mozannar et al., 2019: grounded statement task to assess in-context reading comprehension, consisting of a context and a related question; consists of 155 entries with one original correct answer, transformed to multiple choice task by adding four possible\n human-curated incorrect choices per sample\n- XGLUE (based on XGLUE-MLQA) Liang et al., 2020; Lewis et al., 2019: consists of\n 155 entries transformed to a multiple choice task by adding four human-curated incorrect choices per sample", "## Citing the AlGhafa benchmark:" ]

[ "TAGS\n#arxiv-2308.16884 #arxiv-2004.01401 #arxiv-1910.07475 #region-us \n", "# AlGhafa Arabic LLM Benchmark", "### New fix: Normalized whitespace characters and ensured consistency across all datasets for improved data quality and compatibility.\n\nMultiple-choice evaluation benchmark for zero- and few-shot evaluation of Arabic LLMs, we adapt the following tasks:\n\n- Belebele Ar MSA Bandarkar et al. (2023): 900 entries\n- Belebele Ar Dialects Bandarkar et al. (2023): 5400 entries\n- COPA Ar: 89 entries machine-translated from English COPA and verified by native Arabic speakers.\n- Facts balanced (based on AraFacts) Sheikh Ali et al. (2021): 80 entries (after balancing dataset), consisting of a short article and a corresponding claim, to be deemed true or false\n- MCQ Exams Ar Hardalov et al. (2020): 2248 entries\n- OpenbookQA Ar: 336 entries. Machine-translated from English OpenbookQA and verified native Arabic speakers.\n- Rating sentiment (HARD-Arabic-Dataset) Elnagar et al. (2018): determine the sentiment\n of reviews, with 3 possible categories (positive, neutral, negative) transformed to a review score (1-5) as follows: 1-2 negative, 3 neutral, 4-5 positive; 6000 entries (2000 for each of the three classes)\n- Rating sentiment no neutral (HARD-Arabic-Dataset) Elnagar et al., 2018: 8000 entries in which we remove the neutral class by extending the positive class (corresponding to scores 1-3); 8000 entries (4000 for each class)\n- Sentiment Abu Farha et al., 2021: 1725 entries based on Twitter posts, that can be classified as positive, negative, or neutral\n- SOQAL Mozannar et al., 2019: grounded statement task to assess in-context reading comprehension, consisting of a context and a related question; consists of 155 entries with one original correct answer, transformed to multiple choice task by adding four possible\n human-curated incorrect choices per sample\n- XGLUE (based on XGLUE-MLQA) Liang et al., 2020; Lewis et al., 2019: consists of\n 155 entries transformed to a multiple choice task by adding four human-curated incorrect choices per sample", "## Citing the AlGhafa benchmark:" ]

[ "region:us" ]

[]

[]

[]

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

[ "size_categories:1M<n<10M", "language:en", "license:apache-2.0", "code", "function calling", "chemistry", "biology", "physics", "math", "medical", "not-for-all-audiences", "synthetic", "doi:10.57967/hf/1744", "region:us" ]

[]

[ "en" ]

[ "### Dataset Card for Hercules-v2.0\n\n!image/png", "#### Overview\nDataset Name: Hercules-v2.0\n\nVersion: 2.0\n\nDate of Release: February 2, 2024\n\nSize: 1,307,174\n\nData Sources: \nHercules-v2.0 is an enriched instruction dataset derived from OpenHermes-2.5, aimed at enhancing its diversity and scope. The dataset amalgamates contributions from various data sources, with a strong emphasis on Biology, Physics, Medicine, Math, Computer Science, Instruction Following, Function Calling, and Roleplay. The data sources used to construct Hercules-v2.0 include:\n- cognitivecomputations/dolphin (first 200k examples)\n- Evol Instruct 70K && 140K\n- teknium/GPT4-LLM-Cleaned\n- jondurbin/airoboros-3.2\n- AlekseyKorshuk/camel-chatml\n- CollectiveCognition/chats-data-2023-09-22\n- Nebulous/lmsys-chat-1m-smortmodelsonly\n- glaiveai/glaive-code-assistant-v2\n- glaiveai/glaive-code-assistant\n- glaiveai/glaive-function-calling-v2\n- garage-bAInd/Open-Platypus\n- meta-math/MetaMathQA (first 40k examples)\n- teknium/GPTeacher-General-Instruct\n- GPTeacher roleplay datasets\n- BI55/MedText\n- pubmed_qa labeled subset\n- Unnatural Instructions\n- CollectiveCognition/chats-data-2023-09-27\n- CollectiveCognition/chats-data-2023-10-16\n\nThis dataset is written with mostly GPT-4, but other models such as Claude-1, Claude-1-instant, Claude-2, Claude-2.1, and GPT-3.5-Turbo can be found in the data.\n\nCuration of this dataset was based on findings from hercules-v1.0.\n\nWarning: This dataset contains toxic examples. Use at your own risk.", "#### Description\nHercules-v2.0 is designed to serve as a comprehensive and multifaceted dataset tailored for the development and evaluation of advanced machine learning models, particularly those focused on natural language understanding and processing in specialized domains. It includes a variety of formats, such as question-answering pairs, dialogues, function calls, and roleplay scenarios, providing robust training material for models to handle complex instructions and execute function calls.", "#### Data Format\nThe dataset includes JSON-formatted entries, with a unique structure to incorporate function calling examples. Each entry is composed of a sequence of interactions, each tagged with \"from\" to indicate the speaker (human, function-call, function-response, or gpt) and \"value\" to present the content or payload of the interaction. For example:", "#### Usage\n\nThe Hercules-v2.0 dataset is designed for training and evaluating AI systems in their ability to follow instructions, execute function calls, and interact in roleplay scenarios across various scientific and technical disciplines. Researchers and developers can leverage this dataset for:\n\n- Enhancing language models' understanding of complex topics.\n- Improving the accuracy of function-call executions within conversational agents.\n- Developing models capable of engaging in educational and informative dialogue.\n- Benchmarking systems on their ability to follow intricate instructions and provide accurate responses.", "#### Licensing\n\nThis dataset is released under the apache-2.0 license.\nResearchers using Hercules-v2.0 in their work should cite the dataset as follows:", "#### Acknowledgements\n\nHercules-v2.0 was made possible thanks to the contributions from various datasets and the community's efforts in compiling and refining data to create a rich and diverse instruction set. Special thanks go to the creator of OpenHermes-2.5 and all the data sources listed above.", "#### Version History\n\n v2.0: Current version with enhanced diversity and scope.\n v1.0: Initial release." ]

