Datasets:

---

ftopal

/

huggingface-datasets

like 0

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

[]

[]

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

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

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

[]

[]

[ "### Dataset Card for LangChain Issues", "#### Dataset Summary\n\nLangChain Issues is a dataset consisting of LangChain issues and pull requests associated with the LangChain repository (URL \nIt is intended for educational purposes and can be used for semantic search or multilabel text classification. \nThe contents of each LangChain issue are in English and concern the domain of datasets for NLP, computer vision, and beyond." ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "### Dataset Card for LangChain Issues", "#### Dataset Summary\n\nLangChain Issues is a dataset consisting of LangChain issues and pull requests associated with the LangChain repository (URL \nIt is intended for educational purposes and can be used for semantic search or multilabel text classification. \nThe contents of each LangChain issue are in English and concern the domain of datasets for NLP, computer vision, and beyond." ]

[ "region:us" ]

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[ "# Dataset Card for \"commonsense_generated_answers_2question\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"commonsense_generated_answers_2question\"\n\nMore Information needed" ]

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"covost2_synth\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"covost2_synth\"\n\nMore Information needed" ]

[ "size_categories:n<1K", "language:en", "license:pddl", "climate", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Dataset Name\n\nA dataset of 100 corporate sustainability reports with manually extracted scope 1, 2 and 3 greenhouse gas emission values.", "## Dataset Details", "### Dataset Description\n\nData about corporate greenhouse gas emissions is usually published only as part of sustainability report PDF's, which is not a machine-readable format. Interested actors have to manually extract emission data from these reports, which is a tedious and time-consuming process. An automatic information-extraction system could solve this issue.\n\nTo evaluate such information-extraction systems and to encourage research into solving this task, a dataset of sustainability reports and manually-extracted emission values is created and published.\n\nWhile this dataset is intended for evaluation, the accompanying sustainability-report-emissions dataset is intended for training/finetuning models.\n\n- License: Open Data Commons Public Domain Dedication and License (PDDL)", "### Dataset Sources [optional]\n\n- Repository: URL\n- Paper [optional]: \n- Demo [optional]:", "## Uses\n\nThe dataset is intended to be used to evaluate automatic systems to extract machine-readable greenhouse gas emission data from sustainability reports.", "## Dataset Structure\n\n- 'id' (string): unique instance id, ex. '\"0012\"'.\n- 'emission_year' (int): the year of the extracted emissions, which is useful in reports which contain information about multiple years.\n- 'scope_1' (double): total scope 1 emissions in metric tonnes of CO2eq.\n- 'scope_2_market' (double): total market-based scope 2 emissions in metric tonnes of CO2eq.\n- 'scope_2_location' (double): total location-based scope 2 emissions in metric tonnes of CO2eq.\n- 'scope_3' (double): total scope 3 emissions in metric tonnes of CO2eq.\n- 'scope_1_page' (list<int>): set of pages containing total scope 1 emission data.\n- 'scope_2_market_page' (list<int>): set of pages containing total market-based scope 2 emission data.\n- 'scope_2_location_page' (list<int>): set of pages containing total location-based scope 2 emission data.\n- 'scope_3_page' (list<int>): set of pages containing total scope 1 emission data.\n- 'url' (string): the URL to the sustainability report PDF.\n- 'sha256' (string): SHA-256 hash string of the report PDF to ensure the integrity of downloaded files.\n- 'subset' (string): indication of whether the report comes from the set of Euro Stoxx 50 ('eurostoxx'), NYSE ('nyse') or Nikkei 225 ('tyo') corporations.\n\nThe remaining 15 fields contain the data for each of the 15 scope 3 emission categories.\n\nThe dataset only contains the URL to the report PDF. A helper script to download these files is provided at: URL", "## Dataset Creation", "### Curation Rationale\n\nTo our knowledge, there is no publicly-available dataset containing manually extracted (self-reported) greenhouse gas emission data from sustainability reports. Hence, this dataset was collected to enable the evaluation of automatic information-extraction systems.", "### Source Data\n\nThe dataset is based on sustainability reports from corporations in Europe, North America and Asia.", "#### Data Collection and Processing\n\nTo ensure geographic diversity, the sustainability reports are sourced from three sets of corporations. The first set consists of 39 corporations tracked by the Euro Stoxx 50 stock index as of 18 September 2023. Note that out of the missing 11 corporations of Euro Stoxx 50, 5 are not considered as no publicly-available sustainability reports were found and 6 are not considered it was not possible to extract unambiguous emission values. The second set is a random selection of 39 corporations listed on the New York Stock Exchange as of December 2023. The third set is a random selection of 22 corporations tracked by the Nikkei 225 index as of October 2023. Note, that this selection strategy is intentionally biased towards larger corporations due to the assumption that they have a higher likelihood of publishing sustainability reports.\n\nFor every corporation, the most recent sustainability report is downloaded from the official source. In some cases, the sustainability report is part of a larger annual report.\n\nSustainability reports based on the Carbon Disclosure Project or Global Reporting Initiative templates are not considered as they already follow a consistent structure.", "#### Who are the source data producers?\n\nThe sustainability reports are produced by corporations themselves and optionally verified by third parties. Thus, they only contain self-reported emission information.", "### Annotations [optional]\n\nThe sustainability reports are annotated with manually-extracted emission data, which forms the main purpose of this dataset.", "#### Annotation process\n\nThe annotation was based on the greenhouse gas emission definitions of the GHG Protocol Corporate Standard:\n\n- Scope 1: A reporting organization’s direct GHG emissions.\n- Scope 2: A reporting organization’s emissions associated with the generation of electricity, heating/cooling, or steam purchased for own consumption.\n- Scope 3: A reporting organization’s indirect emissions other than those covered in scope 2.\n\nOnly emission data about the reporting corporation was extracted, invidiual values for subsidiaries were ignored.\n\nScope 2 emissions are annoted as market-based by default if no indication about the calculation method is given in the report.\n\nValues which could not be unambiguously extracted were noted as missing.\n\nNo automatic tools were used in the extraction process.\n\nThe extracted data is not validated by a third party or verified against other data sources.", "#### Who are the annotators?\n\nThe data was annotated by a single person without special expertise in sustainability reporting.", "#### Personal and Sensitive Information\n\nThe dataset contains only public information.", "## Bias, Risks, and Limitations\n\nThe emission information was extracted by a single non-expert. No guarantee can be given that the data is completely correct.\n\nThe dataset does not contain sustainability reports of small enterprises or non-profit organisations.\n\nEven though some care was taken to ensure geographic diversity, the dataset does not include sustainability reports from large parts of the world.\n\n[optional]\n\nBibTeX:" ]

