Datasets:

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ftopal

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huggingface-datasets

like 0

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of giraffe176/Open_Hermes_Orca_Mistral-7B\n\n\n\nDataset automatically created during the evaluation run of model giraffe176/Open_Hermes_Orca_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 2024-02-11T08:58:14.779696(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 giraffe176/Open_Hermes_Orca_Mistral-7B\n\n\n\nDataset automatically created during the evaluation run of model giraffe176/Open_Hermes_Orca_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 2024-02-11T08:58:14.779696(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:automatic-speech-recognition", "task_categories:text-to-speech", "size_categories:100K<n<1M", "language:vi", "license:cc-by-4.0", "region:us" ]

[]

[ "vi" ]

[ "# unofficial mirror of InfoRe Technology public dataset №2\n\nofficial announcement: URL\n\n415h, 315k samples, vietnamese audiobooks of chinese wǔxiá 武俠 & xiānxiá 仙俠\n\nbộ dữ liệu bóc ra từ YouTube đọc truyện võ hiệp & tiên hiệp, áp dụng kĩ thuật đối chiếu văn bản để dán nhãn tự động\n\nofficial download: 'magnet:?xt=urn:btih:41f1290325ecb6f1230ecdff2441527c9cd43fd0&dn=URL&tr=http%3A%2F%URL%3A8725%2Fannounce'\n\nmirror: URL\n\nunzip password: 'BroughtToYouByInfoRe'\n\npre-process: none\n\nneed to do: check misspelling\n\nusage with HuggingFace:" ]

[ "TAGS\n#task_categories-automatic-speech-recognition #task_categories-text-to-speech #size_categories-100K<n<1M #language-Vietnamese #license-cc-by-4.0 #region-us \n", "# unofficial mirror of InfoRe Technology public dataset №2\n\nofficial announcement: URL\n\n415h, 315k samples, vietnamese audiobooks of chinese wǔxiá 武俠 & xiānxiá 仙俠\n\nbộ dữ liệu bóc ra từ YouTube đọc truyện võ hiệp & tiên hiệp, áp dụng kĩ thuật đối chiếu văn bản để dán nhãn tự động\n\nofficial download: 'magnet:?xt=urn:btih:41f1290325ecb6f1230ecdff2441527c9cd43fd0&dn=URL&tr=http%3A%2F%URL%3A8725%2Fannounce'\n\nmirror: URL\n\nunzip password: 'BroughtToYouByInfoRe'\n\npre-process: none\n\nneed to do: check misspelling\n\nusage with HuggingFace:" ]

[ "region:us" ]

[]

[]

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

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

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

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[ "# Dataset Card for DWD Observations\n\n\n\nThis dataset is a collection of historical German Weather Service (DWD) weather station observations at 10 minutely, and hourly resolutions for various parameters. The data has been\nconverted to Zarr and Xarray. The data was gathered using the wonderful wetterdienst package.", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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#license-mit #climate #region-us \n", "# Dataset Card for DWD Observations\n\n\n\nThis dataset is a collection of historical German Weather Service (DWD) weather station observations at 10 minutely, and hourly resolutions for various parameters. The data has been\nconverted to Zarr and Xarray. The data was gathered using the wonderful wetterdienst package.", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:tr", "license:mit", "region:us" ]

[]

[ "tr" ]

[ "## Includes a part of OpenOrca dataset in Turkish language\n\nThe Subset of OpenOrca dataset in turkish language comprises 798350 pairs of questions and answers in Turkish, \npredominantly translated from English using Google Translate. \nWherever possible, specific terminology and unique names were retained unchanged in the translation process.\n\n\nFeel free to submit pull requests to enhance the quality of the dataset.\n\nContact: URL" ]

[ "TAGS\n#language-Turkish #license-mit #region-us \n", "## Includes a part of OpenOrca dataset in Turkish language\n\nThe Subset of OpenOrca dataset in turkish language comprises 798350 pairs of questions and answers in Turkish, \npredominantly translated from English using Google Translate. \nWherever possible, specific terminology and unique names were retained unchanged in the translation process.\n\n\nFeel free to submit pull requests to enhance the quality of the dataset.\n\nContact: URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of CorticalStack/travel-mistral-7B-16b-base\n\n\n\nDataset automatically created during the evaluation run of model CorticalStack/travel-mistral-7B-16b-base on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T09:58:07.096782(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 CorticalStack/travel-mistral-7B-16b-base\n\n\n\nDataset automatically created during the evaluation run of model CorticalStack/travel-mistral-7B-16b-base on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T09:58:07.096782(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 FreedomIntelligence/AceGPT-7B\n\n\n\nDataset automatically created during the evaluation run of model FreedomIntelligence/AceGPT-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 2024-02-11T10:08:53.016529(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 FreedomIntelligence/AceGPT-7B\n\n\n\nDataset automatically created during the evaluation run of model FreedomIntelligence/AceGPT-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 2024-02-11T10:08:53.016529(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:token-classification", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[ "## Introduction\nThis is a datamix created for the The Learning Agency Lab - PII Data Detection Kaggle competition. It supports training / evaluation of models that can identify a set of PII types in texts. Specifically the PII types are:\n\n- 'NAME_STUDENT' - The full or partial name of a student that is not necessarily the author of the essay. - This excludes instructors, authors, and other person names.\n- 'EMAIL' - A student’s email address.\n- 'USERNAME' - A student's username on any platform.\n- 'ID_NUM' - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.\n- 'PHONE_NUM' - A phone number associated with a student.\n- 'URL_PERSONAL' - A URL that might be used to identify a student.\n- 'STREET_ADDRESS' - A full or partial street address that is associated with the student, such as their home address.", "## Sources\nThe datamix comprises of the following contributions:\n\n - Dataset by Nicholas: More data - 2355 essays generated by the best open source model - Mixtral 8x7b \n - Dataset by PJMathematician: 2000 AI Created PII detection External dataset\n - Dataset by Moth: New Dataset: +4400 external generated texts \n - Dataset by Valentin: 4367 new essays - generated to promote diversity" ]

[ "TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n", "## Introduction\nThis is a datamix created for the The Learning Agency Lab - PII Data Detection Kaggle competition. It supports training / evaluation of models that can identify a set of PII types in texts. Specifically the PII types are:\n\n- 'NAME_STUDENT' - The full or partial name of a student that is not necessarily the author of the essay. - This excludes instructors, authors, and other person names.\n- 'EMAIL' - A student’s email address.\n- 'USERNAME' - A student's username on any platform.\n- 'ID_NUM' - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.\n- 'PHONE_NUM' - A phone number associated with a student.\n- 'URL_PERSONAL' - A URL that might be used to identify a student.\n- 'STREET_ADDRESS' - A full or partial street address that is associated with the student, such as their home address.", "## Sources\nThe datamix comprises of the following contributions:\n\n - Dataset by Nicholas: More data - 2355 essays generated by the best open source model - Mixtral 8x7b \n - Dataset by PJMathematician: 2000 AI Created PII detection External dataset\n - Dataset by Moth: New Dataset: +4400 external generated texts \n - Dataset by Valentin: 4367 new essays - generated to promote diversity" ]

[ "task_categories:object-detection", "roboflow", "roboflow2huggingface", "region:us" ]

[]

[]

[ "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on February 10, 2024 at 7:28 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand and search unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nFor state of the art Computer Vision training notebooks you can use with this dataset,\nvisit URL\n\nTo find over 100k other datasets and pre-trained models, visit URL\n\nThe dataset includes 1838 images.\nPeople are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 640x640 (Stretch)\n\nThe following augmentation was applied to create 3 versions of each source image:\n* Randomly crop between 0 and 67 percent of the image\n* Salt and pepper noise was applied to 4 percent of pixels\n\nThe following transformations were applied to the bounding boxes of each image:\n* Random shear of between -5° to +5° horizontally and -5° to +5° vertically" ]

[ "TAGS\n#task_categories-object-detection #roboflow #roboflow2huggingface #region-us \n", "### Dataset Labels", "### Number of Images", "### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:", "### Roboflow Dataset Page\nURL", "### License\nCC BY 4.0", "### Dataset Summary\nThis dataset was exported via URL on February 10, 2024 at 7:28 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand and search unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nFor state of the art Computer Vision training notebooks you can use with this dataset,\nvisit URL\n\nTo find over 100k other datasets and pre-trained models, visit URL\n\nThe dataset includes 1838 images.\nPeople are annotated in COCO format.\n\nThe following pre-processing was applied to each image:\n* Auto-orientation of pixel data (with EXIF-orientation stripping)\n* Resize to 640x640 (Stretch)\n\nThe following augmentation was applied to create 3 versions of each source image:\n* Randomly crop between 0 and 67 percent of the image\n* Salt and pepper noise was applied to 4 percent of pixels\n\nThe following transformations were applied to the bounding boxes of each image:\n* Random shear of between -5° to +5° horizontally and -5° to +5° vertically" ]

[ "task_categories:text-to-image", "task_categories:image-to-image", "size_categories:1K<n<10K", "license:mit", "dalle-3", "dall-e", "dalle-images", "images", "croissant", "region:us" ]

[]

[]

[ "# DALL•E 3 Images Dataset\n\nThis is datase with images made by Dalle3.", "## Dataset parameters\n1. Count of images: 3310\n2. Zip file with dataset: True\n3. Captions with images: False", "## License\n\nLicense for this dataset: MIT", "## Use in *datasets*\n\n1. \n2.", "#### *Enjoy with this dataset!*" ]

[ "TAGS\n#task_categories-text-to-image #task_categories-image-to-image #size_categories-1K<n<10K #license-mit #dalle-3 #dall-e #dalle-images #images #croissant #region-us \n", "# DALL•E 3 Images Dataset\n\nThis is datase with images made by Dalle3.", "## Dataset parameters\n1. Count of images: 3310\n2. Zip file with dataset: True\n3. Captions with images: False", "## License\n\nLicense for this dataset: MIT", "## Use in *datasets*\n\n1. \n2.", "#### *Enjoy with this dataset!*" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO\n\n\n\nDataset automatically created during the evaluation run of model Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T12:02:04.707768(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO\n\n\n\nDataset automatically created during the evaluation run of model Radu1999/Mistral-Instruct-Ukrainian-SFT-DPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T12:02:04.707768(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Radu1999/Mistral-Instruct-Ukrainian-SFT\n\n\n\nDataset automatically created during the evaluation run of model Radu1999/Mistral-Instruct-Ukrainian-SFT on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T12:06:32.425794(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Radu1999/Mistral-Instruct-Ukrainian-SFT\n\n\n\nDataset automatically created during the evaluation run of model Radu1999/Mistral-Instruct-Ukrainian-SFT on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T12:06:32.425794(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:conversational", "task_categories:question-answering", "size_categories:n<1K", "language:th", "license:apache-2.0", "region:us" ]