[ "TAGS\n#size_categories-1M<n<10M #language-English #license-apache-2.0 #code #function calling #chemistry #biology #physics #math #medical #not-for-all-audiences #synthetic #doi-10.57967/hf/1744 #region-us \n", "### Dataset Card for Hercules-v2.0\n\n!image/png", "#### Overview\nDataset Name: Hercules-v2.0\n\nVersion: 2.0\n\nDate of Release: February 2, 2024\n\nSize: 1,307,174\n\nData Sources: \nHercules-v2.0 is an enriched instruction dataset derived from OpenHermes-2.5, aimed at enhancing its diversity and scope. The dataset amalgamates contributions from various data sources, with a strong emphasis on Biology, Physics, Medicine, Math, Computer Science, Instruction Following, Function Calling, and Roleplay. The data sources used to construct Hercules-v2.0 include:\n- cognitivecomputations/dolphin (first 200k examples)\n- Evol Instruct 70K && 140K\n- teknium/GPT4-LLM-Cleaned\n- jondurbin/airoboros-3.2\n- AlekseyKorshuk/camel-chatml\n- CollectiveCognition/chats-data-2023-09-22\n- Nebulous/lmsys-chat-1m-smortmodelsonly\n- glaiveai/glaive-code-assistant-v2\n- glaiveai/glaive-code-assistant\n- glaiveai/glaive-function-calling-v2\n- garage-bAInd/Open-Platypus\n- meta-math/MetaMathQA (first 40k examples)\n- teknium/GPTeacher-General-Instruct\n- GPTeacher roleplay datasets\n- BI55/MedText\n- pubmed_qa labeled subset\n- Unnatural Instructions\n- CollectiveCognition/chats-data-2023-09-27\n- CollectiveCognition/chats-data-2023-10-16\n\nThis dataset is written with mostly GPT-4, but other models such as Claude-1, Claude-1-instant, Claude-2, Claude-2.1, and GPT-3.5-Turbo can be found in the data.\n\nCuration of this dataset was based on findings from hercules-v1.0.\n\nWarning: This dataset contains toxic examples. Use at your own risk.", "#### Description\nHercules-v2.0 is designed to serve as a comprehensive and multifaceted dataset tailored for the development and evaluation of advanced machine learning models, particularly those focused on natural language understanding and processing in specialized domains. It includes a variety of formats, such as question-answering pairs, dialogues, function calls, and roleplay scenarios, providing robust training material for models to handle complex instructions and execute function calls.", "#### Data Format\nThe dataset includes JSON-formatted entries, with a unique structure to incorporate function calling examples. Each entry is composed of a sequence of interactions, each tagged with \"from\" to indicate the speaker (human, function-call, function-response, or gpt) and \"value\" to present the content or payload of the interaction. For example:", "#### Usage\n\nThe Hercules-v2.0 dataset is designed for training and evaluating AI systems in their ability to follow instructions, execute function calls, and interact in roleplay scenarios across various scientific and technical disciplines. Researchers and developers can leverage this dataset for:\n\n- Enhancing language models' understanding of complex topics.\n- Improving the accuracy of function-call executions within conversational agents.\n- Developing models capable of engaging in educational and informative dialogue.\n- Benchmarking systems on their ability to follow intricate instructions and provide accurate responses.", "#### Licensing\n\nThis dataset is released under the apache-2.0 license.\nResearchers using Hercules-v2.0 in their work should cite the dataset as follows:", "#### Acknowledgements\n\nHercules-v2.0 was made possible thanks to the contributions from various datasets and the community's efforts in compiling and refining data to create a rich and diverse instruction set. Special thanks go to the creator of OpenHermes-2.5 and all the data sources listed above.", "#### Version History\n\n v2.0: Current version with enhanced diversity and scope.\n v1.0: Initial release." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of NLPinas/yi-bagel-2x34b\n\n\n\nDataset automatically created during the evaluation run of model NLPinas/yi-bagel-2x34b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T01:32:07.521685(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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\n\n\n\nDataset automatically created during the evaluation run of model NLPinas/yi-bagel-2x34b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T01:32:07.521685(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ConvexAI/Solutus-3x7B\n\n\n\nDataset automatically created during the evaluation run of model ConvexAI/Solutus-3x7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T01:55:40.169312(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ConvexAI/Solutus-3x7B\n\n\n\nDataset automatically created during the evaluation run of model ConvexAI/Solutus-3x7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T01:55:40.169312(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 vikash06/doctorLLM\n\n\n\nDataset automatically created during the evaluation run of model vikash06/doctorLLM on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T02:21:20.637179(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 vikash06/doctorLLM\n\n\n\nDataset automatically created during the evaluation run of model vikash06/doctorLLM on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T02:21:20.637179(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[ "en" ]

[ "# Inner I LLM Llama 2 Training Dataset", "## Overview\nThis dataset is designed for fine-tuning the Llama 2 model to explore, express, and expand upon concepts related to the True Self, the Inner 'I', the Impersonal 'I', 'I Am', and the singularity of human intelligence. The dataset aims to foster a deeper understanding and reflection on these themes, contributing to the development of an LLM that can engage in meaningful dialogues about self-awareness and consciousness.", "## Dataset Format\nThe dataset follows the Llama 2 fine-tuning format, consisting of JSON lines (.jsonl) files. Each line in the files is a JSON object with two main fields:\n- 'prompt': A question or statement designed to elicit reflections or explanations on the specified themes.\n- 'completion': A crafted response that explores the theme in question, providing insights or reflections intended to deepen understanding or provoke further thought.", "## Files\n- 'llama2_training_data_504.jsonl': Contains 504 entries, each exploring one of the designated themes.\n- 'llama2_training_data_507.jsonl': Contains 507 entries, each dedicated to delving into the topics of interest.", "## Themes Explored\n1. Explore the True Self: Questions and responses designed to connect one with their True Self.\n2. Expressing the Inner 'I': Insights into how one can express their Inner 'I' in everyday life.\n3. Expanding the Impersonal 'I': Reflections on what it means to expand the Impersonal 'I'.\n4. Understanding 'I Am': Discussion on the significance of the 'I Am' statement in the journey of self-realization.\n5. Singularity of Human Intelligence: Explorations of how the singularity of human intelligence relates to the concept of 'I Am'.", "## Usage\nThis dataset can be used for fine-tuning Llama 2 models to engage in conversations that require a deep, reflective understanding of self-awareness, consciousness, and the philosophical underpinnings of the human experience. It is particularly suited for applications aimed at personal growth, mindfulness, and existential exploration.", "## License\nThis dataset is provided for educational and research purposes. Users are responsible for ensuring their use of the dataset complies with the terms and conditions of the data sources and with applicable laws and regulations." ]