[ "TAGS\n#size_categories-n<1K #language-English #license-pddl #climate #region-us \n", "# Dataset Card for Dataset Name\n\nA dataset of 100 corporate sustainability reports with manually extracted scope 1, 2 and 3 greenhouse gas emission values.", "## Dataset Details", "### Dataset Description\n\nData about corporate greenhouse gas emissions is usually published only as part of sustainability report PDF's, which is not a machine-readable format. Interested actors have to manually extract emission data from these reports, which is a tedious and time-consuming process. An automatic information-extraction system could solve this issue.\n\nTo evaluate such information-extraction systems and to encourage research into solving this task, a dataset of sustainability reports and manually-extracted emission values is created and published.\n\nWhile this dataset is intended for evaluation, the accompanying sustainability-report-emissions dataset is intended for training/finetuning models.\n\n- License: Open Data Commons Public Domain Dedication and License (PDDL)", "### Dataset Sources [optional]\n\n- Repository: URL\n- Paper [optional]: \n- Demo [optional]:", "## Uses\n\nThe dataset is intended to be used to evaluate automatic systems to extract machine-readable greenhouse gas emission data from sustainability reports.", "## Dataset Structure\n\n- 'id' (string): unique instance id, ex. '\"0012\"'.\n- 'emission_year' (int): the year of the extracted emissions, which is useful in reports which contain information about multiple years.\n- 'scope_1' (double): total scope 1 emissions in metric tonnes of CO2eq.\n- 'scope_2_market' (double): total market-based scope 2 emissions in metric tonnes of CO2eq.\n- 'scope_2_location' (double): total location-based scope 2 emissions in metric tonnes of CO2eq.\n- 'scope_3' (double): total scope 3 emissions in metric tonnes of CO2eq.\n- 'scope_1_page' (list<int>): set of pages containing total scope 1 emission data.\n- 'scope_2_market_page' (list<int>): set of pages containing total market-based scope 2 emission data.\n- 'scope_2_location_page' (list<int>): set of pages containing total location-based scope 2 emission data.\n- 'scope_3_page' (list<int>): set of pages containing total scope 1 emission data.\n- 'url' (string): the URL to the sustainability report PDF.\n- 'sha256' (string): SHA-256 hash string of the report PDF to ensure the integrity of downloaded files.\n- 'subset' (string): indication of whether the report comes from the set of Euro Stoxx 50 ('eurostoxx'), NYSE ('nyse') or Nikkei 225 ('tyo') corporations.\n\nThe remaining 15 fields contain the data for each of the 15 scope 3 emission categories.\n\nThe dataset only contains the URL to the report PDF. A helper script to download these files is provided at: URL", "## Dataset Creation", "### Curation Rationale\n\nTo our knowledge, there is no publicly-available dataset containing manually extracted (self-reported) greenhouse gas emission data from sustainability reports. Hence, this dataset was collected to enable the evaluation of automatic information-extraction systems.", "### Source Data\n\nThe dataset is based on sustainability reports from corporations in Europe, North America and Asia.", "#### Data Collection and Processing\n\nTo ensure geographic diversity, the sustainability reports are sourced from three sets of corporations. The first set consists of 39 corporations tracked by the Euro Stoxx 50 stock index as of 18 September 2023. Note that out of the missing 11 corporations of Euro Stoxx 50, 5 are not considered as no publicly-available sustainability reports were found and 6 are not considered it was not possible to extract unambiguous emission values. The second set is a random selection of 39 corporations listed on the New York Stock Exchange as of December 2023. The third set is a random selection of 22 corporations tracked by the Nikkei 225 index as of October 2023. Note, that this selection strategy is intentionally biased towards larger corporations due to the assumption that they have a higher likelihood of publishing sustainability reports.\n\nFor every corporation, the most recent sustainability report is downloaded from the official source. In some cases, the sustainability report is part of a larger annual report.\n\nSustainability reports based on the Carbon Disclosure Project or Global Reporting Initiative templates are not considered as they already follow a consistent structure.", "#### Who are the source data producers?\n\nThe sustainability reports are produced by corporations themselves and optionally verified by third parties. Thus, they only contain self-reported emission information.", "### Annotations [optional]\n\nThe sustainability reports are annotated with manually-extracted emission data, which forms the main purpose of this dataset.", "#### Annotation process\n\nThe annotation was based on the greenhouse gas emission definitions of the GHG Protocol Corporate Standard:\n\n- Scope 1: A reporting organization’s direct GHG emissions.\n- Scope 2: A reporting organization’s emissions associated with the generation of electricity, heating/cooling, or steam purchased for own consumption.\n- Scope 3: A reporting organization’s indirect emissions other than those covered in scope 2.\n\nOnly emission data about the reporting corporation was extracted, invidiual values for subsidiaries were ignored.\n\nScope 2 emissions are annoted as market-based by default if no indication about the calculation method is given in the report.\n\nValues which could not be unambiguously extracted were noted as missing.\n\nNo automatic tools were used in the extraction process.\n\nThe extracted data is not validated by a third party or verified against other data sources.", "#### Who are the annotators?\n\nThe data was annotated by a single person without special expertise in sustainability reporting.", "#### Personal and Sensitive Information\n\nThe dataset contains only public information.", "## Bias, Risks, and Limitations\n\nThe emission information was extracted by a single non-expert. No guarantee can be given that the data is completely correct.\n\nThe dataset does not contain sustainability reports of small enterprises or non-profit organisations.\n\nEven though some care was taken to ensure geographic diversity, the dataset does not include sustainability reports from large parts of the world.\n\n[optional]\n\nBibTeX:" ]