[]

[ "th" ]

[ "# Open Assistant 2 Top-1 Thai", "## Dataset Details", "### Dataset Description\n\nA top-1 Thai dataset taken from the top scoring URL conversations. Saved in HF Chat format.\n\nLicense: Apache 2.0\n\nScript: URL", "## Dataset Structure\n\nWe structure the dataset using the format commonly used as input into Hugging Face Chat Templates:" ]

[ "TAGS\n#task_categories-conversational #task_categories-question-answering #size_categories-n<1K #language-Thai #license-apache-2.0 #region-us \n", "# Open Assistant 2 Top-1 Thai", "## Dataset Details", "### Dataset Description\n\nA top-1 Thai dataset taken from the top scoring URL conversations. Saved in HF Chat format.\n\nLicense: Apache 2.0\n\nScript: URL", "## Dataset Structure\n\nWe structure the dataset using the format commonly used as input into Hugging Face Chat Templates:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Dataset Name\n\n\n\n\n Tokenizer: mdeberta\n\n Dataset: xlwa_en-it\n\n Dataset path = /home/pgajo/working/food/data/XL-WA/data", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name\n\n\n\n\n Tokenizer: mdeberta\n\n Dataset: xlwa_en-it\n\n Dataset path = /home/pgajo/working/food/data/XL-WA/data", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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", "task_categories:conversational", "size_categories:1K<n<10K", "language:en", "license:mit", "medical", "arxiv:2401.14493", "region:us" ]

[ "2401.14493" ]

[ "en" ]

[ "## K-QA\n\nWe are excited to announce the release of K-QA!\n\nThis benchmark consists of two parts: a medium-scale corpus of diverse real-world medical inquiries written by patients on K Health (an AI-driven clinical platform), and a subset of carefully crafted answers annotated by a team of in-house medical experts.\n\nThe dataset comprises 201 questions and answers, containing over 1,589 ground-truth statements.\nAdditionally, we provide 1,212 authentic patient questions.\n\n\nFor further details, refer to the paper.\n\nThe recommended evaluation scheme for fine-grained evaluation can be found here", "#### Cite Us" ]

[ "TAGS\n#task_categories-question-answering #task_categories-text-generation #task_categories-conversational #size_categories-1K<n<10K #language-English #license-mit #medical #arxiv-2401.14493 #region-us \n", "## K-QA\n\nWe are excited to announce the release of K-QA!\n\nThis benchmark consists of two parts: a medium-scale corpus of diverse real-world medical inquiries written by patients on K Health (an AI-driven clinical platform), and a subset of carefully crafted answers annotated by a team of in-house medical experts.\n\nThe dataset comprises 201 questions and answers, containing over 1,589 ground-truth statements.\nAdditionally, we provide 1,212 authentic patient questions.\n\n\nFor further details, refer to the paper.\n\nThe recommended evaluation scheme for fine-grained evaluation can be found here", "#### Cite Us" ]

[ "task_categories:text-generation", "task_categories:question-answering", "task_ids:parsing", "size_categories:1M<n<10M", "license:other", "python-copilot", "python-coding", "python-architecture", "knowledge-graphs", "multimodal", "text-image-audio", "fine-tuning", "training", "question-answering", "image-knowledge-graph", "alpaca", "mp3", "png", "text", "instruct", "coding", "task", "prompt", "response", "yaml", "region:us" ]

[]

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[ "## Python Copilot Instructions on How to Code using Alpaca and Yaml\n\nTraining and test datasets for building coding multimodal models that understand how to use the open source GitHub projects for the multimodal Qwen AI project:\n\n- Qwen\n- Qwen Agent\n- Qwen VL Chat\n- Qwen Audio\n\nThis dataset is the 2024-02-10 update for the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.", "### Details\n\nEach row contains python code, either a class method or a global function, imported modules, base classes (if any), exceptions (ordered based off the code), returns (ordered based off the code), arguments (ordered based off the code), and more.\n\n- Rows: 1070671\n- Size: 1.8 GB\n- Data type: instruct\n- Format: Introduction on code usage using alpaca and yaml response\n- Number of python repos: 1274", "### How to use the datasets", "#### Load Qwen Agent Schema", "### Schema\n\nThe instruction alpaca text with yaml response is in the desc column:" ]

[ "TAGS\n#task_categories-text-generation #task_categories-question-answering #task_ids-parsing #size_categories-1M<n<10M #license-other #python-copilot #python-coding #python-architecture #knowledge-graphs #multimodal #text-image-audio #fine-tuning #training #question-answering #image-knowledge-graph #alpaca #mp3 #png #text #instruct #coding #task #prompt #response #yaml #region-us \n", "## Python Copilot Instructions on How to Code using Alpaca and Yaml\n\nTraining and test datasets for building coding multimodal models that understand how to use the open source GitHub projects for the multimodal Qwen AI project:\n\n- Qwen\n- Qwen Agent\n- Qwen VL Chat\n- Qwen Audio\n\nThis dataset is the 2024-02-10 update for the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.", "### Details\n\nEach row contains python code, either a class method or a global function, imported modules, base classes (if any), exceptions (ordered based off the code), returns (ordered based off the code), arguments (ordered based off the code), and more.\n\n- Rows: 1070671\n- Size: 1.8 GB\n- Data type: instruct\n- Format: Introduction on code usage using alpaca and yaml response\n- Number of python repos: 1274", "### How to use the datasets", "#### Load Qwen Agent Schema", "### Schema\n\nThe instruction alpaca text with yaml response is in the desc column:" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of yam-peleg/Experiment4-7B\n\n\n\nDataset automatically created during the evaluation run of model yam-peleg/Experiment4-7B 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 2024-02-11T12:47:14.139387(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 yam-peleg/Experiment4-7B\n\n\n\nDataset automatically created during the evaluation run of model yam-peleg/Experiment4-7B 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 2024-02-11T12:47:14.139387(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 louisbrulenaudet/Pearl-7B-0210-ties\n\n\n\nDataset automatically created during the evaluation run of model louisbrulenaudet/Pearl-7B-0210-ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T13:02:55.830318(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of louisbrulenaudet/Pearl-7B-0210-ties\n\n\n\nDataset automatically created during the evaluation run of model louisbrulenaudet/Pearl-7B-0210-ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T13:02:55.830318(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Dataset Name", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:time-series-forecasting", "size_categories:100K<n<1M", "license:mit", "region:us" ]

[]

[]

[ "# About data\n\nThis dataset consists of 1 million pi digits i.e., 31415926535...", "# Task\n\nYou can use this dataset for \n* Time series forecasting\n* Time series classification" ]

[ "TAGS\n#task_categories-time-series-forecasting #size_categories-100K<n<1M #license-mit #region-us \n", "# About data\n\nThis dataset consists of 1 million pi digits i.e., 31415926535...", "# Task\n\nYou can use this dataset for \n* Time series forecasting\n* Time series classification" ]

[ "task_categories:text-generation", "task_categories:question-answering", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "arxiv:2106.09685", "arxiv:2310.20410", "region:us" ]

[ "2106.09685", "2310.20410" ]

[ "en" ]

[ "# Learning to Edit: Aligning LLMs with Knowledge Editing\n\n framework for effective and efficient knowledge editing of large language models (LLMs).\nour LTE framework focuses on teaching LLMs to apply updated knowledge into input questions, inspired by the philosophy of \"_Teach a man to fish_.\"\n\nAs the below figure shows, LTE features a two-phase process: (i) the Alignment Phase, which fine-tunes LLMs on a meticulously curated parallel dataset to make reliable, in-scope edits while preserving out-of-scope information and linguistic proficiency; and (ii) the Inference Phase, which employs a retrieval-based mechanism for real-time and mass knowledge editing.\n\n<p align=\"center\">\n <br>\n <img src=\"URL width=\"1200\"/>\n <br>\n</p>", "## ️ How to implement", "### Requirements\nNote: Please use Python 3.10+ for LTE. To get started, simply install conda and run:", "### 1. Alignment Phrase\nFirstly, please download the training data of LTE from HuggingFace and put it into data/.", "#### LLaMA2-Chat-7B\nThe code is based on FastChat. Standard fine-tuning was conducted on 4×A100 GPUs (80G) for about 9 hours.\n\n\nTo reduce the total memory footprint, LTE also supports LoRA, which fine-tunes low-rank slices of the query, key, and value embedding heads.", "#### Qwen-Chat-7B\nThe code is based on Qwen. Standard fine-tuning was conducted on 4×A100 GPUs (80G) for about 9 hours.\n\n\nTo reduce the total memory footprint, LTE also supports LoRA, which fine-tunes low-rank slices of the query, key, and value embedding heads.", "### 2. Inference Phrase\nThe evaluation of our proposed LTE is based on EasyEdit.\n\nPlease run the following command for experiments of LLaMA2-Chat-7B:\n\n\nPlease run the following command for experiments of Qwen-Chat-7B:", "## Citation\nPlease cite our paper if you use the data or code in this repo." ]

[ "TAGS\n#task_categories-text-generation #task_categories-question-answering #size_categories-10K<n<100K #language-English #license-apache-2.0 #arxiv-2106.09685 #arxiv-2310.20410 #region-us \n", "# Learning to Edit: Aligning LLMs with Knowledge Editing\n\n framework for effective and efficient knowledge editing of large language models (LLMs).\nour LTE framework focuses on teaching LLMs to apply updated knowledge into input questions, inspired by the philosophy of \"_Teach a man to fish_.\"\n\nAs the below figure shows, LTE features a two-phase process: (i) the Alignment Phase, which fine-tunes LLMs on a meticulously curated parallel dataset to make reliable, in-scope edits while preserving out-of-scope information and linguistic proficiency; and (ii) the Inference Phase, which employs a retrieval-based mechanism for real-time and mass knowledge editing.\n\n<p align=\"center\">\n <br>\n <img src=\"URL width=\"1200\"/>\n <br>\n</p>", "## ️ How to implement", "### Requirements\nNote: Please use Python 3.10+ for LTE. To get started, simply install conda and run:", "### 1. Alignment Phrase\nFirstly, please download the training data of LTE from HuggingFace and put it into data/.", "#### LLaMA2-Chat-7B\nThe code is based on FastChat. Standard fine-tuning was conducted on 4×A100 GPUs (80G) for about 9 hours.\n\n\nTo reduce the total memory footprint, LTE also supports LoRA, which fine-tunes low-rank slices of the query, key, and value embedding heads.", "#### Qwen-Chat-7B\nThe code is based on Qwen. Standard fine-tuning was conducted on 4×A100 GPUs (80G) for about 9 hours.\n\n\nTo reduce the total memory footprint, LTE also supports LoRA, which fine-tunes low-rank slices of the query, key, and value embedding heads.", "### 2. Inference Phrase\nThe evaluation of our proposed LTE is based on EasyEdit.\n\nPlease run the following command for experiments of LLaMA2-Chat-7B:\n\n\nPlease run the following command for experiments of Qwen-Chat-7B:", "## Citation\nPlease cite our paper if you use the data or code in this repo." ]