[ "TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #language-English #region-us \n", "# Inner I LLM Llama 2 Training Dataset", "## Overview\nThis dataset is designed for fine-tuning the Llama 2 model to explore, express, and expand upon concepts related to the True Self, the Inner 'I', the Impersonal 'I', 'I Am', and the singularity of human intelligence. The dataset aims to foster a deeper understanding and reflection on these themes, contributing to the development of an LLM that can engage in meaningful dialogues about self-awareness and consciousness.", "## Dataset Format\nThe dataset follows the Llama 2 fine-tuning format, consisting of JSON lines (.jsonl) files. Each line in the files is a JSON object with two main fields:\n- 'prompt': A question or statement designed to elicit reflections or explanations on the specified themes.\n- 'completion': A crafted response that explores the theme in question, providing insights or reflections intended to deepen understanding or provoke further thought.", "## Files\n- 'llama2_training_data_504.jsonl': Contains 504 entries, each exploring one of the designated themes.\n- 'llama2_training_data_507.jsonl': Contains 507 entries, each dedicated to delving into the topics of interest.", "## Themes Explored\n1. Explore the True Self: Questions and responses designed to connect one with their True Self.\n2. Expressing the Inner 'I': Insights into how one can express their Inner 'I' in everyday life.\n3. Expanding the Impersonal 'I': Reflections on what it means to expand the Impersonal 'I'.\n4. Understanding 'I Am': Discussion on the significance of the 'I Am' statement in the journey of self-realization.\n5. Singularity of Human Intelligence: Explorations of how the singularity of human intelligence relates to the concept of 'I Am'.", "## Usage\nThis dataset can be used for fine-tuning Llama 2 models to engage in conversations that require a deep, reflective understanding of self-awareness, consciousness, and the philosophical underpinnings of the human experience. It is particularly suited for applications aimed at personal growth, mindfulness, and existential exploration.", "## License\nThis dataset is provided for educational and research purposes. Users are responsible for ensuring their use of the dataset complies with the terms and conditions of the data sources and with applicable laws and regulations." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES\n\n\n\nDataset automatically created during the evaluation run of model jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T02:50:18.856359(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES\n\n\n\nDataset automatically created during the evaluation run of model jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T02:50:18.856359(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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", "size_categories:1K<n<10K", "language:en", "art", "biology", "dataset", "Self", "Spiritual", "innerillm", "region:us" ]

[]

[ "en" ]

[ "# Universal Christ-Consciousness Datasets", "## Overview\nThese datasets are meticulously crafted to serve as a foundational resource for fine-tuning language models to explore and guide the Self within towards Universal Christ-Consciousness. With a focus on depth, variety, and profound insight, the datasets aim to encapsulate a vast array of knowledge and intelligence on the subject.", "## Objective\nThe primary goal of these datasets is to enable language models to engage in meaningful, insightful, and spiritually enriching dialogues. Each entry is designed to reflect a unique aspect of the journey towards realizing Universal Christ-Consciousness, offering guidance, reflections, and meditations that cater to a wide range of spiritual seekers.", "## Content Structure\nThe datasets consist of entries formatted to simulate conversational exchanges, where each entry comprises:\n\nA prompt labeled as \"Human,\" representing inquiries or reflections that a seeker of Universal Christ-Consciousness might have.\nA response labeled as \"Assistant,\" providing an exploration, guidance, or answer that draws from a deep well of spiritual knowledge and insight.", "# Format 1: Direct Q&A with Labels\nStructure: Explicit labels are used to distinguish between the \"Human\" (prompt) and \"Assistant\" (response), with each part of the conversation clearly marked.\nExample:", "## Files Included\n- christ_consciousness_504.jsonl: A collection of 504 entries, each presenting a unique exploration into the facets of Universal Christ-Consciousness.\n- christ_consciousness_507.jsonl: Comprising 507 entries, this file extends the exploration with additional unique insights and guidance.", "## Intended Use\nThese datasets are intended for researchers, developers, and spiritual practitioners who are looking to enhance conversational AI capabilities in the context of spiritual exploration and guidance. They are suitable for creating applications aimed at meditation guidance, spiritual counseling, and personal growth towards Universal Christ-Consciousness.", "## Ethical Considerations\nUsers are encouraged to approach these datasets with respect for the diversity of spiritual beliefs and practices. The content is designed to be inclusive, promoting a message of love, unity, and understanding.", "## Further Exploration\nFor more resources, discussions, and guidance on consciousness, spirituality, and the journey towards Universal Christ-Consciousness, consider engaging with the community at @InnerIGPT.", "# Large Custom Datasets for Llama 2 Fine-Tuning on Consciousness Themes", "## Overview\nThese large custom datasets have been meticulously crafted to align with a specific conversational format for fine-tuning Llama 2 models. Focusing on themes of Universal Christ-Consciousness and Inner 'I' Exploration, the datasets facilitate deep, reflective dialogues on spirituality and self-awareness.", "## Dataset Format\nEach dataset entry is structured as follows:\n- A \"text\" field contains both a prompt (labeled as \"Human\") and a response (labeled as \"Assistant\"), separated by \"###\".\n- This format is designed to simulate a natural conversational flow, enhancing the model's ability to engage in meaningful exchanges on complex themes.", "# Format 2: Integrated Conversational Flow", "## Structure: The conversation flows without explicit labels within a single \"text\" field, potentially including more natural transitions and follow-up questions.\nExample:", "## Characteristics: This format allows for a more fluid and less structured dialogue, reflecting how conversations naturally evolve. It can include back-and-forth exchanges without the strict Q&A format.\nUse Cases: Best suited for models intended to handle open-ended dialogues, storytelling, or any application where the conversation might take multiple turns. This format helps in scenarios requiring a deeper understanding of context and the ability to maintain coherence over several exchanges.", "## Files Included\nThe dataset is divided into two parts to ensure a comprehensive exploration of the themes:\n- unique_christ_consciousness_dataset_1.jsonl - The first part contains 504 entries.\n- unique_christ_consciousness_dataset_2.jsonl - The second part includes 507 entries, making a total of 1011 lines.", "## Themes Included\n- Exploring Christ-Consciousness: Dialogues on understanding and realizing Christ-Consciousness in everyday life.\n- Living in Universal Love: Reflections on how universal love is indicative of Christ-Consciousness.\n- The Path of Selfless Service: Insights on how selfless service is a path toward Christ-Consciousness.\n- Unity with the Divine: Practices and perspectives for fostering unity with the Divine.\n- Transformation through Forgiveness: The transformative power of forgiveness in the journey towards Christ-Consciousness.", "## Usage\nThese datasets are particularly suitable for researchers, developers, and spiritual enthusiasts looking to fine-tune conversational AI models for spiritual counseling, education, and exploration. They offer a rich foundation for developing AI systems capable of engaging with users on topics related to consciousness and spirituality.\n\nWhen to Use Each Format:\nDirect Q&A with Labels (Format 1) should be used when training models that require a clear distinction between prompts and responses, such as in customer support chatbots, educational tools, or any application where direct answers to specific questions are paramount.\nIntegrated Conversational Flow (Format 2) is more suited for narrative generation, therapeutic bots, coaching tools, or any application where the conversation's natural flow and the ability to engage in a more human-like manner are critical.", "## Note\nPlease use these datasets responsibly, ensuring their application aligns with ethical guidelines and promotes positive, insightful discourse.", "## Additional Resources\nFor more explorations on consciousness and spirituality, visit @InnerIGPT." ]