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"Gold-alpaca-legal\"\n\nMore Information needed" ]

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

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

[]

[]

[]

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

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

[]

[ "id" ]

[]

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

[]

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

[ "task_categories:image-classification", "task_categories:image-to-video", "size_categories:n<1K", "language:en", "license:cc-by-4.0", "self-supervised learning", "representation learning", "region:us" ]

[]

[ "en" ]

[ "## Overview\n\nThe Walking Tours dataset is a unique collection of long-duration egocentric videos captured in urban environments from cities in Europe and Asia. It consists of 10 high-resolution videos, each showcasing a person walking through a different environment, ranging from city centers to parks to residential areas, under different lighting conditions. A video from a Wildlife safari is also included to diversify the dataset with natural environments. The dataset is completely unlabeled and uncurated, making it suitable for self-supervised pretraining.", "## Cities Covered\n\nThe dataset encompasses walks through the following cities:\n\n- Amsterdam\n- Bangkok\n- Chiang Mai\n- Istanbul\n- Kuala Lumpur\n- Singapore\n- Stockholm\n- Venice\n- Zurich", "## Video Specifications\n\n- Resolution: 4K (3840 × 2160 pixels)\n- Frame Rate: 60 frames-per-second\n- License: Creative Commons License (CC-BY)", "## Duration\n\nThe videos vary in duration, offering a diverse range of content:\n\n- Minimum Duration: 59 minutes (Wildlife safari)\n- Maximum Duration: 2 hours 55 minutes (Bangkok)\n- Average Duration: 1 hour 38 minutes", "## Download the Dataset\n\nThe complete list of WTour videos are available in , comprising the YouTube link and the corresponding city. \n\nTo download the dataset, we first install pytube\n\n\nthen, we run \n\n\nIn order to comply with GDPR, we also try to blur out all faces and license plates appearing in the video using Deface \n\nTo do this for all videos in WTour dataset:\n\nThen run Deface on all videos using the bash script:\n\n\n\nIf you find this work useful and use it on your own research, please cite our paper:\n\n\n---" ]

[ "TAGS\n#task_categories-image-classification #task_categories-image-to-video #size_categories-n<1K #language-English #license-cc-by-4.0 #self-supervised learning #representation learning #region-us \n", "## Overview\n\nThe Walking Tours dataset is a unique collection of long-duration egocentric videos captured in urban environments from cities in Europe and Asia. It consists of 10 high-resolution videos, each showcasing a person walking through a different environment, ranging from city centers to parks to residential areas, under different lighting conditions. A video from a Wildlife safari is also included to diversify the dataset with natural environments. The dataset is completely unlabeled and uncurated, making it suitable for self-supervised pretraining.", "## Cities Covered\n\nThe dataset encompasses walks through the following cities:\n\n- Amsterdam\n- Bangkok\n- Chiang Mai\n- Istanbul\n- Kuala Lumpur\n- Singapore\n- Stockholm\n- Venice\n- Zurich", "## Video Specifications\n\n- Resolution: 4K (3840 × 2160 pixels)\n- Frame Rate: 60 frames-per-second\n- License: Creative Commons License (CC-BY)", "## Duration\n\nThe videos vary in duration, offering a diverse range of content:\n\n- Minimum Duration: 59 minutes (Wildlife safari)\n- Maximum Duration: 2 hours 55 minutes (Bangkok)\n- Average Duration: 1 hour 38 minutes", "## Download the Dataset\n\nThe complete list of WTour videos are available in , comprising the YouTube link and the corresponding city. \n\nTo download the dataset, we first install pytube\n\n\nthen, we run \n\n\nIn order to comply with GDPR, we also try to blur out all faces and license plates appearing in the video using Deface \n\nTo do this for all videos in WTour dataset:\n\nThen run Deface on all videos using the bash script:\n\n\n\nIf you find this work useful and use it on your own research, please cite our paper:\n\n\n---" ]

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

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

[]

[]

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

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

[ "region:us" ]

[]

[]

[]

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

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

[]

[]

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

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

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

[]

[]

[ "## Sports Image Classification dataset\n\nFrom Kaggle: 100 Sports Image Classification\n\nCollection of sports images covering 100 different sports. Images are 224x224x3 jpg format.\n\nData is separated into train, test and valid directories.\n\n* 13493 train images\n* 500 test images\n* 500 validate images\n\nAdditionallly a csv file is included for those that wish to use it to create there own train, test and validation datasets.", "### Clone\n\n\n\nor", "### Modify dataset\n\nTo add data, ensure to install LFS.\n\n\n\nThen proceed accordingly with 'git add' and 'git push'." ]

[ "TAGS\n#license-cc0-1.0 #region-us \n", "## Sports Image Classification dataset\n\nFrom Kaggle: 100 Sports Image Classification\n\nCollection of sports images covering 100 different sports. Images are 224x224x3 jpg format.\n\nData is separated into train, test and valid directories.\n\n* 13493 train images\n* 500 test images\n* 500 validate images\n\nAdditionallly a csv file is included for those that wish to use it to create there own train, test and validation datasets.", "### Clone\n\n\n\nor", "### Modify dataset\n\nTo add data, ensure to install LFS.\n\n\n\nThen proceed accordingly with 'git add' and 'git push'." ]