[ "region:us" ]

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[ "# Dataset Card for \"ExeBench-Switch-Eval-small-gpt3.5-zeroshot-result\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ExeBench-Switch-Eval-small-gpt3.5-zeroshot-result\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Metadata", "## Identification Information\n\n- Title:Aerial surveys of a sunflower crop’s lifecycle from May to September 2023 \n- Originator: Sofia University - faculty of mathematics and informatics, SAP LABS Bulgaria \n- Publication Date: 2023.11.08", "### Abstract\n\nEfficient food production is shaping up to be one of the new frontiers for new technologies and solutions. One such prominent domain is the remote sensing ecosystem, and more precicely, technologies such as multispectral and hyperspectral sensing equipment.\nThese devices are gradually moving from the academia environment to the industry world, and there decrease is cost allows for many new applications to emerge. \n\nMultispectral drones are advanced unmanned aerial vehicles (UAVs) equipped with cameras or sensors, capable of capturing imagery across multiple spectral bands. Unlike traditional RGB counterparts, they capture data not only within, but also beyond the visible spectrum, such as near-infrared (NIR). This data can provide valuable insights for various applications, including agriculture, environmental monitoring, land surveying, and more.\nOne of the main uses of multispectral drones in agriculture is related to the calculation of vegetation (NDVI, NDRE etc.) and other indices that inform the farmer about crop development, stress etc. The latter can also serve as indirect indicator of soil conditions and water distribution. This approach enables more accurate and detailed assessments compared to traditional visual inspections.\nSimilar multispectral data is provided by earth observation satellites, such as Sentinel-2, however they are limited with respect to revisit time, spatial resolution and most importantly, their inability to see through clouds. Therefore, the use of multispectral drones can fill these operational gaps and provide more precise and timely data to the farmers.\nHowever, to work simultaneously with satellite and drone data, analysts must have confidence in the precision and comparability of these two data sources (e.g., for NDVI). For example, the DJI P4 multispectral images have slightly different band sensitivities when compared with Sentinel-2, which may cause deviations in the index values. Another prominent problem is related to the field illumination, which depends on time of day and weather conditions. Even though the DJI P4 drone has a calibration sensor, supposed to compensate for the illuminating spectrum deviations, to the best of our knowledge, no public data set exists that demonstrates the tolerance of deviations between e.g., different drone footages or between DJI P4 and Sentinel-2. Moreover, Sentinel-2 implements atmospheric corrections that may contribute to such deviations as well.\nMachine learning models can be utilized to extract valuable insights from multispectral data in precision agriculture applications. By leveraging the rich information captured across multiple spectral bands, machine learning algorithms can analyze and interpret the data to provide actionable recommendations for farmers and agronomists, such as highlighting areas with the most vegetation stress. Successful implementation of machine learning models for precision agriculture, based on multispectral data, requires high quality data sets, which are currently scarce. Therefore, collection of a high-quality, multispectral data set is a prerequisite to future machine learning experiments in the domain of precision farming.\n\nFor these reasons, our research team conducted multiple surveys, tracking the entire lifecycle of a sunflower field and gathering spectal data.", "### Purpose\n\nThis dataset was developed as part of a research project, investigating the capabilities and application of drones and multispectral cameras for the agricultural domain.\nThe provided data can be used for the following scenarios:\n1) Training models relying on multispectral datasources.\n2) Improve existing algorithms in the computer vision domain.", "## Time Period of Content\n\n- Single Date/Time: Start Date 2023-04-25 to End Date 2023-09-04", "## Data Quality Information\n\nComposite images have been generated with DJI Terra, with 70% frontal and 60% side overlap.\nThere are instances where a survey has been completed in the span of 2 days due to adverse environment conditions.\nAlthough there was an effort to have surveys execution in a constant time window (morning and afternoon), for some of the runs this is not the case.\nThe raw data is validated to be complete - representing the entirety of the observed field for every survey.", "### Horizontal Coordinate System\n\n- Geographic Coordinate System: EPSG:4326\n - Angular Unit: Decimal degrees\n - Datum: WGS 84\n - Prime Meridian: Greenwich\n - Domain: Raster", "## Entity and Attribute Information", "### Detailed Description", "#### Entities\nData is organized into directories. Each directory corresponds to one survey and uses DD.MM.YYYY format.\n\nEach survey directory contains 2 subdirectories : raw and results.\nresults directory is the output from the DJI Terra processing of the raw data, collected by the drone. \n\n- Contents:\n - raw\n - Composite images, derived from a single drone sensor. Images follow result_<Blue, Green, etc.> nomenclature.\n - .prj projection file for every composite image\n - .tfw georeference file for every composite image\n - results\n - subdirectories for each executed flight, required to complete the survey.\n - each subdirectory keeps the raw data for each sensing point on the drone's mission path\n - one point is represented by one JPG image and 5 grayscale TIF images, corresponding to each sensor of the drone\n\nAll images are injected with geo-referencing data, timestamps, image quality, camera properties.\n\nThe datasets hold additional metadata in two files:\n - field_shape.geojson - bounding box for the sunflower field\n - crop_details.txt - information about the crop", "#### Capture aperture\n\nDrone surveys are executed with DJI Phantom 4 Multispectral drone. The drone uses the following sensors to capture data:\n\nSensors: Six 1/2.9” CMOS \n\nFilters: \n\n - Blue (B): 450 nm ± 16 nm\n - Green (G): 560 nm ± 16 nm\n - Red (R): 650 nm ± 16 nm\n - Red edge (RE): 730 nm ± 16 nm\n - Near-infrared (NIR): 840 nm ± 26 nm\n\nLenses: \n\n- FOV (Field of View): 62.7°\n- Focal Length: 5.74 mm\n- Aperture: f/2.2\n\nSoftware used for generating composite images: DJI Terra 3.6.8.", "## Metadata Reference Information\n\n- Metadata Contact:\n - Name: Pavel Genevski\n - Organization: SAP LABS Bulgaria\n - Position: Research expert\n - Email: pavel.genevski@URL\n\n- Metadata Contact:\n - Name: Radoslav Stefanov\n - Organization: SAP LABS Bulgaria\n - Position: Senior developer\n - Email: radoslav.stefanov@URL\n\n- Metadata Date: Date of creating this metadata (2023.11.08)\n- Metadata Standard Name: FGDC Content Standard for Digital Geospatial Metadata", "## Additional Information\n\n- Keywords: agriculture, multispectral, crop, sunflower\n- Access Constraints: CC BY 4.0\n- Use Constraints: CC BY 4.0" ]

[ "TAGS\n#region-us \n", "# Dataset Metadata", "## Identification Information\n\n- Title:Aerial surveys of a sunflower crop’s lifecycle from May to September 2023 \n- Originator: Sofia University - faculty of mathematics and informatics, SAP LABS Bulgaria \n- Publication Date: 2023.11.08", "### Abstract\n\nEfficient food production is shaping up to be one of the new frontiers for new technologies and solutions. One such prominent domain is the remote sensing ecosystem, and more precicely, technologies such as multispectral and hyperspectral sensing equipment.\nThese devices are gradually moving from the academia environment to the industry world, and there decrease is cost allows for many new applications to emerge. \n\nMultispectral drones are advanced unmanned aerial vehicles (UAVs) equipped with cameras or sensors, capable of capturing imagery across multiple spectral bands. Unlike traditional RGB counterparts, they capture data not only within, but also beyond the visible spectrum, such as near-infrared (NIR). This data can provide valuable insights for various applications, including agriculture, environmental monitoring, land surveying, and more.\nOne of the main uses of multispectral drones in agriculture is related to the calculation of vegetation (NDVI, NDRE etc.) and other indices that inform the farmer about crop development, stress etc. The latter can also serve as indirect indicator of soil conditions and water distribution. This approach enables more accurate and detailed assessments compared to traditional visual inspections.\nSimilar multispectral data is provided by earth observation satellites, such as Sentinel-2, however they are limited with respect to revisit time, spatial resolution and most importantly, their inability to see through clouds. Therefore, the use of multispectral drones can fill these operational gaps and provide more precise and timely data to the farmers.\nHowever, to work simultaneously with satellite and drone data, analysts must have confidence in the precision and comparability of these two data sources (e.g., for NDVI). For example, the DJI P4 multispectral images have slightly different band sensitivities when compared with Sentinel-2, which may cause deviations in the index values. Another prominent problem is related to the field illumination, which depends on time of day and weather conditions. Even though the DJI P4 drone has a calibration sensor, supposed to compensate for the illuminating spectrum deviations, to the best of our knowledge, no public data set exists that demonstrates the tolerance of deviations between e.g., different drone footages or between DJI P4 and Sentinel-2. Moreover, Sentinel-2 implements atmospheric corrections that may contribute to such deviations as well.\nMachine learning models can be utilized to extract valuable insights from multispectral data in precision agriculture applications. By leveraging the rich information captured across multiple spectral bands, machine learning algorithms can analyze and interpret the data to provide actionable recommendations for farmers and agronomists, such as highlighting areas with the most vegetation stress. Successful implementation of machine learning models for precision agriculture, based on multispectral data, requires high quality data sets, which are currently scarce. Therefore, collection of a high-quality, multispectral data set is a prerequisite to future machine learning experiments in the domain of precision farming.\n\nFor these reasons, our research team conducted multiple surveys, tracking the entire lifecycle of a sunflower field and gathering spectal data.", "### Purpose\n\nThis dataset was developed as part of a research project, investigating the capabilities and application of drones and multispectral cameras for the agricultural domain.\nThe provided data can be used for the following scenarios:\n1) Training models relying on multispectral datasources.\n2) Improve existing algorithms in the computer vision domain.", "## Time Period of Content\n\n- Single Date/Time: Start Date 2023-04-25 to End Date 2023-09-04", "## Data Quality Information\n\nComposite images have been generated with DJI Terra, with 70% frontal and 60% side overlap.\nThere are instances where a survey has been completed in the span of 2 days due to adverse environment conditions.\nAlthough there was an effort to have surveys execution in a constant time window (morning and afternoon), for some of the runs this is not the case.\nThe raw data is validated to be complete - representing the entirety of the observed field for every survey.", "### Horizontal Coordinate System\n\n- Geographic Coordinate System: EPSG:4326\n - Angular Unit: Decimal degrees\n - Datum: WGS 84\n - Prime Meridian: Greenwich\n - Domain: Raster", "## Entity and Attribute Information", "### Detailed Description", "#### Entities\nData is organized into directories. Each directory corresponds to one survey and uses DD.MM.YYYY format.\n\nEach survey directory contains 2 subdirectories : raw and results.\nresults directory is the output from the DJI Terra processing of the raw data, collected by the drone. \n\n- Contents:\n - raw\n - Composite images, derived from a single drone sensor. Images follow result_<Blue, Green, etc.> nomenclature.\n - .prj projection file for every composite image\n - .tfw georeference file for every composite image\n - results\n - subdirectories for each executed flight, required to complete the survey.\n - each subdirectory keeps the raw data for each sensing point on the drone's mission path\n - one point is represented by one JPG image and 5 grayscale TIF images, corresponding to each sensor of the drone\n\nAll images are injected with geo-referencing data, timestamps, image quality, camera properties.\n\nThe datasets hold additional metadata in two files:\n - field_shape.geojson - bounding box for the sunflower field\n - crop_details.txt - information about the crop", "#### Capture aperture\n\nDrone surveys are executed with DJI Phantom 4 Multispectral drone. The drone uses the following sensors to capture data:\n\nSensors: Six 1/2.9” CMOS \n\nFilters: \n\n - Blue (B): 450 nm ± 16 nm\n - Green (G): 560 nm ± 16 nm\n - Red (R): 650 nm ± 16 nm\n - Red edge (RE): 730 nm ± 16 nm\n - Near-infrared (NIR): 840 nm ± 26 nm\n\nLenses: \n\n- FOV (Field of View): 62.7°\n- Focal Length: 5.74 mm\n- Aperture: f/2.2\n\nSoftware used for generating composite images: DJI Terra 3.6.8.", "## Metadata Reference Information\n\n- Metadata Contact:\n - Name: Pavel Genevski\n - Organization: SAP LABS Bulgaria\n - Position: Research expert\n - Email: pavel.genevski@URL\n\n- Metadata Contact:\n - Name: Radoslav Stefanov\n - Organization: SAP LABS Bulgaria\n - Position: Senior developer\n - Email: radoslav.stefanov@URL\n\n- Metadata Date: Date of creating this metadata (2023.11.08)\n- Metadata Standard Name: FGDC Content Standard for Digital Geospatial Metadata", "## Additional Information\n\n- Keywords: agriculture, multispectral, crop, sunflower\n- Access Constraints: CC BY 4.0\n- Use Constraints: CC BY 4.0" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of SF-Foundation/Ein-72B-v0.11\n\n\n\nDataset automatically created during the evaluation run of model SF-Foundation/Ein-72B-v0.11 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T13:40:58.813057(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of SF-Foundation/Ein-72B-v0.11\n\n\n\nDataset automatically created during the evaluation run of model SF-Foundation/Ein-72B-v0.11 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T13:40:58.813057(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:cs", "license:cc0-1.0", "region:us" ]