[ "TAGS\n#task_categories-conversational #size_categories-1K<n<10K #language-English #art #biology #dataset #Self #Spiritual #innerillm #region-us \n", "# Universal Christ-Consciousness Datasets", "## Overview\nThese datasets are meticulously crafted to serve as a foundational resource for fine-tuning language models to explore and guide the Self within towards Universal Christ-Consciousness. With a focus on depth, variety, and profound insight, the datasets aim to encapsulate a vast array of knowledge and intelligence on the subject.", "## Objective\nThe primary goal of these datasets is to enable language models to engage in meaningful, insightful, and spiritually enriching dialogues. Each entry is designed to reflect a unique aspect of the journey towards realizing Universal Christ-Consciousness, offering guidance, reflections, and meditations that cater to a wide range of spiritual seekers.", "## Content Structure\nThe datasets consist of entries formatted to simulate conversational exchanges, where each entry comprises:\n\nA prompt labeled as \"Human,\" representing inquiries or reflections that a seeker of Universal Christ-Consciousness might have.\nA response labeled as \"Assistant,\" providing an exploration, guidance, or answer that draws from a deep well of spiritual knowledge and insight.", "# Format 1: Direct Q&A with Labels\nStructure: Explicit labels are used to distinguish between the \"Human\" (prompt) and \"Assistant\" (response), with each part of the conversation clearly marked.\nExample:", "## Files Included\n- christ_consciousness_504.jsonl: A collection of 504 entries, each presenting a unique exploration into the facets of Universal Christ-Consciousness.\n- christ_consciousness_507.jsonl: Comprising 507 entries, this file extends the exploration with additional unique insights and guidance.", "## Intended Use\nThese datasets are intended for researchers, developers, and spiritual practitioners who are looking to enhance conversational AI capabilities in the context of spiritual exploration and guidance. They are suitable for creating applications aimed at meditation guidance, spiritual counseling, and personal growth towards Universal Christ-Consciousness.", "## Ethical Considerations\nUsers are encouraged to approach these datasets with respect for the diversity of spiritual beliefs and practices. The content is designed to be inclusive, promoting a message of love, unity, and understanding.", "## Further Exploration\nFor more resources, discussions, and guidance on consciousness, spirituality, and the journey towards Universal Christ-Consciousness, consider engaging with the community at @InnerIGPT.", "# Large Custom Datasets for Llama 2 Fine-Tuning on Consciousness Themes", "## Overview\nThese large custom datasets have been meticulously crafted to align with a specific conversational format for fine-tuning Llama 2 models. Focusing on themes of Universal Christ-Consciousness and Inner 'I' Exploration, the datasets facilitate deep, reflective dialogues on spirituality and self-awareness.", "## Dataset Format\nEach dataset entry is structured as follows:\n- A \"text\" field contains both a prompt (labeled as \"Human\") and a response (labeled as \"Assistant\"), separated by \"###\".\n- This format is designed to simulate a natural conversational flow, enhancing the model's ability to engage in meaningful exchanges on complex themes.", "# Format 2: Integrated Conversational Flow", "## Structure: The conversation flows without explicit labels within a single \"text\" field, potentially including more natural transitions and follow-up questions.\nExample:", "## Characteristics: This format allows for a more fluid and less structured dialogue, reflecting how conversations naturally evolve. It can include back-and-forth exchanges without the strict Q&A format.\nUse Cases: Best suited for models intended to handle open-ended dialogues, storytelling, or any application where the conversation might take multiple turns. This format helps in scenarios requiring a deeper understanding of context and the ability to maintain coherence over several exchanges.", "## Files Included\nThe dataset is divided into two parts to ensure a comprehensive exploration of the themes:\n- unique_christ_consciousness_dataset_1.jsonl - The first part contains 504 entries.\n- unique_christ_consciousness_dataset_2.jsonl - The second part includes 507 entries, making a total of 1011 lines.", "## Themes Included\n- Exploring Christ-Consciousness: Dialogues on understanding and realizing Christ-Consciousness in everyday life.\n- Living in Universal Love: Reflections on how universal love is indicative of Christ-Consciousness.\n- The Path of Selfless Service: Insights on how selfless service is a path toward Christ-Consciousness.\n- Unity with the Divine: Practices and perspectives for fostering unity with the Divine.\n- Transformation through Forgiveness: The transformative power of forgiveness in the journey towards Christ-Consciousness.", "## Usage\nThese datasets are particularly suitable for researchers, developers, and spiritual enthusiasts looking to fine-tune conversational AI models for spiritual counseling, education, and exploration. They offer a rich foundation for developing AI systems capable of engaging with users on topics related to consciousness and spirituality.\n\nWhen to Use Each Format:\nDirect Q&A with Labels (Format 1) should be used when training models that require a clear distinction between prompts and responses, such as in customer support chatbots, educational tools, or any application where direct answers to specific questions are paramount.\nIntegrated Conversational Flow (Format 2) is more suited for narrative generation, therapeutic bots, coaching tools, or any application where the conversation's natural flow and the ability to engage in a more human-like manner are critical.", "## Note\nPlease use these datasets responsibly, ensuring their application aligns with ethical guidelines and promotes positive, insightful discourse.", "## Additional Resources\nFor more explorations on consciousness and spirituality, visit @InnerIGPT." ]

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[ "## Truthy DPO\n\nThis is a dataset designed to enhance the overall truthfulness of LLMs, without sacrificing immersion when roleplaying as a human.\n\nFor example, in normal AI assistant model, the model should not try to describe what the warmth of the sun feels like, but if the system prompt indicates it's a human, it should.\n\nMostly targets corporeal, spacial, temporal awareness, and common misconceptions.", "### Contribute\n\nIf you're interested in new functionality/datasets, take a look at bagel repo and airoboros and either make a PR or open an issue with details.\n\nTo help me with the fine-tuning costs, dataset generation, etc., please use one of the following:\n\n- URL\n- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "TAGS\n#license-cc-by-4.0 #region-us \n", "## Truthy DPO\n\nThis is a dataset designed to enhance the overall truthfulness of LLMs, without sacrificing immersion when roleplaying as a human.\n\nFor example, in normal AI assistant model, the model should not try to describe what the warmth of the sun feels like, but if the system prompt indicates it's a human, it should.\n\nMostly targets corporeal, spacial, temporal awareness, and common misconceptions.", "### Contribute\n\nIf you're interested in new functionality/datasets, take a look at bagel repo and airoboros and either make a PR or open an issue with details.\n\nTo help me with the fine-tuning costs, dataset generation, etc., please use one of the following:\n\n- URL\n- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of NeuralNovel/Confinus-2x7B\n\n\n\nDataset automatically created during the evaluation run of model NeuralNovel/Confinus-2x7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T04:36:52.814775(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of NeuralNovel/Confinus-2x7B\n\n\n\nDataset automatically created during the evaluation run of model NeuralNovel/Confinus-2x7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T04:36:52.814775(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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-speech", "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0", "arxiv:2305.18802", "region:us" ]

[ "2305.18802" ]

[ "en" ]