[ "region:us" ]

[]

[]

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

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

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

[]

[]

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

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

[ "task_categories:table-question-answering", "size_categories:10B<n<100B", "language:en", "language:es", "language:fr", "language:it", "region:us" ]

[]

[ "en", "es", "fr", "it" ]

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

[ "TAGS\n#task_categories-table-question-answering #size_categories-10B<n<100B #language-English #language-Spanish #language-French #language-Italian #region-us \n", "# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of wang7776/vicuna-7b-v1.3-sparsity-10\n\n\n\nDataset automatically created during the evaluation run of model wang7776/vicuna-7b-v1.3-sparsity-10 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T11:22:25.072953(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 wang7776/vicuna-7b-v1.3-sparsity-10\n\n\n\nDataset automatically created during the evaluation run of model wang7776/vicuna-7b-v1.3-sparsity-10 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T11:22:25.072953(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of wang7776/Mistral-7B-Instruct-v0.2-sparsity-20-v0.1\n\n\n\nDataset automatically created during the evaluation run of model wang7776/Mistral-7B-Instruct-v0.2-sparsity-20-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T11:52:33.965270(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 wang7776/Mistral-7B-Instruct-v0.2-sparsity-20-v0.1\n\n\n\nDataset automatically created during the evaluation run of model wang7776/Mistral-7B-Instruct-v0.2-sparsity-20-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T11:52:33.965270(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"mmCodeQL89\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"mmCodeQL89\"\n\nMore Information needed" ]

[ "audio", "region:us" ]

[]

[]

[ "### About dataset\nIt is a dataset of ukrainian audiobooks \nEach sample contain an approximately 8 seconds od ukrainian speech", "### Loading script", "### Dataset structure\nEvery example has the following: \naudio - the waveform \nrate - the sampling rate of the waveform \nfile_id - the id of the speaker \nduration - the duration of the video in seconds \nsentence - the transcript of the video" ]

[ "TAGS\n#audio #region-us \n", "### About dataset\nIt is a dataset of ukrainian audiobooks \nEach sample contain an approximately 8 seconds od ukrainian speech", "### Loading script", "### Dataset structure\nEvery example has the following: \naudio - the waveform \nrate - the sampling rate of the waveform \nfile_id - the id of the speaker \nduration - the duration of the video in seconds \nsentence - the transcript of the video" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of kodonho/Momo-70b-DPO-mixed\n\n\n\nDataset automatically created during the evaluation run of model kodonho/Momo-70b-DPO-mixed on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T12:09:45.590059(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of kodonho/Momo-70b-DPO-mixed\n\n\n\nDataset automatically created during the evaluation run of model kodonho/Momo-70b-DPO-mixed on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T12:09:45.590059(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of chargoddard/internlm2-7b-llama\n\n\n\nDataset automatically created during the evaluation run of model chargoddard/internlm2-7b-llama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T12:13:04.932540(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of chargoddard/internlm2-7b-llama\n\n\n\nDataset automatically created during the evaluation run of model chargoddard/internlm2-7b-llama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T12:13:04.932540(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of liminerity/Blurstral-7b-slerp\n\n\n\nDataset automatically created during the evaluation run of model liminerity/Blurstral-7b-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T12:26:10.788349(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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/Blurstral-7b-slerp\n\n\n\nDataset automatically created during the evaluation run of model liminerity/Blurstral-7b-slerp on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T12:26:10.788349(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

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

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of liminerity/Blured-Ties-7B\n\n\n\nDataset automatically created during the evaluation run of model liminerity/Blured-Ties-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T12:35:33.368960(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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/Blured-Ties-7B\n\n\n\nDataset automatically created during the evaluation run of model liminerity/Blured-Ties-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T12:35:33.368960(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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-test\"\n\nMore Information needed" ]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of leveldevai/TurdusDareBeagle-7B\n\n\n\nDataset automatically created during the evaluation run of model leveldevai/TurdusDareBeagle-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T12:52:49.102510(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 leveldevai/TurdusDareBeagle-7B\n\n\n\nDataset automatically created during the evaluation run of model leveldevai/TurdusDareBeagle-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T12:52:49.102510(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Cartinoe5930/iDUS-8layers\n\n\n\nDataset automatically created during the evaluation run of model Cartinoe5930/iDUS-8layers on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T12:59:32.021751(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Cartinoe5930/iDUS-8layers\n\n\n\nDataset automatically created during the evaluation run of model Cartinoe5930/iDUS-8layers on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T12:59:32.021751(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 liminerity/Blur-7b-v1.2\n\n\n\nDataset automatically created during the evaluation run of model liminerity/Blur-7b-v1.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:00:27.961191(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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/Blur-7b-v1.2\n\n\n\nDataset automatically created during the evaluation run of model liminerity/Blur-7b-v1.2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:00:27.961191(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Charlie911/MultiLora-sharegpt\n\n\n\nDataset automatically created during the evaluation run of model Charlie911/MultiLora-sharegpt on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:16:53.063805(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Charlie911/MultiLora-sharegpt\n\n\n\nDataset automatically created during the evaluation run of model Charlie911/MultiLora-sharegpt on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:16:53.063805(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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-classification", "task_categories:reinforcement-learning", "task_categories:text-generation", "size_categories:1K<n<10K", "license:mit", "ecolinguistics", "ecology", "sustainability", "environment", "synthetic", "region:us" ]

[]

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[ "TAGS\n#task_categories-question-answering #task_categories-text-classification #task_categories-reinforcement-learning #task_categories-text-generation #size_categories-1K<n<10K #license-mit #ecolinguistics #ecology #sustainability #environment #synthetic #region-us \n" ]

[ "region:us" ]

[]