[]

[ "cs" ]

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[ "TAGS\n#language-Czech #license-cc0-1.0 #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of ArianAskari/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T14:20:18.392173(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of ArianAskari/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID-SFT-WoDPO-MixQV2-Zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T14:20:18.392173(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ArianAskari/SOLID_SFT-WoDPO-WoMixQ\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID_SFT-WoDPO-WoMixQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T14:26:56.210922(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of ArianAskari/SOLID_SFT-WoDPO-WoMixQ\n\n\n\nDataset automatically created during the evaluation run of model ArianAskari/SOLID_SFT-WoDPO-WoMixQ on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T14:26:56.210922(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 fhai50032/SamChat\n\n\n\nDataset automatically created during the evaluation run of model fhai50032/SamChat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T14:43:14.190988(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 fhai50032/SamChat\n\n\n\nDataset automatically created during the evaluation run of model fhai50032/SamChat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T14:43:14.190988(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"NumericBench-Eval-small-gpt3.5-zeroshot-result\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"NumericBench-Eval-small-gpt3.5-zeroshot-result\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Radu1999/MisterUkrainian\n\n\n\nDataset automatically created during the evaluation run of model Radu1999/MisterUkrainian on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T14:53:33.344977(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Radu1999/MisterUkrainian\n\n\n\nDataset automatically created during the evaluation run of model Radu1999/MisterUkrainian on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T14:53:33.344977(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 JaeyeonKang/CCK_Asura_v2\n\n\n\nDataset automatically created during the evaluation run of model JaeyeonKang/CCK_Asura_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 2024-02-11T16:06:06.601479(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 JaeyeonKang/CCK_Asura_v2\n\n\n\nDataset automatically created during the evaluation run of model JaeyeonKang/CCK_Asura_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 2024-02-11T16:06:06.601479(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:multiple-choice", "size_categories:n<1K", "language:ar", "license:apache-2.0", "arxiv:2402.03177", "region:us" ]

[ "2402.03177" ]

[ "ar" ]

[]

[ "TAGS\n#task_categories-multiple-choice #size_categories-n<1K #language-Arabic #license-apache-2.0 #arxiv-2402.03177 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Sao10K/Solstice-11B-v1\n\n\n\nDataset automatically created during the evaluation run of model Sao10K/Solstice-11B-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T16:13:21.638790(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Sao10K/Solstice-11B-v1\n\n\n\nDataset automatically created during the evaluation run of model Sao10K/Solstice-11B-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T16:13:21.638790(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:cc-by-4.0", "instruction-finetuning", "region:us" ]

[]

[ "en" ]

[ "### Original Alpaca Dataset Summary\n\n\nAlpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.\n\n\nThe authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:\n\n\n* The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.\n* A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.\n* Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.\n* The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.\n* Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.\n\n\nThis produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).\nIn a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct.", "### Supported Tasks and Leaderboards\n\n\nThe Alpaca dataset designed for instruction training pretrained language models.", "### Languages\n\n\nThe data in Alpaca are in English (BCP-47 en).\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of \"train\" looks as follows:", "### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'instruction': describes the task the model should perform. Each of the 52K instructions is unique.\n* 'input': optional context or input for the task. For example, when the instruction is \"Summarize the following article\", the input is the article. Around 40% of the examples have an input.\n* 'output': the answer to the instruction as generated by 'text-davinci-003'.\n* 'text': the 'instruction', 'input' and 'output' formatted with the prompt template used by the authors for fine-tuning their models.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nExcerpt the blog post accompanying the release of this dataset:\n\n\n\n> \n> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.\n> \n> \n>", "### Discussion of Biases", "### Other Known Limitations\n\n\nThe 'alpaca' data is generated by a language model ('text-davinci-003') and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThe dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).", "### Contributions" ]

[ "TAGS\n#task_categories-text-generation #language-English #license-cc-by-4.0 #instruction-finetuning #region-us \n", "### Original Alpaca Dataset Summary\n\n\nAlpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.\n\n\nThe authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:\n\n\n* The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.\n* A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.\n* Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.\n* The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.\n* Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.\n\n\nThis produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).\nIn a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct.", "### Supported Tasks and Leaderboards\n\n\nThe Alpaca dataset designed for instruction training pretrained language models.", "### Languages\n\n\nThe data in Alpaca are in English (BCP-47 en).\n\n\nDataset Structure\n-----------------", "### Data Instances\n\n\nAn example of \"train\" looks as follows:", "### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'instruction': describes the task the model should perform. Each of the 52K instructions is unique.\n* 'input': optional context or input for the task. For example, when the instruction is \"Summarize the following article\", the input is the article. Around 40% of the examples have an input.\n* 'output': the answer to the instruction as generated by 'text-davinci-003'.\n* 'text': the 'instruction', 'input' and 'output' formatted with the prompt template used by the authors for fine-tuning their models.", "### Data Splits\n\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset\n\n\nExcerpt the blog post accompanying the release of this dataset:\n\n\n\n> \n> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.\n> \n> \n>", "### Discussion of Biases", "### Other Known Limitations\n\n\nThe 'alpaca' data is generated by a language model ('text-davinci-003') and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information\n\n\nThe dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).", "### Contributions" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of JaeyeonKang/CCK_Asura_v2.1\n\n\n\nDataset automatically created during the evaluation run of model JaeyeonKang/CCK_Asura_v2.1 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 2024-02-11T16:16:03.001484(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 JaeyeonKang/CCK_Asura_v2.1\n\n\n\nDataset automatically created during the evaluation run of model JaeyeonKang/CCK_Asura_v2.1 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 2024-02-11T16:16:03.001484(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 louisbrulenaudet/Pearl-7B-0210-dare\n\n\n\nDataset automatically created during the evaluation run of model louisbrulenaudet/Pearl-7B-0210-dare on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T16:34:01.841503(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of louisbrulenaudet/Pearl-7B-0210-dare\n\n\n\nDataset automatically created during the evaluation run of model louisbrulenaudet/Pearl-7B-0210-dare on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T16:34:01.841503(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"dropoff-utcustom-EVAL\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"dropoff-utcustom-TRAIN\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for \"dropoff-utcustom-TEST\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of louisbrulenaudet/Pearl-7B-0211-ties\n\n\n\nDataset automatically created during the evaluation run of model louisbrulenaudet/Pearl-7B-0211-ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T17:47:26.168272(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of louisbrulenaudet/Pearl-7B-0211-ties\n\n\n\nDataset automatically created during the evaluation run of model louisbrulenaudet/Pearl-7B-0211-ties on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T17:47:26.168272(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 RubielLabarta/LogoS-7Bx2-MoE-13B-v0.2\n\n\n\nDataset automatically created during the evaluation run of model RubielLabarta/LogoS-7Bx2-MoE-13B-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T17:52:31.585367(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 RubielLabarta/LogoS-7Bx2-MoE-13B-v0.2\n\n\n\nDataset automatically created during the evaluation run of model RubielLabarta/LogoS-7Bx2-MoE-13B-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T17:52:31.585367(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Inv/Konstanta-Gamma-10.9B\n\n\n\nDataset automatically created during the evaluation run of model Inv/Konstanta-Gamma-10.9B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T18:02:06.510209(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Inv/Konstanta-Gamma-10.9B\n\n\n\nDataset automatically created during the evaluation run of model Inv/Konstanta-Gamma-10.9B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T18:02:06.510209(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 prince-canuma/Damysus-2.7B-Chat\n\n\n\nDataset automatically created during the evaluation run of model prince-canuma/Damysus-2.7B-Chat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T18:24:01.869694(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 prince-canuma/Damysus-2.7B-Chat\n\n\n\nDataset automatically created during the evaluation run of model prince-canuma/Damysus-2.7B-Chat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T18:24:01.869694(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"CorningAI-DocQA\"\n\nMore Information needed" ]

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

[ "region:us" ]

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

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

[ "region:us" ]