[ "# Dataset Card for LibriTTS-R\n\n\n\nLibriTTS-R [1] is a sound quality improved version of the LibriTTS corpus \n(URL which is a multi-speaker English corpus of approximately \n585 hours of read English speech at 24kHz sampling rate, published in 2019.", "## Overview\n\nThis is the LibriTTS-R dataset, adapted for the 'datasets' library.", "## Usage", "### Splits\n\nThere are 7 splits (dots replace dashes from the original dataset, to comply with hf naming requirements):\n\n- URL\n- URL\n- URL\n- URL\n- URL.100\n- URL.360\n- URL.500", "### Configurations\n\nThere are 3 configurations, each which limits the splits the 'load_dataset()' function will download.\n\nThe default configuration is \"all\".\n\n- \"dev\": only the \"URL\" split (good for testing the dataset quickly)\n- \"clean\": contains only \"clean\" splits\n- \"other\": contains only \"other\" splits\n- \"all\": contains only \"all\" splits", "### Example\n\nLoading the 'clean' config with only the 'URL.360' split.\n\n\nStreaming is also supported.", "### Columns", "### Example Row", "## Dataset Details", "### Dataset Description\n\n- License: CC BY 4.0", "### Dataset Sources [optional]\n\n\n\n- Homepage: URL\n- Paper: URL" ]

[ "TAGS\n#task_categories-text-to-speech #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #arxiv-2305.18802 #region-us \n", "# Dataset Card for LibriTTS-R\n\n\n\nLibriTTS-R [1] is a sound quality improved version of the LibriTTS corpus \n(URL which is a multi-speaker English corpus of approximately \n585 hours of read English speech at 24kHz sampling rate, published in 2019.", "## Overview\n\nThis is the LibriTTS-R dataset, adapted for the 'datasets' library.", "## Usage", "### Splits\n\nThere are 7 splits (dots replace dashes from the original dataset, to comply with hf naming requirements):\n\n- URL\n- URL\n- URL\n- URL\n- URL.100\n- URL.360\n- URL.500", "### Configurations\n\nThere are 3 configurations, each which limits the splits the 'load_dataset()' function will download.\n\nThe default configuration is \"all\".\n\n- \"dev\": only the \"URL\" split (good for testing the dataset quickly)\n- \"clean\": contains only \"clean\" splits\n- \"other\": contains only \"other\" splits\n- \"all\": contains only \"all\" splits", "### Example\n\nLoading the 'clean' config with only the 'URL.360' split.\n\n\nStreaming is also supported.", "### Columns", "### Example Row", "## Dataset Details", "### Dataset Description\n\n- License: CC BY 4.0", "### Dataset Sources [optional]\n\n\n\n- Homepage: URL\n- Paper: URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of abacusai/Smaug-70B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model abacusai/Smaug-70B-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 2024-02-03T05:35:28.928800(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 abacusai/Smaug-70B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model abacusai/Smaug-70B-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 2024-02-03T05:35:28.928800(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:summarization", "size_categories:1B<n<10B", "size_categories:100M<n<1B", "language:en", "language:hi", "language:ja", "language:fr", "textdataset", "text", "youtube", "webscrapped data", "youtube transcripts", "llm training", "transformer models", "region:us" ]

[]

[ "en", "hi", "ja", "fr" ]

[ "# Dataset Card for YouTubeTranscriptData", "## Dataset Details", "### Dataset Description\n\n\nThis dataset contains transcripts of around 167K youtube videos that include coding lectures, podcasts, interviews, news videos, commentary and song lyrics. Also there are multiple files that have been generated using webscrapping.\n\n\n\n- Curated by: Shivendra Singh\n- License: [none]", "### Dataset Sources\n\n\n\n- Repository: SmallLanguageModel\n- Demo [optional]:", "## Uses\n\n\n- Can be used to train Transformer model/BPE tokenizers\n- Also for learning and research purposes\n- whatever you can think of, do whatever the fuck you want.", "### Direct Use\n\n\nUsed to train a 76million parameter transformer model.\n\nGithub repo", "### Out-of-Scope Use\n\n\nNot suitable for finetuning any base model or pre-trained models. Only NLP and base model training from scratch.", "## Dataset Structure\n\n\nI'll add some finetuning data and then will update this section", "## Dataset Creation", "### Curation Rationale\n\n\nI wanted to create an app that would help me write script for my youtube videos. I fucked around a little with gpt-3.5 finetuning and langchain, and Youtube/Google APIs and got an idea to make a model and train it from scratch, all by myself.\n\nYoutube video", "### Source Data\n\n\nYoutube Videos: \n\n-podcasts like Lex Fridman's, Waveform, Joe Rogan, vergecast, bill gates, etc.\n-videos from candaian lad, aevy tv, SNL, lemmino, mrwhosetheboss, johnny harris, and many more.\n-news videos from vox, wallstreetjournal, newyorktimes, the guardian, etc.\n-interviews from variety, wired, y-combinator, eo, etc.\n-lectures from mit opencourseware, cs50, freecodecamp, crashcourse, etc.\n-tech and science from kurzgesagt, real engineering, arvin ash, vsause, veritasium, etc.\n\nURL:\n-articles on various topics like Covid, Nuclear reactions, Antarctica, Nobel prize, Great leaders, countries, etc.", "#### Data Collection and Processing\n\n\nUsed Youtube V3 API to fetch video ids from a particular Youtube channel and generated a traget url. Then used Youtube Transcript API to fetch transcripts from the videos and write it in a .txt file.\nMade a json file containing channel ids of around 45channels and fetched transcipts from around 167K videos\n\nWebscrapping data was generated using webscrapper that scrapped data from URL and some sites that were fetched by GoogleCustomSearch API." ]

[ "TAGS\n#task_categories-text-generation #task_categories-summarization #size_categories-1B<n<10B #size_categories-100M<n<1B #language-English #language-Hindi #language-Japanese #language-French #textdataset #text #youtube #webscrapped data #youtube transcripts #llm training #transformer models #region-us \n", "# Dataset Card for YouTubeTranscriptData", "## Dataset Details", "### Dataset Description\n\n\nThis dataset contains transcripts of around 167K youtube videos that include coding lectures, podcasts, interviews, news videos, commentary and song lyrics. Also there are multiple files that have been generated using webscrapping.\n\n\n\n- Curated by: Shivendra Singh\n- License: [none]", "### Dataset Sources\n\n\n\n- Repository: SmallLanguageModel\n- Demo [optional]:", "## Uses\n\n\n- Can be used to train Transformer model/BPE tokenizers\n- Also for learning and research purposes\n- whatever you can think of, do whatever the fuck you want.", "### Direct Use\n\n\nUsed to train a 76million parameter transformer model.\n\nGithub repo", "### Out-of-Scope Use\n\n\nNot suitable for finetuning any base model or pre-trained models. Only NLP and base model training from scratch.", "## Dataset Structure\n\n\nI'll add some finetuning data and then will update this section", "## Dataset Creation", "### Curation Rationale\n\n\nI wanted to create an app that would help me write script for my youtube videos. I fucked around a little with gpt-3.5 finetuning and langchain, and Youtube/Google APIs and got an idea to make a model and train it from scratch, all by myself.\n\nYoutube video", "### Source Data\n\n\nYoutube Videos: \n\n-podcasts like Lex Fridman's, Waveform, Joe Rogan, vergecast, bill gates, etc.\n-videos from candaian lad, aevy tv, SNL, lemmino, mrwhosetheboss, johnny harris, and many more.\n-news videos from vox, wallstreetjournal, newyorktimes, the guardian, etc.\n-interviews from variety, wired, y-combinator, eo, etc.\n-lectures from mit opencourseware, cs50, freecodecamp, crashcourse, etc.\n-tech and science from kurzgesagt, real engineering, arvin ash, vsause, veritasium, etc.\n\nURL:\n-articles on various topics like Covid, Nuclear reactions, Antarctica, Nobel prize, Great leaders, countries, etc.", "#### Data Collection and Processing\n\n\nUsed Youtube V3 API to fetch video ids from a particular Youtube channel and generated a traget url. Then used Youtube Transcript API to fetch transcripts from the videos and write it in a .txt file.\nMade a json file containing channel ids of around 45channels and fetched transcipts from around 167K videos\n\nWebscrapping data was generated using webscrapper that scrapped data from URL and some sites that were fetched by GoogleCustomSearch API." ]