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[ "# Dataset Card for Evaluation run of chargoddard/internlm2-20b-llama\n\n\n\nDataset automatically created during the evaluation run of model chargoddard/internlm2-20b-llama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T17:52:12.379059(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of chargoddard/internlm2-20b-llama\n\n\n\nDataset automatically created during the evaluation run of model chargoddard/internlm2-20b-llama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T17:52:12.379059(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 liminerity/Blur-7b-v1.21\n\n\n\nDataset automatically created during the evaluation run of model liminerity/Blur-7b-v1.21 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:28:00.366540(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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/Blur-7b-v1.21\n\n\n\nDataset automatically created during the evaluation run of model liminerity/Blur-7b-v1.21 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:28:00.366540(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 xriminact/TarsChattyBasev0.1\n\n\n\nDataset automatically created during the evaluation run of model xriminact/TarsChattyBasev0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:28:46.282791(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of xriminact/TarsChattyBasev0.1\n\n\n\nDataset automatically created during the evaluation run of model xriminact/TarsChattyBasev0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:28:46.282791(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ewqr2130/alignment-handbook-zephyr-7b_ppostep_100\n\n\n\nDataset automatically created during the evaluation run of model ewqr2130/alignment-handbook-zephyr-7b_ppostep_100 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:32:11.965537(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of ewqr2130/alignment-handbook-zephyr-7b_ppostep_100\n\n\n\nDataset automatically created during the evaluation run of model ewqr2130/alignment-handbook-zephyr-7b_ppostep_100 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:32:11.965537(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 xriminact/TarsChattyBasev0.0\n\n\n\nDataset automatically created during the evaluation run of model xriminact/TarsChattyBasev0.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:38:14.654966(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of xriminact/TarsChattyBasev0.0\n\n\n\nDataset automatically created during the evaluation run of model xriminact/TarsChattyBasev0.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:38:14.654966(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 OdiaGenAI/odia_llama2_7B_base\n\n\n\nDataset automatically created during the evaluation run of model OdiaGenAI/odia_llama2_7B_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 2024-01-18T13:44:42.651990(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 OdiaGenAI/odia_llama2_7B_base\n\n\n\nDataset automatically created during the evaluation run of model OdiaGenAI/odia_llama2_7B_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 2024-01-18T13:44:42.651990(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 duoqi/Nanbeige-16B-Base-Llama\n\n\n\nDataset automatically created during the evaluation run of model duoqi/Nanbeige-16B-Base-Llama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:45:09.426165(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 duoqi/Nanbeige-16B-Base-Llama\n\n\n\nDataset automatically created during the evaluation run of model duoqi/Nanbeige-16B-Base-Llama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T13:45:09.426165(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 fierysurf/Kan-LLaMA-7B-base\n\n\n\nDataset automatically created during the evaluation run of model fierysurf/Kan-LLaMA-7B-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 2024-01-18T13:48:16.932348(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 fierysurf/Kan-LLaMA-7B-base\n\n\n\nDataset automatically created during the evaluation run of model fierysurf/Kan-LLaMA-7B-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 2024-01-18T13:48:16.932348(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "language:en", "license:cc-by-4.0", "human-feedback", "region:us" ]

[]

[ "en" ]

[ "# Binarized version of HelpSteer", "### Dataset Description\n\nA binarized version of URL ready for DPO using URL or similar.\n\nFor each unique prompt, we take the best and worst scoring (average of helpfulness and correctness) responses. These are converted into MessagesList format in the 'chosen' and 'rejected' columns.\n\n- Created by: dctanner and the team at Sablo AI\n- License: CC BY 4.0" ]

[ "TAGS\n#language-English #license-cc-by-4.0 #human-feedback #region-us \n", "# Binarized version of HelpSteer", "### Dataset Description\n\nA binarized version of URL ready for DPO using URL or similar.\n\nFor each unique prompt, we take the best and worst scoring (average of helpfulness and correctness) responses. These are converted into MessagesList format in the 'chosen' and 'rejected' columns.\n\n- Created by: dctanner and the team at Sablo AI\n- License: CC BY 4.0" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of liminerity/Blur-7b-v1.22\n\n\n\nDataset automatically created during the evaluation run of model liminerity/Blur-7b-v1.22 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T14:27:00.815176(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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/Blur-7b-v1.22\n\n\n\nDataset automatically created during the evaluation run of model liminerity/Blur-7b-v1.22 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T14:27:00.815176(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 fierysurf/Kan-LLaMA-7B-SFT-v0.1-sharded\n\n\n\nDataset automatically created during the evaluation run of model fierysurf/Kan-LLaMA-7B-SFT-v0.1-sharded on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T14:17:05.941006(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 fierysurf/Kan-LLaMA-7B-SFT-v0.1-sharded\n\n\n\nDataset automatically created during the evaluation run of model fierysurf/Kan-LLaMA-7B-SFT-v0.1-sharded on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T14:17:05.941006(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 fierysurf/Ambari-7B-base-v0.1-sharded\n\n\n\nDataset automatically created during the evaluation run of model fierysurf/Ambari-7B-base-v0.1-sharded on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T14:24:01.960531(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 fierysurf/Ambari-7B-base-v0.1-sharded\n\n\n\nDataset automatically created during the evaluation run of model fierysurf/Ambari-7B-base-v0.1-sharded on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T14:24:01.960531(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"aihub_admin_generated_answers_3question\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"aihub_admin_generated_answers_3question\"\n\nMore Information needed" ]

[ "task_categories:text-classification", "task_categories:multiple-choice", "language:en", "arxiv:2401.09646", "region:us" ]

[ "2401.09646" ]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-classification #task_categories-multiple-choice #language-English #arxiv-2401.09646 #region-us \n" ]

[ "language:ja", "license:cc-by-sa-3.0", "region:us" ]

[]

[ "ja" ]

[ "## Preprocessing Code" ]