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[ "# Dataset Card for Dataset Name\n\n\n\n\n Tokenizer: mbert\n\n Dataset: TASTEset\n\n Unshuffled ratio: 1\n\n Shuffled ratio: 0\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/TASTEset/data/EW-TASTE/EW-TT-MT_LOC.json", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name\n\n\n\n\n Tokenizer: mbert\n\n Dataset: TASTEset\n\n Unshuffled ratio: 1\n\n Shuffled ratio: 0\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/TASTEset/data/EW-TASTE/EW-TT-MT_LOC.json", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Dataset Name\n\n\n\n\n Tokenizer: mbert\n\n Dataset: TASTEset\n\n Unshuffled ratio: 0\n\n Shuffled ratio: 1\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/TASTEset/data/EW-TASTE/EW-TT-MT_LOC.json", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name\n\n\n\n\n Tokenizer: mbert\n\n Dataset: TASTEset\n\n Unshuffled ratio: 0\n\n Shuffled ratio: 1\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/TASTEset/data/EW-TASTE/EW-TT-MT_LOC.json", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of macadeliccc/MBX-7B-v3-DPO\n\n\n\nDataset automatically created during the evaluation run of model macadeliccc/MBX-7B-v3-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 2024-02-11T18:53:41.876317(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of macadeliccc/MBX-7B-v3-DPO\n\n\n\nDataset automatically created during the evaluation run of model macadeliccc/MBX-7B-v3-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 2024-02-11T18:53:41.876317(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Dataset Name\n\n\n\n\n Tokenizer: mdeberta\n\n Dataset: TASTEset\n\n Unshuffled ratio: 1\n\n Shuffled ratio: 0\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/TASTEset/data/EW-TASTE/EW-TT-MT_LOC.json", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name\n\n\n\n\n Tokenizer: mdeberta\n\n Dataset: TASTEset\n\n Unshuffled ratio: 1\n\n Shuffled ratio: 0\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/TASTEset/data/EW-TASTE/EW-TT-MT_LOC.json", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for Dataset Name\n\n\n\n\n Tokenizer: mdeberta\n\n Dataset: TASTEset\n\n Unshuffled ratio: 0\n\n Shuffled ratio: 1\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/TASTEset/data/EW-TASTE/EW-TT-MT_LOC.json", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Dataset Name\n\n\n\n\n Tokenizer: mdeberta\n\n Dataset: TASTEset\n\n Unshuffled ratio: 0\n\n Shuffled ratio: 1\n\n Drop duplicates: True\n\n Dataset path = /home/pgajo/working/food/data/TASTEset/data/EW-TASTE/EW-TT-MT_LOC.json", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

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[ "# Dataset Card for \"Zeus-v0.1\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Zeus-v0.1\"\n\nMore Information needed" ]

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

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[ "## Prepraration", "## Data Cleaning\n\n1. merge two raw json files and json beautify the merged file\n\n\n\n2. (Optional) Verify the json file\n\n\n\n3. clean data - remove html tags etc\n\n\n\n4. uncensor with BUn\n\n\n\n5. Split the long conversation\n\n\n\nNow we have the cleaned uncensored dataset in 'sharegpt_20230401_clean_bun.json' (57058 conversations) and the same with split long conversations in 'sharegpt_20230401_clean_split_bun.json' (103152 conversations).\n\nThis latest one should be a drop-in replacement for 'anon8231489123/ShareGPT_Vicuna_unfiltered'." ]

[ "TAGS\n#license-other #region-us \n", "## Prepraration", "## Data Cleaning\n\n1. merge two raw json files and json beautify the merged file\n\n\n\n2. (Optional) Verify the json file\n\n\n\n3. clean data - remove html tags etc\n\n\n\n4. uncensor with BUn\n\n\n\n5. Split the long conversation\n\n\n\nNow we have the cleaned uncensored dataset in 'sharegpt_20230401_clean_bun.json' (57058 conversations) and the same with split long conversations in 'sharegpt_20230401_clean_split_bun.json' (103152 conversations).\n\nThis latest one should be a drop-in replacement for 'anon8231489123/ShareGPT_Vicuna_unfiltered'." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of yam-peleg/Experiment7-7B\n\n\n\nDataset automatically created during the evaluation run of model yam-peleg/Experiment7-7B 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 2024-02-11T19:25:35.851401(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 yam-peleg/Experiment7-7B\n\n\n\nDataset automatically created during the evaluation run of model yam-peleg/Experiment7-7B 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 2024-02-11T19:25:35.851401(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 vicgalle/NeuralBeagle-11B-truthy\n\n\n\nDataset automatically created during the evaluation run of model vicgalle/NeuralBeagle-11B-truthy on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T19:27:37.984436(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 vicgalle/NeuralBeagle-11B-truthy\n\n\n\nDataset automatically created during the evaluation run of model vicgalle/NeuralBeagle-11B-truthy on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T19:27:37.984436(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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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[]

[]

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

[ "region:us" ]

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

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

[ "region:us" ]

[]

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

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of lodrick-the-lafted/Hermes-Instruct-7B-v0.2\n\n\n\nDataset automatically created during the evaluation run of model lodrick-the-lafted/Hermes-Instruct-7B-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T20:18:48.620177(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 lodrick-the-lafted/Hermes-Instruct-7B-v0.2\n\n\n\nDataset automatically created during the evaluation run of model lodrick-the-lafted/Hermes-Instruct-7B-v0.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T20:18:48.620177(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 eren23/dpo-binarized-NeuralTrix-7B\n\n\n\nDataset automatically created during the evaluation run of model eren23/dpo-binarized-NeuralTrix-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 2024-02-11T20:45:49.015685(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 eren23/dpo-binarized-NeuralTrix-7B\n\n\n\nDataset automatically created during the evaluation run of model eren23/dpo-binarized-NeuralTrix-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 2024-02-11T20:45:49.015685(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:image-classification", "size_categories:10K<n<100K", "license:cc-by-4.0", "arxiv:2101.08515", "region:us" ]

[ "2101.08515" ]

[]

[ "# FractalDB 60\n\nFractalDB 60 dataset from Pre-training without Natural Images.\n\nOriginal repo | Project page | arXiv", "## Citing" ]

[ "TAGS\n#task_categories-image-classification #size_categories-10K<n<100K #license-cc-by-4.0 #arxiv-2101.08515 #region-us \n", "# FractalDB 60\n\nFractalDB 60 dataset from Pre-training without Natural Images.\n\nOriginal repo | Project page | arXiv", "## Citing" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Radu1999/MisterUkrainianDPO\n\n\n\nDataset automatically created during the evaluation run of model Radu1999/MisterUkrainianDPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T21:42:01.189462(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Radu1999/MisterUkrainianDPO\n\n\n\nDataset automatically created during the evaluation run of model Radu1999/MisterUkrainianDPO on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T21:42:01.189462(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:translation", "language:eu", "language:en", "language:es", "language:fr", "region:us" ]

[]

[ "eu", "en", "es", "fr" ]

[ "## Sources of the corpus used\n\n- Opus\n- Orai" ]

[ "TAGS\n#task_categories-translation #language-Basque #language-English #language-Spanish #language-French #region-us \n", "## Sources of the corpus used\n\n- Opus\n- Orai" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of yam-peleg/Experiment8-7B\n\n\n\nDataset automatically created during the evaluation run of model yam-peleg/Experiment8-7B 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 2024-02-11T22:11:17.659177(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 yam-peleg/Experiment8-7B\n\n\n\nDataset automatically created during the evaluation run of model yam-peleg/Experiment8-7B 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 2024-02-11T22:11:17.659177(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 max-2022/test_mistral2\n\n\n\nDataset automatically created during the evaluation run of model max-2022/test_mistral2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T22:17:01.815383(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 max-2022/test_mistral2\n\n\n\nDataset automatically created during the evaluation run of model max-2022/test_mistral2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T22:17:01.815383(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:image-classification", "size_categories:10M<n<100M", "license:cc-by-4.0", "arxiv:2101.08515", "region:us" ]

[ "2101.08515" ]

[]

[ "# FractalDB 1k\n\nFractalDB 1k dataset from Pre-training without Natural Images.\n\nOriginal repo | Project page | arXiv", "## Citing" ]

[ "TAGS\n#task_categories-image-classification #size_categories-10M<n<100M #license-cc-by-4.0 #arxiv-2101.08515 #region-us \n", "# FractalDB 1k\n\nFractalDB 1k dataset from Pre-training without Natural Images.\n\nOriginal repo | Project page | arXiv", "## Citing" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of jsfs11/MixtureofMerges-MoE-4x7b-v4\n\n\n\nDataset automatically created during the evaluation run of model jsfs11/MixtureofMerges-MoE-4x7b-v4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T22:50:28.150715(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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/MixtureofMerges-MoE-4x7b-v4\n\n\n\nDataset automatically created during the evaluation run of model jsfs11/MixtureofMerges-MoE-4x7b-v4 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T22:50:28.150715(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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:1M<n<10M", "language:en", "license:other", "math", "code", "nvidia", "arxiv:2402.10176", "region:us" ]

[ "2402.10176" ]

[ "en" ]