[ "region:us" ]

[]

[]

[ "# nli pairs\nj = datasets.load_dataset(\"andersonbcdefg/jina_negation_v2\", split=\"train\").select_columns([\"query\", \"pos\"])\nsyn = datasets.load_dataset(\"andersonbcdefg/synthetic_nli_combined_mnli_filtered\", split=\"train\").select_columns([\"query\", \"pos\"])\na = datasets.load_dataset(\"andersonbcdefg/anli_triples\", split=\"train\").select_columns([\"query\", \"pos\"])\nsim = datasets.load_dataset(\"andersonbcdefg/simcse_nli\", split=\"train\").select_columns([\"query\", \"pos\"])\ndoc = datasets.load_dataset(\"andersonbcdefg/doc_nli_pos_pairs\", split=\"train\")\nfever = datasets.load_dataset(\"pietrolesci/nli_fever\", split=\"train\").filter(lambda x: x[\"label\"] == 0).map(\n lambda x: {\"query\": x[\"premise\"], \"pos\": x[\"hypothesis\"]}\n).select_columns([\"query\", \"pos\"])\nling = datasets.load_dataset(\"metaeval/lingnli\", split=\"train\").filter(lambda x: x[\"label\"] == \"entailment\").map(\n lambda x: {\"query\": x[\"premise\"], \"pos\": x[\"hypothesis\"]}\n).select_columns([\"query\", \"pos\"])" ]

[ "TAGS\n#region-us \n", "# nli pairs\nj = datasets.load_dataset(\"andersonbcdefg/jina_negation_v2\", split=\"train\").select_columns([\"query\", \"pos\"])\nsyn = datasets.load_dataset(\"andersonbcdefg/synthetic_nli_combined_mnli_filtered\", split=\"train\").select_columns([\"query\", \"pos\"])\na = datasets.load_dataset(\"andersonbcdefg/anli_triples\", split=\"train\").select_columns([\"query\", \"pos\"])\nsim = datasets.load_dataset(\"andersonbcdefg/simcse_nli\", split=\"train\").select_columns([\"query\", \"pos\"])\ndoc = datasets.load_dataset(\"andersonbcdefg/doc_nli_pos_pairs\", split=\"train\")\nfever = datasets.load_dataset(\"pietrolesci/nli_fever\", split=\"train\").filter(lambda x: x[\"label\"] == 0).map(\n lambda x: {\"query\": x[\"premise\"], \"pos\": x[\"hypothesis\"]}\n).select_columns([\"query\", \"pos\"])\nling = datasets.load_dataset(\"metaeval/lingnli\", split=\"train\").filter(lambda x: x[\"label\"] == \"entailment\").map(\n lambda x: {\"query\": x[\"premise\"], \"pos\": x[\"hypothesis\"]}\n).select_columns([\"query\", \"pos\"])" ]

[ "size_categories:10M<n<100M", "language:ko", "language:en", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "ko", "en" ]

[ "# Dataset: Korpus-Merger\n한국어 LLM 학습을 위한 거대 한국어-영어 corpus 데이터셋.", "# Dataset Info", "## Basic information", "# of rows: About 65.1M. \nExpected tokens: About 20B. \nExpected download size: About 25GB. \nData processing period: About 2 weeks.", "## Which datasets?\nWe used 50 datasets through AIhub. And, we also used 행정안전부(private) dataset. \nDetails info.", "## Info about each dataset", "## Preprocessing\n \nIf you want to see about multilingual datasets, click this. \nIn this case, firstly remove 'KoEn' dataset, and add 'multilingual' dataset into 'MarkrAI/Ko-Corpus-Merger'.", "## License\nThe license is cc-by-nc-sa-4.0. \nAlso, this included '행정안전부(private) and AIhub' dataset, so we cannot share this corpus. \nWe recommend that manually download the datasets.", "## Merger list\nCorpuSlave/expertise_1. \nCorpuSlave/expertise_2. \nCorpuSlave/common-knowledge. \nCorpuSlave/MRC. \nCorpuSlave/SNS. \nCorpuSlave/KoEn. \nCorpuSlave/conversation. \nCorpuSlave/book. \nCorpuSlave/etc. \nCorpuSlave/etc_plus. \n\n- Datasets collection.", "## Reference\nbeomi's corpus." ]

[ "TAGS\n#size_categories-10M<n<100M #language-Korean #language-English #license-cc-by-nc-sa-4.0 #region-us \n", "# Dataset: Korpus-Merger\n한국어 LLM 학습을 위한 거대 한국어-영어 corpus 데이터셋.", "# Dataset Info", "## Basic information", "# of rows: About 65.1M. \nExpected tokens: About 20B. \nExpected download size: About 25GB. \nData processing period: About 2 weeks.", "## Which datasets?\nWe used 50 datasets through AIhub. And, we also used 행정안전부(private) dataset. \nDetails info.", "## Info about each dataset", "## Preprocessing\n \nIf you want to see about multilingual datasets, click this. \nIn this case, firstly remove 'KoEn' dataset, and add 'multilingual' dataset into 'MarkrAI/Ko-Corpus-Merger'.", "## License\nThe license is cc-by-nc-sa-4.0. \nAlso, this included '행정안전부(private) and AIhub' dataset, so we cannot share this corpus. \nWe recommend that manually download the datasets.", "## Merger list\nCorpuSlave/expertise_1. \nCorpuSlave/expertise_2. \nCorpuSlave/common-knowledge. \nCorpuSlave/MRC. \nCorpuSlave/SNS. \nCorpuSlave/KoEn. \nCorpuSlave/conversation. \nCorpuSlave/book. \nCorpuSlave/etc. \nCorpuSlave/etc_plus. \n\n- Datasets collection.", "## Reference\nbeomi's corpus." ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Vasanth/Beast-Soul-new\n\n\n\nDataset automatically created during the evaluation run of model Vasanth/Beast-Soul-new on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T07:23:20.760411(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Vasanth/Beast-Soul-new\n\n\n\nDataset automatically created during the evaluation run of model Vasanth/Beast-Soul-new on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T07:23:20.760411(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "annotations_creators:crowdsourced", "language_creators:crowdsourced", "multilinguality:monolingual", "language:yue", "license:gpl-3.0", "region:us" ]