[ "TAGS\n#language-Japanese #license-cc-by-sa-3.0 #region-us \n", "## Preprocessing Code" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of fierysurf/Ambari-7B-Instruct-v0.1-sharded\n\n\n\nDataset automatically created during the evaluation run of model fierysurf/Ambari-7B-Instruct-v0.1-sharded on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T14:37:24.844010(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 fierysurf/Ambari-7B-Instruct-v0.1-sharded\n\n\n\nDataset automatically created during the evaluation run of model fierysurf/Ambari-7B-Instruct-v0.1-sharded on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T14:37:24.844010(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"compositionality-subsample\"\n\nMore Information needed" ]

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

[ "task_categories:image-segmentation", "size_categories:1K<n<10K", "language:en", "vegetation", "segmentation", "region:us" ]

[]

[ "en" ]

[ "# VegAnn Dataset", "### Vegetation Annotation of a large multi-crop RGB Dataset acquired under diverse conditions for image semantic segmentation", "## Keypoints ⏳\n\n- VegAnn contains 3775 images \n- Images are 512*512 pixels \n- Corresponding binary masks is 0 for soil + crop residues (background) 255 for Vegetation (foreground)\n- The dataset includes images of 26+ crop species, which are not evenly represented\n- VegAnn was compiled using a variety of outdoor images captured with different acquisition systems and configurations\n- For more information about VegAnn, details, labeling rules and potential uses see URL", "## Dataset Description \n\nVegAnn, short for Vegetation Annotation, is a meticulously curated collection of 3,775 multi-crop RGB images aimed at enhancing research in crop vegetation segmentation. These images span various phenological stages and were captured using diverse systems and platforms under a wide range of illumination conditions. By aggregating sub-datasets from different projects and institutions, VegAnn represents a broad spectrum of measurement conditions, crop species, and development stages.", "### Languages \n\nThe annotations and documentation are primarily in English.", "## Dataset Structure", "### Data Instances \n\nA VegAnn data instance consists of a 512x512 pixel RGB image patch derived from larger raw images. These patches are designed to provide sufficient detail for distinguishing between vegetation and background, crucial for applications in semantic segmentation and other forms of computer vision analysis in agricultural contexts.\n\n\n!image/png", "### Data Fields \n\n- 'Name': Unique identifier for each image patch.\n- 'System': The imaging system used to acquire the photo (e.g., Handheld Cameras, DHP, UAV).\n- 'Orientation': The camera's orientation during image capture (e.g., Nadir, 45 degrees).\n- 'latitude' and 'longitude': Geographic coordinates where the image was taken.\n- 'date': Date of image acquisition.\n- 'LocAcc': Location accuracy flag (1 for high accuracy, 0 for low or uncertain accuracy).\n- 'Species': The crop species featured in the image (e.g., Wheat, Maize, Soybean).\n- 'Owner': The institution or entity that provided the image (e.g., Arvalis, INRAe).\n- 'Dataset-Name': The sub-dataset or project from which the image originates (e.g., Phenomobile, Easypcc).\n- 'TVT-split1' to 'TVT-split5': Fields indicating the train/validation/test split configurations, facilitating various experimental setups.", "### Data Splits \n\nThe dataset is structured into multiple splits (as indicated by 'TVT-split' fields) to support different training, validation, and testing scenarios in machine learning workflows.", "## Dataset Creation", "### Curation Rationale \n\nThe VegAnn dataset was developed to address the gap in available datasets for training convolutional neural networks (CNNs) for the task of semantic segmentation in real-world agricultural environments. By incorporating images from a wide array of conditions and stages of crop development, VegAnn aims to enhance the performance of segmentation algorithms, promote benchmarking, and foster research on large-scale crop vegetation segmentation.", "### Source Data", "#### Initial Data Collection and Normalization\n\nImages within VegAnn were sourced from various sub-datasets contributed by different institutions, each under specific acquisition configurations. These were then standardized into 512x512 pixel patches to maintain consistency across the dataset.", "#### Who are the source data providers?\n\nThe data was provided by a collaboration of institutions including Arvalis, INRAe, The University of Tokyo, University of Queensland, NEON, and EOLAB, among others.\n\n\n!image/png", "### Annotations", "#### Annotation process\n\nAnnotations for the dataset were focused on distinguishing between vegetation and background within the images. The process ensured that the images offered sufficient spatial resolution to allow for accurate visual segmentation.", "#### Who are the annotators?\n\nThe annotations were performed by a team comprising researchers and domain experts from the contributing institutions.", "## Considerations for Using the Data", "### Social Impact of Dataset \n\nThe VegAnn dataset is expected to significantly impact agricultural research and commercial applications by enhancing the accuracy of crop monitoring, disease detection, and yield estimation through improved vegetation segmentation techniques.", "### Discussion of Biases \n\nGiven the diverse sources of the images, there may be inherent biases towards certain crop types, geographical locations, and imaging conditions. Users should consider this diversity in applications and analyses.", "### Licensing Information \n\nPlease refer to the specific licensing agreements of the contributing institutions or contact the dataset providers for more information on usage rights and restrictions.\n\n \n\nIf you use the VegAnn dataset in your research, please cite the following:", "## Additional Information\n\n- Dataset Curators: Simon Madec et al.\n- Version: 1.0\n- License: CC-BY\n- Contact: URL@URL" ]