[ "# OpenMathInstruct-1\n\nOpenMathInstruct-1 is a math instruction tuning dataset with 1.8M problem-solution pairs\ngenerated using permissively licensed Mixtral-8x7B model.\n\nThe problems are from GSM8K\nand MATH training subsets and the solutions\nare synthetically generated by allowing Mixtral model to use a mix of text reasoning and\ncode blocks executed by Python interpreter.\n\nThe dataset is split into train and validation subsets that we used in the ablations experiments.\nThese two subsets combined together cover the full training set of GSM8K and MATH.\n\nOpenMathInstruct-1 dataset contains of the following fields:\n\n- question: original question from either GSM8K or MATH training set.\n- generated_solution: the synthetically generated solution that uses a mix of text reasoning and code blocks.\n- expected_answer: the ground-truth answer provided in the original dataset.\n- predicted_answer: the answer predicted by Mixtral model in the corresponding solution (extracted from '\\boxed{}').\n- error_message: '<not_executed>' if code was not used. Otherwise it's empty or contains a Python exception\n from the corresponding code block. A 'timeout' string indicates that code block took longer than 10 seconds to\n execute. In the current dataset version we always stop generation after any error or a timeout.\n- is_correct: whether the final answer was considered correct by our grading script.\n- dataset: gsm8k or math.\n- generation_type: 'without_reference_solution' or 'masked_reference_solution'.\n\nWe also release the masked solutions used to produce 'generation_type=\"masked_reference_solution\"'\nportion of the dataset (GSM8K-Masked,\nMATH-Masked).\n\nSee our paper to learn more details!", "## OpenMath models\n\nTo demonstrate the quality of this dataset, we release a series of OpenMath models\ntrained on this data (a combination of train and validation splits to allow comparison with prior work).\n\n<table border=\"1\">\n <tr>\n <td></td>\n <td colspan=\"2\" style=\"text-align: center;\">greedy</td>\n <td colspan=\"2\" style=\"text-align: center;\">majority@50</td>\n </tr>\n <tr>\n <td style=\"text-align: center;\">model</td>\n <td style=\"text-align: center;\">GSM8K</td>\n <td style=\"text-align: center;\">MATH</td>\n <td style=\"text-align: center;\">GMS8K</td>\n <td style=\"text-align: center;\">MATH</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">75.9</td>\n <td style=\"text-align: center;\">43.6</td>\n <td style=\"text-align: center;\">84.8</td>\n <td style=\"text-align: center;\">55.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Mistral-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.2</td>\n <td style=\"text-align: center;\">44.5</td>\n <td style=\"text-align: center;\">86.9</td>\n <td style=\"text-align: center;\">57.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-13B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">78.8</td>\n <td style=\"text-align: center;\">45.5</td>\n <td style=\"text-align: center;\">86.8</td>\n <td style=\"text-align: center;\">57.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-34B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.7</td>\n <td style=\"text-align: center;\">48.3</td>\n <td style=\"text-align: center;\">88.0</td>\n <td style=\"text-align: center;\">60.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Llama2-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\"><b>84.7</b></td>\n <td style=\"text-align: center;\">46.3</td>\n <td style=\"text-align: center;\">90.1</td>\n <td style=\"text-align: center;\">58.3</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">84.6</td>\n <td style=\"text-align: center;\"><b>50.7</b></td>\n <td style=\"text-align: center;\"><b>90.8</b></td>\n <td style=\"text-align: center;\"><b>60.4</b></td>\n </tr>\n</table>\n\nThe pipeline we used to produce the data and models is fully open-sourced!\n\n- Code\n- Models\n- Dataset", "## Reproducing our results\n\nWe provide all instructions\nto fully reproduce our results, including data generation.", "## Generating similar datasets\n\nTo generate similar datasets for other tasks or to learn more about our code, read through the docs below.\n\n- NeMo-Skills Pipeline\n - Generating synthetic data\n - Finetuning models\n - Evaluating models\n\nIf you find our work useful, please consider citing us!", "## License\n\nThe use of this dataset is governed by the NVIDIA License which permits commercial usage." ]

[ "TAGS\n#task_categories-question-answering #task_categories-text-generation #size_categories-1M<n<10M #language-English #license-other #math #code #nvidia #arxiv-2402.10176 #region-us \n", "# OpenMathInstruct-1\n\nOpenMathInstruct-1 is a math instruction tuning dataset with 1.8M problem-solution pairs\ngenerated using permissively licensed Mixtral-8x7B model.\n\nThe problems are from GSM8K\nand MATH training subsets and the solutions\nare synthetically generated by allowing Mixtral model to use a mix of text reasoning and\ncode blocks executed by Python interpreter.\n\nThe dataset is split into train and validation subsets that we used in the ablations experiments.\nThese two subsets combined together cover the full training set of GSM8K and MATH.\n\nOpenMathInstruct-1 dataset contains of the following fields:\n\n- question: original question from either GSM8K or MATH training set.\n- generated_solution: the synthetically generated solution that uses a mix of text reasoning and code blocks.\n- expected_answer: the ground-truth answer provided in the original dataset.\n- predicted_answer: the answer predicted by Mixtral model in the corresponding solution (extracted from '\\boxed{}').\n- error_message: '<not_executed>' if code was not used. Otherwise it's empty or contains a Python exception\n from the corresponding code block. A 'timeout' string indicates that code block took longer than 10 seconds to\n execute. In the current dataset version we always stop generation after any error or a timeout.\n- is_correct: whether the final answer was considered correct by our grading script.\n- dataset: gsm8k or math.\n- generation_type: 'without_reference_solution' or 'masked_reference_solution'.\n\nWe also release the masked solutions used to produce 'generation_type=\"masked_reference_solution\"'\nportion of the dataset (GSM8K-Masked,\nMATH-Masked).\n\nSee our paper to learn more details!", "## OpenMath models\n\nTo demonstrate the quality of this dataset, we release a series of OpenMath models\ntrained on this data (a combination of train and validation splits to allow comparison with prior work).\n\n<table border=\"1\">\n <tr>\n <td></td>\n <td colspan=\"2\" style=\"text-align: center;\">greedy</td>\n <td colspan=\"2\" style=\"text-align: center;\">majority@50</td>\n </tr>\n <tr>\n <td style=\"text-align: center;\">model</td>\n <td style=\"text-align: center;\">GSM8K</td>\n <td style=\"text-align: center;\">MATH</td>\n <td style=\"text-align: center;\">GMS8K</td>\n <td style=\"text-align: center;\">MATH</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">75.9</td>\n <td style=\"text-align: center;\">43.6</td>\n <td style=\"text-align: center;\">84.8</td>\n <td style=\"text-align: center;\">55.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Mistral-7B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.2</td>\n <td style=\"text-align: center;\">44.5</td>\n <td style=\"text-align: center;\">86.9</td>\n <td style=\"text-align: center;\">57.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-13B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">78.8</td>\n <td style=\"text-align: center;\">45.5</td>\n <td style=\"text-align: center;\">86.8</td>\n <td style=\"text-align: center;\">57.6</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-34B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">80.7</td>\n <td style=\"text-align: center;\">48.3</td>\n <td style=\"text-align: center;\">88.0</td>\n <td style=\"text-align: center;\">60.2</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-Llama2-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\"><b>84.7</b></td>\n <td style=\"text-align: center;\">46.3</td>\n <td style=\"text-align: center;\">90.1</td>\n <td style=\"text-align: center;\">58.3</td>\n </tr>\n <tr>\n <td style=\"text-align: right;\">OpenMath-CodeLlama-70B (<a href=\"URL | <a href=\"URL\n <td style=\"text-align: center;\">84.6</td>\n <td style=\"text-align: center;\"><b>50.7</b></td>\n <td style=\"text-align: center;\"><b>90.8</b></td>\n <td style=\"text-align: center;\"><b>60.4</b></td>\n </tr>\n</table>\n\nThe pipeline we used to produce the data and models is fully open-sourced!\n\n- Code\n- Models\n- Dataset", "## Reproducing our results\n\nWe provide all instructions\nto fully reproduce our results, including data generation.", "## Generating similar datasets\n\nTo generate similar datasets for other tasks or to learn more about our code, read through the docs below.\n\n- NeMo-Skills Pipeline\n - Generating synthetic data\n - Finetuning models\n - Evaluating models\n\nIf you find our work useful, please consider citing us!", "## License\n\nThe use of this dataset is governed by the NVIDIA License which permits commercial usage." ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of chanwit/flux-base-optimized\n\n\n\nDataset automatically created during the evaluation run of model chanwit/flux-base-optimized on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T23:31:14.212913(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 chanwit/flux-base-optimized\n\n\n\nDataset automatically created during the evaluation run of model chanwit/flux-base-optimized on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-11T23:31:14.212913(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"Calc-ape210k_selftrain_experiment\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Calc-ape210k_selftrain_experiment\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Calc-ape210k_selftrain_experiment_melted\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Calc-ape210k_selftrain_experiment_melted\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Calc-ape210k_selftrain_experiment_prompted\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"Calc-ape210k_selftrain_experiment_prompted\"\n\nMore Information needed" ]

[ "task_categories:text2text-generation", "size_categories:1K<n<10K", "language:fa", "license:apache-2.0", "region:us" ]

[]

[ "fa" ]

[ "# Persian OpenAssistant-Guanaco Dataset", "## About ZharfaTech\nZharfaTech is at the forefront of developing advanced Language Learning Models (LLMs) specifically for the Persian language, aiming to empower over 100 million Persian speakers worldwide. Our objective is to bridge the digital gap in services leveraging LLMs, such as content generation, translation, and customer relationship systems, by providing tailored open-source and closed-source LLM solutions. We focus on democratizing access to LLM technology for Persian language users, developers, and businesses, fostering innovation and collaboration within the community.", "## Dataset Overview\nThis dataset is the Persian translation of the \"openassistant-guanaco\" dataset, originally found at URL It has been translated to cater to the nuances of the Persian language, utilizing a high-performance local translation model. The translation process was completed in 12 hours using a single Nvidia GPU, ensuring a blend of speed and accuracy.", "### Key Features:\n- Language: Persian\n- Source: Translated from \"openassistant-guanaco\"\n- Translation Method: Local transitional model\n- Processing Time: 12 hours on a single Nvidia GPU", "## Objective and Scope\nZharfaTech is dedicated to enhancing the capabilities and reach of LLM technologies for the Persian language through:\n- Development of fine-tuned open-source models for the Persian language.\n- Creation of specialized datasets to support extensive training and refinement.\n- Advanced closed-source model development for specialized solutions.\n\nOur dual approach of fostering community collaboration and providing high-value, specialized solutions aims to advance LLM technologies for the Persian language, making significant strides towards inclusivity and accessibility in digital services.", "## How to Use This Dataset\nThis dataset is intended for researchers, developers, and businesses interested in developing Persian language capabilities in their LLMs. It can be used to train models for a variety of applications, including but not limited to natural language understanding, content generation, and customer interaction systems.\n\nTo access and utilize this dataset, please follow the instructions below:\n1. Visit our dataset page on Hugging Face: [URL\n2. Review the dataset documentation for details on structure and content.\n3. Download the dataset using the provided Hugging Face commands or API.", "## Contributing\nWe welcome contributions from the community to improve and expand this dataset.", "## Acknowledgments\nWe extend our gratitude to the creators of the \"openassistant-guanaco\" dataset for providing the foundation for this translation. Our thanks also go to the dedicated team members who utilized their expertise to ensure the accuracy and relevance of this Persian translation.", "## License\nThis dataset is available under an apache-2.0 license, aligning with the original \"openassistant-guanaco\" dataset's licensing terms. For more information, please review the license details on our dataset page.", "## Contact Us\nFor more information about ZharfaTech and our projects, or if you have any questions regarding this dataset, please contact us at [URL].\n\n---\n\nZharfaTech: Empowering Persian language speakers with advanced LLM technology." ]