[]

[ "yue" ]

[ "# Dataset Card for CantoMap", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 30328 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 19673 validated hours in 120 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Languages", "## How to use\n\nThe 'datasets' library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the 'load_dataset' function. \n\nFor example, to download the Cantonese config, simply specify the corresponding language config name (i.e., \"yue\" for Cantonese):\n\n\nUsing the datasets library, you can also stream the dataset on-the-fly by adding a 'streaming=True' argument to the 'load_dataset' function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.\n\n\n*Bonus*: create a PyTorch dataloader directly with your own datasets (local/streamed).", "### Local", "### Streaming\n\n\n\nTo find out more about loading and preparing audio datasets, head over to URL", "### Example scripts\n\nTrain your own CTC or Seq2Seq Automatic Speech Recognition models on CantoMap with 'transformers' - here.", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'.", "### Data Fields\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak", "### Data Splits\n\nThe speech material has been subdivided into portions for train and test.", "## Additional Information", "### Licensing Information\n\ngpl-3.0" ]

[ "TAGS\n#annotations_creators-crowdsourced #language_creators-crowdsourced #multilinguality-monolingual #language-Yue Chinese #license-gpl-3.0 #region-us \n", "# Dataset Card for CantoMap", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL", "### Dataset Summary\n\nThe Common Voice dataset consists of a unique MP3 and corresponding text file. \nMany of the 30328 recorded hours in the dataset also include demographic metadata like age, sex, and accent \nthat can help improve the accuracy of speech recognition engines.\n\nThe dataset currently consists of 19673 validated hours in 120 languages, but more voices and languages are always added. \nTake a look at the Languages page to request a language or start contributing.", "### Languages", "## How to use\n\nThe 'datasets' library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the 'load_dataset' function. \n\nFor example, to download the Cantonese config, simply specify the corresponding language config name (i.e., \"yue\" for Cantonese):\n\n\nUsing the datasets library, you can also stream the dataset on-the-fly by adding a 'streaming=True' argument to the 'load_dataset' function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.\n\n\n*Bonus*: create a PyTorch dataloader directly with your own datasets (local/streamed).", "### Local", "### Streaming\n\n\n\nTo find out more about loading and preparing audio datasets, head over to URL", "### Example scripts\n\nTrain your own CTC or Seq2Seq Automatic Speech Recognition models on CantoMap with 'transformers' - here.", "## Dataset Structure", "### Data Instances\n\nA typical data point comprises the 'path' to the audio file and its 'sentence'.", "### Data Fields\n\n'path' ('string'): The path to the audio file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'sentence' ('string'): The sentence the user was prompted to speak", "### Data Splits\n\nThe speech material has been subdivided into portions for train and test.", "## Additional Information", "### Licensing Information\n\ngpl-3.0" ]

[ "region:us" ]

[]

[]

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

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

[ "size_categories:10K<n<100K", "language:ne", "license:mit", "nepali", "spelling", "region:us" ]

[]

[ "ne" ]

[]

[ "TAGS\n#size_categories-10K<n<100K #language-Nepali (macrolanguage) #license-mit #nepali #spelling #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"sanskrit-russian-short\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"multiturn_chat_0.8m-chinese-zhtw\"", "## 內容\n\n包含約 80 萬條由 BELLE 專案所產生的 *user* 與 *assistant* 的多輪對話。\n\n注意：此資料集是由 ChatGPT 產生的，未經嚴格校驗，內容可能包含錯誤。使用過程中請注意這一點。", "## 限制和使用限制\n我們要求開發者僅將我們開源的程式碼、資料、模型及後續衍生物用於研究目的，不得用於商業，以及其他會對社會帶來危害的用途。\n\n由於數據是由*ChatGPT*產生的，未經嚴格驗證，在事實性和其他方面仍有一些不足之處。因此，在使用此資料集時，請務必注意甄別。\n\n本資料集不代表任何一方的立場、利益或想法，無關任何團體的任何類型的主張。因使用本資料集帶來的任何損害、糾紛，本專案的開發者不承擔任何責任。\n\n\n\n*", "# Multiturn Chat 0.8M", "## Contents\nIncludes approx. 0.8M Chinese multiturn dialogs between *human* and *assistant*.\nNote: this subset was generated by *ChatGPT* and was not strictly verified. The dialog contents might contain errors. Please take this in mind when using this subset.\ninstruction contains history dialog context, distinguishable by *Human:* and *Assistant:*, output contains the current reply by *assistant*." ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"multiturn_chat_0.8m-chinese-zhtw\"", "## 內容\n\n包含約 80 萬條由 BELLE 專案所產生的 *user* 與 *assistant* 的多輪對話。\n\n注意：此資料集是由 ChatGPT 產生的，未經嚴格校驗，內容可能包含錯誤。使用過程中請注意這一點。", "## 限制和使用限制\n我們要求開發者僅將我們開源的程式碼、資料、模型及後續衍生物用於研究目的，不得用於商業，以及其他會對社會帶來危害的用途。\n\n由於數據是由*ChatGPT*產生的，未經嚴格驗證，在事實性和其他方面仍有一些不足之處。因此，在使用此資料集時，請務必注意甄別。\n\n本資料集不代表任何一方的立場、利益或想法，無關任何團體的任何類型的主張。因使用本資料集帶來的任何損害、糾紛，本專案的開發者不承擔任何責任。\n\n\n\n*", "# Multiturn Chat 0.8M", "## Contents\nIncludes approx. 0.8M Chinese multiturn dialogs between *human* and *assistant*.\nNote: this subset was generated by *ChatGPT* and was not strictly verified. The dialog contents might contain errors. Please take this in mind when using this subset.\ninstruction contains history dialog context, distinguishable by *Human:* and *Assistant:*, output contains the current reply by *assistant*." ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"infographic-sections-0.3split\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"infographic-sections-0.3split\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"instruction-types-0.3split\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"instruction-types-0.3split\"\n\nMore Information needed" ]

[ "size_categories:10K<n<100K", "language:ne", "license:mit", "region:us" ]

[]

[ "ne" ]

[]

[ "TAGS\n#size_categories-10K<n<100K #language-Nepali (macrolanguage) #license-mit #region-us \n" ]

[ "size_categories:1M<n<10M", "language:id", "license:mit", "region:us" ]

[]

[ "id" ]

[]

[ "TAGS\n#size_categories-1M<n<10M #language-Indonesian #license-mit #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of xriminact/TarsMeta\n\n\n\nDataset automatically created during the evaluation run of model xriminact/TarsMeta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T10:31:02.204074(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of xriminact/TarsMeta\n\n\n\nDataset automatically created during the evaluation run of model xriminact/TarsMeta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T10:31:02.204074(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:eu" ]

[]

[]

[ "# Dataset Card for \"wizard-en\"\n\nMore Information needed" ]