[ "TAGS\n#task_categories-image-segmentation #size_categories-1K<n<10K #language-English #vegetation #segmentation #region-us \n", "# VegAnn Dataset", "### Vegetation Annotation of a large multi-crop RGB Dataset acquired under diverse conditions for image semantic segmentation", "## Keypoints ⏳\n\n- VegAnn contains 3775 images \n- Images are 512*512 pixels \n- Corresponding binary masks is 0 for soil + crop residues (background) 255 for Vegetation (foreground)\n- The dataset includes images of 26+ crop species, which are not evenly represented\n- VegAnn was compiled using a variety of outdoor images captured with different acquisition systems and configurations\n- For more information about VegAnn, details, labeling rules and potential uses see URL", "## Dataset Description \n\nVegAnn, short for Vegetation Annotation, is a meticulously curated collection of 3,775 multi-crop RGB images aimed at enhancing research in crop vegetation segmentation. These images span various phenological stages and were captured using diverse systems and platforms under a wide range of illumination conditions. By aggregating sub-datasets from different projects and institutions, VegAnn represents a broad spectrum of measurement conditions, crop species, and development stages.", "### Languages \n\nThe annotations and documentation are primarily in English.", "## Dataset Structure", "### Data Instances \n\nA VegAnn data instance consists of a 512x512 pixel RGB image patch derived from larger raw images. These patches are designed to provide sufficient detail for distinguishing between vegetation and background, crucial for applications in semantic segmentation and other forms of computer vision analysis in agricultural contexts.\n\n\n!image/png", "### Data Fields \n\n- 'Name': Unique identifier for each image patch.\n- 'System': The imaging system used to acquire the photo (e.g., Handheld Cameras, DHP, UAV).\n- 'Orientation': The camera's orientation during image capture (e.g., Nadir, 45 degrees).\n- 'latitude' and 'longitude': Geographic coordinates where the image was taken.\n- 'date': Date of image acquisition.\n- 'LocAcc': Location accuracy flag (1 for high accuracy, 0 for low or uncertain accuracy).\n- 'Species': The crop species featured in the image (e.g., Wheat, Maize, Soybean).\n- 'Owner': The institution or entity that provided the image (e.g., Arvalis, INRAe).\n- 'Dataset-Name': The sub-dataset or project from which the image originates (e.g., Phenomobile, Easypcc).\n- 'TVT-split1' to 'TVT-split5': Fields indicating the train/validation/test split configurations, facilitating various experimental setups.", "### Data Splits \n\nThe dataset is structured into multiple splits (as indicated by 'TVT-split' fields) to support different training, validation, and testing scenarios in machine learning workflows.", "## Dataset Creation", "### Curation Rationale \n\nThe VegAnn dataset was developed to address the gap in available datasets for training convolutional neural networks (CNNs) for the task of semantic segmentation in real-world agricultural environments. By incorporating images from a wide array of conditions and stages of crop development, VegAnn aims to enhance the performance of segmentation algorithms, promote benchmarking, and foster research on large-scale crop vegetation segmentation.", "### Source Data", "#### Initial Data Collection and Normalization\n\nImages within VegAnn were sourced from various sub-datasets contributed by different institutions, each under specific acquisition configurations. These were then standardized into 512x512 pixel patches to maintain consistency across the dataset.", "#### Who are the source data providers?\n\nThe data was provided by a collaboration of institutions including Arvalis, INRAe, The University of Tokyo, University of Queensland, NEON, and EOLAB, among others.\n\n\n!image/png", "### Annotations", "#### Annotation process\n\nAnnotations for the dataset were focused on distinguishing between vegetation and background within the images. The process ensured that the images offered sufficient spatial resolution to allow for accurate visual segmentation.", "#### Who are the annotators?\n\nThe annotations were performed by a team comprising researchers and domain experts from the contributing institutions.", "## Considerations for Using the Data", "### Social Impact of Dataset \n\nThe VegAnn dataset is expected to significantly impact agricultural research and commercial applications by enhancing the accuracy of crop monitoring, disease detection, and yield estimation through improved vegetation segmentation techniques.", "### Discussion of Biases \n\nGiven the diverse sources of the images, there may be inherent biases towards certain crop types, geographical locations, and imaging conditions. Users should consider this diversity in applications and analyses.", "### Licensing Information \n\nPlease refer to the specific licensing agreements of the contributing institutions or contact the dataset providers for more information on usage rights and restrictions.\n\n \n\nIf you use the VegAnn dataset in your research, please cite the following:", "## Additional Information\n\n- Dataset Curators: Simon Madec et al.\n- Version: 1.0\n- License: CC-BY\n- Contact: URL@URL" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Guanaco-1k -> Llama-2 Dataset\n\nSubset of 1000 samples of the 'timdettmers/openassistant-guanaco' dataset.\n\nIt has been transformed to match Llama 2's prompt format according to how to prompt llama 2.\n\nColab notebook." ]

[ "TAGS\n#region-us \n", "# Guanaco-1k -> Llama-2 Dataset\n\nSubset of 1000 samples of the 'timdettmers/openassistant-guanaco' dataset.\n\nIt has been transformed to match Llama 2's prompt format according to how to prompt llama 2.\n\nColab notebook." ]

[ "arxiv:2212.10190", "region:us" ]

[ "2212.10190" ]

[]

[]

[ "TAGS\n#arxiv-2212.10190 #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"wsd_myriade_v2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"wsd_myriade_v2\"\n\nMore Information needed" ]

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

[]

[]

[]

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

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

[]

[]

[ "# Phrase Detectives Version 3\n\n- Homepage: CoNLL-2012 Shared Task, Author's page\n- Repository: Mendeley\n- Conversion: URL", "## Details\n\nThe original consistuency parse annotations of 'coref-data/conll2012_raw' converted to conllu dependency parses using 'convert_to_heads.py' from URL\n\ns" ]

[ "TAGS\n#license-other #region-us \n", "# Phrase Detectives Version 3\n\n- Homepage: CoNLL-2012 Shared Task, Author's page\n- Repository: Mendeley\n- Conversion: URL", "## Details\n\nThe original consistuency parse annotations of 'coref-data/conll2012_raw' converted to conllu dependency parses using 'convert_to_heads.py' from URL\n\ns" ]

[ "audio", "region:us" ]

[]

[]