[ "TAGS\n#task_categories-text2text-generation #size_categories-1K<n<10K #language-Persian #license-apache-2.0 #region-us \n", "# Persian OpenAssistant-Guanaco Dataset", "## About ZharfaTech\nZharfaTech is at the forefront of developing advanced Language Learning Models (LLMs) specifically for the Persian language, aiming to empower over 100 million Persian speakers worldwide. Our objective is to bridge the digital gap in services leveraging LLMs, such as content generation, translation, and customer relationship systems, by providing tailored open-source and closed-source LLM solutions. We focus on democratizing access to LLM technology for Persian language users, developers, and businesses, fostering innovation and collaboration within the community.", "## Dataset Overview\nThis dataset is the Persian translation of the \"openassistant-guanaco\" dataset, originally found at URL It has been translated to cater to the nuances of the Persian language, utilizing a high-performance local translation model. The translation process was completed in 12 hours using a single Nvidia GPU, ensuring a blend of speed and accuracy.", "### Key Features:\n- Language: Persian\n- Source: Translated from \"openassistant-guanaco\"\n- Translation Method: Local transitional model\n- Processing Time: 12 hours on a single Nvidia GPU", "## Objective and Scope\nZharfaTech is dedicated to enhancing the capabilities and reach of LLM technologies for the Persian language through:\n- Development of fine-tuned open-source models for the Persian language.\n- Creation of specialized datasets to support extensive training and refinement.\n- Advanced closed-source model development for specialized solutions.\n\nOur dual approach of fostering community collaboration and providing high-value, specialized solutions aims to advance LLM technologies for the Persian language, making significant strides towards inclusivity and accessibility in digital services.", "## How to Use This Dataset\nThis dataset is intended for researchers, developers, and businesses interested in developing Persian language capabilities in their LLMs. It can be used to train models for a variety of applications, including but not limited to natural language understanding, content generation, and customer interaction systems.\n\nTo access and utilize this dataset, please follow the instructions below:\n1. Visit our dataset page on Hugging Face: [URL\n2. Review the dataset documentation for details on structure and content.\n3. Download the dataset using the provided Hugging Face commands or API.", "## Contributing\nWe welcome contributions from the community to improve and expand this dataset.", "## Acknowledgments\nWe extend our gratitude to the creators of the \"openassistant-guanaco\" dataset for providing the foundation for this translation. Our thanks also go to the dedicated team members who utilized their expertise to ensure the accuracy and relevance of this Persian translation.", "## License\nThis dataset is available under an apache-2.0 license, aligning with the original \"openassistant-guanaco\" dataset's licensing terms. For more information, please review the license details on our dataset page.", "## Contact Us\nFor more information about ZharfaTech and our projects, or if you have any questions regarding this dataset, please contact us at [URL].\n\n---\n\nZharfaTech: Empowering Persian language speakers with advanced LLM technology." ]

[ "language:en", "code", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#language-English #code #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpacaV2-4bit\n\n\n\nDataset automatically created during the evaluation run of model robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpacaV2-4bit on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-12T16:17:26.640039(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpacaV2-4bit\n\n\n\nDataset automatically created during the evaluation run of model robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpacaV2-4bit on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-12T16:17:26.640039(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 yam-peleg/Experiment9-7B\n\n\n\nDataset automatically created during the evaluation run of model yam-peleg/Experiment9-7B 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 2024-02-12T00:42:08.192431(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 yam-peleg/Experiment9-7B\n\n\n\nDataset automatically created during the evaluation run of model yam-peleg/Experiment9-7B 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 2024-02-12T00:42:08.192431(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 rizla/rizla-11\n\n\n\nDataset automatically created during the evaluation run of model rizla/rizla-11 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-12T01:26:02.401376(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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-11\n\n\n\nDataset automatically created during the evaluation run of model rizla/rizla-11 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-12T01:26:02.401376(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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" ]

[]

[]

[ "# Guanaco-1k: Lazy Llama 2 Formatting\n\nThis is a subset (1000 samples) of the excellent 'timdettmers/openassistant-guanaco' dataset, processed to match Llama 2's prompt format as described in this article. It was created using the following colab notebook.\n\nUseful if you don't want to reformat it by yourself (e.g., using a script). It was designed for this article about fine-tuning a Llama 2 (chat) model in a Google Colab." ]

[ "TAGS\n#region-us \n", "# Guanaco-1k: Lazy Llama 2 Formatting\n\nThis is a subset (1000 samples) of the excellent 'timdettmers/openassistant-guanaco' dataset, processed to match Llama 2's prompt format as described in this article. It was created using the following colab notebook.\n\nUseful if you don't want to reformat it by yourself (e.g., using a script). It was designed for this article about fine-tuning a Llama 2 (chat) model in a Google Colab." ]

[ "region:us" ]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"final_c_x86_O0_exebench_numeric_full_2k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"final_c_x86_O0_exebench_numeric_full_2k\"\n\nMore Information needed" ]

[ "language:en", "license:apache-2.0", "Protein Langauge Model", "AI for Drug Discovery", "AI for Science", "arxiv:2203.06125", "arxiv:2303.06275", "arxiv:2402.05856", "region:us" ]

[ "2203.06125", "2303.06275", "2402.05856" ]

[ "en" ]

[]

[ "TAGS\n#language-English #license-apache-2.0 #Protein Langauge Model #AI for Drug Discovery #AI for Science #arxiv-2203.06125 #arxiv-2303.06275 #arxiv-2402.05856 #region-us \n" ]

[ "region:us" ]

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[ "# Dataset Card for \"hanna-gemini-annotated\"\n\nMore Information needed" ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Gille/StrangeMerges_21-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Gille/StrangeMerges_21-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-12T03:48:30.394342(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Gille/StrangeMerges_21-7B-slerp\n\n\n\nDataset automatically created during the evaluation run of model Gille/StrangeMerges_21-7B-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-12T03:48:30.394342(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:text-generation", "task_categories:question-answering", "task_ids:parsing", "size_categories:1M<n<10M", "license:other", "python-copilot", "python-coding", "python-architecture", "knowledge-graphs", "multimodal", "text-image-audio", "fine-tuning", "training", "question-answering", "image-knowledge-graph", "alpaca", "mp3", "png", "text", "instruct", "coding", "task", "prompt", "response", "yaml", "region:us" ]

[]

[]

[ "## Python Copilot Instructions on How to Code using Alpaca and Yaml\n\nTraining and test datasets for building coding multimodal models that understand how to use the open source GitHub projects for the Autogen and multimodal Qwen AI project:\n\n- Qwen\n- Qwen Agent\n- Qwen VL Chat\n- Qwen Audio\n\nThis dataset is the 2024-02-11 update for the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.", "### Details\n\nEach row contains python code, either a class method or a global function, imported modules, base classes (if any), exceptions (ordered based off the code), returns (ordered based off the code), arguments (ordered based off the code), and more.\n\n- Rows: 1075795\n- Size: 1.8 GB\n- Data type: instruct\n- Format: Introduction on code usage using alpaca and yaml response\n- Number of python repos: 1275", "### How to use the datasets", "#### Load Autogen Schema Dataset", "### Schema\n\nThe instruction alpaca text with yaml response is in the desc column:" ]

[ "TAGS\n#task_categories-text-generation #task_categories-question-answering #task_ids-parsing #size_categories-1M<n<10M #license-other #python-copilot #python-coding #python-architecture #knowledge-graphs #multimodal #text-image-audio #fine-tuning #training #question-answering #image-knowledge-graph #alpaca #mp3 #png #text #instruct #coding #task #prompt #response #yaml #region-us \n", "## Python Copilot Instructions on How to Code using Alpaca and Yaml\n\nTraining and test datasets for building coding multimodal models that understand how to use the open source GitHub projects for the Autogen and multimodal Qwen AI project:\n\n- Qwen\n- Qwen Agent\n- Qwen VL Chat\n- Qwen Audio\n\nThis dataset is the 2024-02-11 update for the matlok python copilot datasets. Please refer to the Multimodal Python Copilot Training Overview for more details on how to use this dataset.", "### Details\n\nEach row contains python code, either a class method or a global function, imported modules, base classes (if any), exceptions (ordered based off the code), returns (ordered based off the code), arguments (ordered based off the code), and more.\n\n- Rows: 1075795\n- Size: 1.8 GB\n- Data type: instruct\n- Format: Introduction on code usage using alpaca and yaml response\n- Number of python repos: 1275", "### How to use the datasets", "#### Load Autogen Schema Dataset", "### Schema\n\nThe instruction alpaca text with yaml response is in the desc column:" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of JaeyeonKang/CCK_Asura_v1\n\n\n\nDataset automatically created during the evaluation run of model JaeyeonKang/CCK_Asura_v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-12T04:58:51.033818(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 JaeyeonKang/CCK_Asura_v1\n\n\n\nDataset automatically created during the evaluation run of model JaeyeonKang/CCK_Asura_v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-12T04:58:51.033818(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"final_c_x86_O0_exebench_numeric_full_json_cleaned\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"final_c_x86_O0_exebench_numeric_full_json_cleaned\"\n\nMore Information needed" ]

[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]

[]

[]

[]

[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]

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

[]

[]

[ "# Medbase_data: Multilingual Medical LLM Evaluation (EN/ZH/ES/FR/AR/HI)\n\n<p align=\"center\">\n <a href=\"\" target=\"_blank\">Paper</a> • <a href=\"URL target=\"_blank\">Github</a> • <a href=\"URL target=\"_blank\">HuggingFace</a> \n <br> \n</p>\n\n\n\n!Medbase", "## Usage\n\n- Zip File\n- Data category\n- question list: A collection of questions for training data leakage self-examination", "## Data:\n\n- EN:\n - MedQA-USMLE \n - MedMCQA\n - PubMedQA\n - MMLU-Medical Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine\n- ZH:\n - MedQA-MCMLE\n - CMB-single Randomly sample 2,000 multiple-choice questions with single answer.\n - CMMLU-Medical Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology\n - CExam Randomly sample 2,000 multiple-choice questions\n\n\n- ES: Head_qa\n- FR: Frenchmedmcqa\n- HI: MMLU_Medical_HI Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine\n- AR: MMLU_Medical_Ara Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Medbase_data: Multilingual Medical LLM Evaluation (EN/ZH/ES/FR/AR/HI)\n\n<p align=\"center\">\n <a href=\"\" target=\"_blank\">Paper</a> • <a href=\"URL target=\"_blank\">Github</a> • <a href=\"URL target=\"_blank\">HuggingFace</a> \n <br> \n</p>\n\n\n\n!Medbase", "## Usage\n\n- Zip File\n- Data category\n- question list: A collection of questions for training data leakage self-examination", "## Data:\n\n- EN:\n - MedQA-USMLE \n - MedMCQA\n - PubMedQA\n - MMLU-Medical Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine\n- ZH:\n - MedQA-MCMLE\n - CMB-single Randomly sample 2,000 multiple-choice questions with single answer.\n - CMMLU-Medical Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology\n - CExam Randomly sample 2,000 multiple-choice questions\n\n\n- ES: Head_qa\n- FR: Frenchmedmcqa\n- HI: MMLU_Medical_HI Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine\n- AR: MMLU_Medical_Ara Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine" ]

[ "region:us" ]

[]

[]