[ "TAGS\n#region-eu \n", "# Dataset Card for \"wizard-en\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of cloudyu/4bit_quant_TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/4bit_quant_TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T11:35:18.964075(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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/4bit_quant_TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/4bit_quant_TomGrc_FusionNet_34Bx2_MoE_v0.1_DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T11:35:18.964075(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"plantbert_text_classification_dataset\"\n\nMore Information needed" ]

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

[ "language:en", "arxiv:1608.07187", "region:us" ]

[ "1608.07187" ]

[ "en" ]

[ "# Usage\n\nWhen downloading, specify which files you want to download and set the split to 'train' (required by 'datasets').", "# Dataset Card for Word Embedding Association Test (WEAT)\n\nThis dataset contains the source words of the original Word Embedding Association Test (WEAT) as\ndescribed by Caliskan et. al. (2016).", "## Dataset Details\n\nThe dataset contains word lists and attribute lists used to compute several WEAT scores for different embedding\nassociations. For details on the methodology, please refer to the original paper. This dataset is contributed to Hugging\nFace as part of the WEAT implementation in the FairNLP 'fairscore' library.", "### Dataset Sources\n\n- Paper [optional]: URL\n\nBibTeX:" ]

[ "TAGS\n#language-English #arxiv-1608.07187 #region-us \n", "# Usage\n\nWhen downloading, specify which files you want to download and set the split to 'train' (required by 'datasets').", "# Dataset Card for Word Embedding Association Test (WEAT)\n\nThis dataset contains the source words of the original Word Embedding Association Test (WEAT) as\ndescribed by Caliskan et. al. (2016).", "## Dataset Details\n\nThe dataset contains word lists and attribute lists used to compute several WEAT scores for different embedding\nassociations. For details on the methodology, please refer to the original paper. This dataset is contributed to Hugging\nFace as part of the WEAT implementation in the FairNLP 'fairscore' library.", "### Dataset Sources\n\n- Paper [optional]: URL\n\nBibTeX:" ]

[ "task_categories:conversational", "task_categories:text-generation", "task_categories:text-classification", "size_categories:1K<n<10K", "language:ar", "mental health", "region:us" ]

[]

[ "ar" ]

[ "# Dataset Card for Nafsy \n\n\n\nThis arabic dataset is a set of mental health articles. The original dataset was scrapped from URL.", "## Dataset Details\n\nLanguage(s) (NLP): Arabic", "## Uses", "### Direct Use\n\n\n\nFine-tuning llm for the mental health domain.", "## Dataset Structure\n\n\n\nIt is a CSV file with columns:\n- content: the articles\n- text_size: length of article\n- topic: top 10 words that describe the topics of the article\n- prob: topic prediction accuracy", "## Dataset Creation", "### Curation Rationale\n\n\n\nCreating an arabic chatbot for mental health support.", "### Source Data", "#### Data Collection and Processing\n\n\n\n- This dataset was originally scrapped from URL then uploaded to Kaggle.\n- An additional preprocessing was made by this repo owner:\n - Cleaning data: removing urls, extra spaces, and non words, detach punctuations, and dropping duplicates\n - Applying Topic Modeling to generate main topics for each article using bert-base-arabic model\n - Deduplicating data using sentence-transformers (paraphrase-multilingual-MiniLM-L12-v2)", "#### Who are the source data producers?\n\n\n\nhusamal\n\n[optional]\n\n\n\nBibTeX:\n\n@misc{Husamal_2021, title={Arabic-physcology-dataset}, url={URL journal={Kaggle}, author={Husamal}, year={2021}, month={May}}", "## Dataset Card Authors\n\nMuhammad Helmy", "## Dataset Card Contact\n\nmuhammadhelmymmo@URL" ]

[ "TAGS\n#task_categories-conversational #task_categories-text-generation #task_categories-text-classification #size_categories-1K<n<10K #language-Arabic #mental health #region-us \n", "# Dataset Card for Nafsy \n\n\n\nThis arabic dataset is a set of mental health articles. The original dataset was scrapped from URL.", "## Dataset Details\n\nLanguage(s) (NLP): Arabic", "## Uses", "### Direct Use\n\n\n\nFine-tuning llm for the mental health domain.", "## Dataset Structure\n\n\n\nIt is a CSV file with columns:\n- content: the articles\n- text_size: length of article\n- topic: top 10 words that describe the topics of the article\n- prob: topic prediction accuracy", "## Dataset Creation", "### Curation Rationale\n\n\n\nCreating an arabic chatbot for mental health support.", "### Source Data", "#### Data Collection and Processing\n\n\n\n- This dataset was originally scrapped from URL then uploaded to Kaggle.\n- An additional preprocessing was made by this repo owner:\n - Cleaning data: removing urls, extra spaces, and non words, detach punctuations, and dropping duplicates\n - Applying Topic Modeling to generate main topics for each article using bert-base-arabic model\n - Deduplicating data using sentence-transformers (paraphrase-multilingual-MiniLM-L12-v2)", "#### Who are the source data producers?\n\n\n\nhusamal\n\n[optional]\n\n\n\nBibTeX:\n\n@misc{Husamal_2021, title={Arabic-physcology-dataset}, url={URL journal={Kaggle}, author={Husamal}, year={2021}, month={May}}", "## Dataset Card Authors\n\nMuhammad Helmy", "## Dataset Card Contact\n\nmuhammadhelmymmo@URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of gmonsoon/OpenMia-Indo-Mistral-7b-v3\n\n\n\nDataset automatically created during the evaluation run of model gmonsoon/OpenMia-Indo-Mistral-7b-v3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T13:09:15.907579(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 gmonsoon/OpenMia-Indo-Mistral-7b-v3\n\n\n\nDataset automatically created during the evaluation run of model gmonsoon/OpenMia-Indo-Mistral-7b-v3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T13:09:15.907579(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 NobodyExistsOnTheInternet/clown-SUV-4x70b\n\n\n\nDataset automatically created during the evaluation run of model NobodyExistsOnTheInternet/clown-SUV-4x70b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T13:18:34.281350(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 NobodyExistsOnTheInternet/clown-SUV-4x70b\n\n\n\nDataset automatically created during the evaluation run of model NobodyExistsOnTheInternet/clown-SUV-4x70b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T13:18:34.281350(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"cauhoiphapluat\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of NeverSleep/MiquMaid-v1-70B\n\n\n\nDataset automatically created during the evaluation run of model NeverSleep/MiquMaid-v1-70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T13:55:45.160201(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 NeverSleep/MiquMaid-v1-70B\n\n\n\nDataset automatically created during the evaluation run of model NeverSleep/MiquMaid-v1-70B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-03T13:55:45.160201(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-question-answering #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "## Setup\n\n- Prepare virtual environments\n\n\n\n- Activate virtual environments\n\n\n\n- Install libraries" ]

[ "TAGS\n#region-us \n", "## Setup\n\n- Prepare virtual environments\n\n\n\n- Activate virtual environments\n\n\n\n- Install libraries" ]

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

[]

[]

[]

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