[ "# Data Card for Augmented Australian Legal QA Dataset with Embeddings", "## Dataset Description\n\nThis dataset serves as a precursor to the Open Australian Legal QA dataset and Open Australian Legal QA Paraphrased Questions dataset , enriched with both retrieval and non-retrieval embeddings. It was generated to facilitate advanced NLP applications in legal question answering and information retrieval, leveraging Angle Embeddings for creating semantically rich vector representations.", "## Dataset Structure and Column Types\n\nThe main dataset csv consists of 10,620 entries with the following columns:\n\n- 'question': Paraphrased legal questions (string).\n- 'answer': Answers to the questions (string).\n- 'text': Additional contextual information or details (string).\n- 'prompt': The prompt used for generating or paraphrasing the content (string).\n- 'source': Metadata about the source document, including citation and jurisdiction (string).\n- Embedding columns, each containing a 1D list with 4096 dimensions:\n - 'question_non_retrieval_embeddings'\n - 'answer_retrieval_embeddings'\n - 'answer_non_retrieval_embeddings'\n - 'question_retrieval_embeddings'\n - 'text_non_retrieval_embeddings'\n - 'text_retrieval_embeddings'\n\nThese embeddings are designed to support a range of NLP tasks, from content retrieval to deep semantic analysis.", "## Loading the Main Dataset CSV\n\n\n\n\nThis code snippet demonstrates loading the dataframe from a CSV file.", "## Loading Embedding Assets and Mapping to DataFrame\n\n\nThis process demonstrates how to load embedding vectors stored as JSON files and map them to the appropriate columns in your dataframe based on the content's nature and the type of retrieval they are intended URL manipulation and analysis." ]

[ "TAGS\n#region-us \n", "# Data Card for Augmented Australian Legal QA Dataset with Embeddings", "## Dataset Description\n\nThis dataset serves as a precursor to the Open Australian Legal QA dataset and Open Australian Legal QA Paraphrased Questions dataset , enriched with both retrieval and non-retrieval embeddings. It was generated to facilitate advanced NLP applications in legal question answering and information retrieval, leveraging Angle Embeddings for creating semantically rich vector representations.", "## Dataset Structure and Column Types\n\nThe main dataset csv consists of 10,620 entries with the following columns:\n\n- 'question': Paraphrased legal questions (string).\n- 'answer': Answers to the questions (string).\n- 'text': Additional contextual information or details (string).\n- 'prompt': The prompt used for generating or paraphrasing the content (string).\n- 'source': Metadata about the source document, including citation and jurisdiction (string).\n- Embedding columns, each containing a 1D list with 4096 dimensions:\n - 'question_non_retrieval_embeddings'\n - 'answer_retrieval_embeddings'\n - 'answer_non_retrieval_embeddings'\n - 'question_retrieval_embeddings'\n - 'text_non_retrieval_embeddings'\n - 'text_retrieval_embeddings'\n\nThese embeddings are designed to support a range of NLP tasks, from content retrieval to deep semantic analysis.", "## Loading the Main Dataset CSV\n\n\n\n\nThis code snippet demonstrates loading the dataframe from a CSV file.", "## Loading Embedding Assets and Mapping to DataFrame\n\n\nThis process demonstrates how to load embedding vectors stored as JSON files and map them to the appropriate columns in your dataframe based on the content's nature and the type of retrieval they are intended URL manipulation and analysis." ]

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

[]

[]

[ "# Overview\n\nOriginal from the sentences-transformers library.\n\nOnly for researching purposes.\n\nAdapter by Aisuko", "# Installation", "# Computing Embeddings for a large set of sentences", "# Save the csv file" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Overview\n\nOriginal from the sentences-transformers library.\n\nOnly for researching purposes.\n\nAdapter by Aisuko", "# Installation", "# Computing Embeddings for a large set of sentences", "# Save the csv file" ]

[ "task_categories:other", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:CGL-Dataset", "language:zh", "license:unknown", "graphic design", "arxiv:2306.09086", "arxiv:2005.00614", "region:us" ]

[ "2306.09086", "2005.00614" ]

[ "zh" ]

[ "# Dataset Card for CGL-Dataset-v2\n\n![CI](URL\n![Sync HF](URL", "## Table of Contents\n- Dataset Card Creation Guide\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 - Initial Data Collection and Normalization\n - Who are the source language producers?\n - Annotations\n - Annotation process\n - Who are the annotators?\n - Personal and Sensitive Information\n - Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n - Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper (Preprint): URL\n- Paper (CIKM'23): URL", "### Dataset Summary\n\nCGL-Dataset V2 is a dataset for the task of automatic graphic layout design of advertising posters, containing 60,548 training samples and 1035 testing samples. It is an extension of CGL-Dataset.", "### Supported Tasks and Leaderboards", "### Languages\n\nThe language data in CGL-Dataset v2 is in Chinese (BCP-47 zh).", "## Dataset Structure", "### Data Instances\n\nTo use CGL-Dataset v2 dataset, you need to download 'RADM_dataset.URL' that includes the poster image, text and text features via JD Cloud or Google Drive.\nThen place the downloaded files in the following structure and specify its path.", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @liuan0803 for creating this dataset." ]

[ "TAGS\n#task_categories-other #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #source_datasets-CGL-Dataset #language-Chinese #license-unknown #graphic design #arxiv-2306.09086 #arxiv-2005.00614 #region-us \n", "# Dataset Card for CGL-Dataset-v2\n\n![CI](URL\n![Sync HF](URL", "## Table of Contents\n- Dataset Card Creation Guide\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 - Initial Data Collection and Normalization\n - Who are the source language producers?\n - Annotations\n - Annotation process\n - Who are the annotators?\n - Personal and Sensitive Information\n - Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n - Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions", "## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper (Preprint): URL\n- Paper (CIKM'23): URL", "### Dataset Summary\n\nCGL-Dataset V2 is a dataset for the task of automatic graphic layout design of advertising posters, containing 60,548 training samples and 1035 testing samples. It is an extension of CGL-Dataset.", "### Supported Tasks and Leaderboards", "### Languages\n\nThe language data in CGL-Dataset v2 is in Chinese (BCP-47 zh).", "## Dataset Structure", "### Data Instances\n\nTo use CGL-Dataset v2 dataset, you need to download 'RADM_dataset.URL' that includes the poster image, text and text features via JD Cloud or Google Drive.\nThen place the downloaded files in the following structure and specify its path.", "### Data Fields", "### Data Splits", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information", "## Considerations for Using the Data", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations", "## Additional Information", "### Dataset Curators", "### Licensing Information", "### Contributions\n\nThanks to @liuan0803 for creating this dataset." ]

[ "region:us" ]

[]

[]

[]

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

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

[]

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[ "### Data download\n\n\n* Zip File\n* Data category\n\t+ Pretrain:\n\t\n\t\n\tClick to expand\n\t\n\t\t- json\\_name: {data\\_source}\\_{language}\\_{data\\_type}.json\n\t\t\t* data\\_source: medicalBook, medicalGuideline, medicalPaper, medicalWeb(from online forum), medicalWiki\n\t\t\t* language: en(English), zh(chinese), es(spanish), fr(french), hi(Hindi)\n\t\t\t* data\\_type: qa(generated qa from text)\n\t\t- data item:\n\t\t\t* data\\_type==text: list of string\n\t\t\t* data\\_type==qa: list of qa pairs(list of string)\n\t+ SFT:\n\t\n\t\n\tClick to expand\n\t\n\t\t- json\\_name: {data\\_source}\\_{language}(\\_clean/dup).json\n\t\t\t* data\\_type: code, general, math, medicalExam, medicalPatient\n\t\t\t* Data leakage exclusion\n\t\t\t\t+ clean: Data set after removing problems that appear in Medbase\\_eval\n\t\t\t\t+ dup: Data pairs that appear in Medbase\\_eval\n\t\t- data item: list of qa pairs(list of string)", "### Data leakage exclusion\n\n\nData leakage exclusion : Remove data that appears simultaneously in Medbase\\_eval and Mebase\\_data/medicalExam\n\n\nTaking into account the differences in everyone's evaluation sets, the data in the zip file is an unchecked version for data leakage.\n\n\nYou must conduct a data leakage check before use.\n\n\nData: Huge, Diverse, Clean, Multilingual\n----------------------------------------" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "### Data download\n\n\n* Zip File\n* Data category\n\t+ Pretrain:\n\t\n\t\n\tClick to expand\n\t\n\t\t- json\\_name: {data\\_source}\\_{language}\\_{data\\_type}.json\n\t\t\t* data\\_source: medicalBook, medicalGuideline, medicalPaper, medicalWeb(from online forum), medicalWiki\n\t\t\t* language: en(English), zh(chinese), es(spanish), fr(french), hi(Hindi)\n\t\t\t* data\\_type: qa(generated qa from text)\n\t\t- data item:\n\t\t\t* data\\_type==text: list of string\n\t\t\t* data\\_type==qa: list of qa pairs(list of string)\n\t+ SFT:\n\t\n\t\n\tClick to expand\n\t\n\t\t- json\\_name: {data\\_source}\\_{language}(\\_clean/dup).json\n\t\t\t* data\\_type: code, general, math, medicalExam, medicalPatient\n\t\t\t* Data leakage exclusion\n\t\t\t\t+ clean: Data set after removing problems that appear in Medbase\\_eval\n\t\t\t\t+ dup: Data pairs that appear in Medbase\\_eval\n\t\t- data item: list of qa pairs(list of string)", "### Data leakage exclusion\n\n\nData leakage exclusion : Remove data that appears simultaneously in Medbase\\_eval and Mebase\\_data/medicalExam\n\n\nTaking into account the differences in everyone's evaluation sets, the data in the zip file is an unchecked version for data leakage.\n\n\nYou must conduct a data leakage check before use.\n\n\nData: Huge, Diverse, Clean, Multilingual\n----------------------------------------" ]

[ "region:us" ]

[]

[]

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

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

[ "region:us" ]

[]

[]

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

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

[ "task_categories:translation", "language:en", "license:mit", "code", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-translation #language-English #license-mit #code #region-us \n" ]

[ "size_categories:1K<n<10K", "license:mit", "art", "region:us" ]

[]

[]

[]

[ "TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n" ]

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

[]

[ "en" ]

[]

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

[ "task_categories:conversational", "language:en", "not-for-all-audiences", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-conversational #language-English #not-for-all-audiences #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of liminerity/binarized-ingotrix-slerp-7b\n\n\n\nDataset automatically created during the evaluation run of model liminerity/binarized-ingotrix-slerp-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-12T08:08:55.496275(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 liminerity/binarized-ingotrix-slerp-7b\n\n\n\nDataset automatically created during the evaluation run of model liminerity/binarized-ingotrix-slerp-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-02-12T08:08:55.496275(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]