[ "### About dataset\nIt is a dataset of multispeaker speech with noise \neach sample is at most 30 seconds", "### Loading script", "### Dataset structure\nEvery example has the following: \nnum_speakers - the number of speakers \nutter - list of utterences data \naudio - the waveform of the audio \n\nEach entry in the utter is a dict and has the following structure: \nstart - the starting position in audio of the speaker audio \nend - the ending position in audio of the speaker audio \nfile_id - the identificator of the speaker \nsentence - the transcription \nrate - has to be the same across all examples in the dataset, if not something bad happened" ]

[ "TAGS\n#audio #region-us \n", "### About dataset\nIt is a dataset of multispeaker speech with noise \neach sample is at most 30 seconds", "### Loading script", "### Dataset structure\nEvery example has the following: \nnum_speakers - the number of speakers \nutter - list of utterences data \naudio - the waveform of the audio \n\nEach entry in the utter is a dict and has the following structure: \nstart - the starting position in audio of the speaker audio \nend - the ending position in audio of the speaker audio \nfile_id - the identificator of the speaker \nsentence - the transcription \nrate - has to be the same across all examples in the dataset, if not something bad happened" ]

[ "license:cc-by-4.0", "arxiv:2303.09092", "region:us" ]

[ "2303.09092" ]

[]

[ "# Wingrande Recast as Coreference Resolution", "### Dataset Summary\n\nWinoGrande train and development sets recast as coreference resolution as described in Investigating Failures to Generalize for Coreference Resolution Models. Conllu columns are parsed using Stanza.", "### Data Fields" ]

[ "TAGS\n#license-cc-by-4.0 #arxiv-2303.09092 #region-us \n", "# Wingrande Recast as Coreference Resolution", "### Dataset Summary\n\nWinoGrande train and development sets recast as coreference resolution as described in Investigating Failures to Generalize for Coreference Resolution Models. Conllu columns are parsed using Stanza.", "### Data Fields" ]

[ "size_categories:n<1K", "license:cc-by-sa-4.0", "tgirl", "trans-female", "region:us" ]

[]

[]

[]

[ "TAGS\n#size_categories-n<1K #license-cc-by-sa-4.0 #tgirl #trans-female #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of AA051611/A0118\n\n\n\nDataset automatically created during the evaluation run of model AA051611/A0118 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T23:48:21.810095(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of AA051611/A0118\n\n\n\nDataset automatically created during the evaluation run of model AA051611/A0118 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T23:48:21.810095(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 cloudyu/Pluto_24B_DPO_200\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/Pluto_24B_DPO_200 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T17:18:01.366806(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of cloudyu/Pluto_24B_DPO_200\n\n\n\nDataset automatically created during the evaluation run of model cloudyu/Pluto_24B_DPO_200 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T17:18:01.366806(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"IIT-AFF-Dataset-Modified\"\n\nMore Information needed" ]

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

[ "task_categories:object-detection", "size_categories:n<1K", "language:en", "license:mit", "crowd-counting", "cnn", "detection", "region:us" ]

[]

[ "en" ]

[ "# Dataset Card for Dataset Name\n\n\n\nThis dataset card has 6 .h5 and 6 .mat files that are used by the crowd counting demo", "## Dataset Details", "### Dataset Description\n\n\n\n- Curated by: Rootstrap\n- License: MIT", "### Dataset Sources \n\n\n\n- Repository: URL", "## Uses\n\n\n\nThe dataset is used for the demo", "### Direct Use\n\n\n\nThis dataset is intended to use only for the demo", "### Out-of-Scope Use\n\n\n\nThis dataset was not used for training the model and can not be used for training a new model as it is very limited.", "## Dataset Structure\n\n\n\nThe dataset consist of 6 .h5 and 6 .mat.", "## Dataset Creation", "### Curation Rationale\n\n\n\nThis dataset was created for the demo", "### Source Data\n\n\n\nThis .mat files where obtained from the ShangaiTech Dataset and the .h5 were generated from the .mat files using python.", "#### Data Collection and Processing\n\n\n\nThe ShangaiTech Dataset part B has 400 images. From this original dataset, 6 random files were gathered.", "#### Who are the source data producers?\n\n\n\n@inproceedings{zhang2016single, title={Single-image crowd counting via multi-column convolutional neural network}, author={Zhang, Yingying and Zhou, Desen and Chen, Siqin and Gao, Shenghua and Ma, Yi}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={589--597}, year={2016} }", "## Bias, Risks, and Limitations\n\n\n\nAs we stated before, this dataset is only ment to be used for the demo and cannot be reproduced in any way.", "## Dataset Card Authors\n\nRootstrap", "## Dataset Card Contact\n\ninfo@URL" ]

[ "TAGS\n#task_categories-object-detection #size_categories-n<1K #language-English #license-mit #crowd-counting #cnn #detection #region-us \n", "# Dataset Card for Dataset Name\n\n\n\nThis dataset card has 6 .h5 and 6 .mat files that are used by the crowd counting demo", "## Dataset Details", "### Dataset Description\n\n\n\n- Curated by: Rootstrap\n- License: MIT", "### Dataset Sources \n\n\n\n- Repository: URL", "## Uses\n\n\n\nThe dataset is used for the demo", "### Direct Use\n\n\n\nThis dataset is intended to use only for the demo", "### Out-of-Scope Use\n\n\n\nThis dataset was not used for training the model and can not be used for training a new model as it is very limited.", "## Dataset Structure\n\n\n\nThe dataset consist of 6 .h5 and 6 .mat.", "## Dataset Creation", "### Curation Rationale\n\n\n\nThis dataset was created for the demo", "### Source Data\n\n\n\nThis .mat files where obtained from the ShangaiTech Dataset and the .h5 were generated from the .mat files using python.", "#### Data Collection and Processing\n\n\n\nThe ShangaiTech Dataset part B has 400 images. From this original dataset, 6 random files were gathered.", "#### Who are the source data producers?\n\n\n\n@inproceedings{zhang2016single, title={Single-image crowd counting via multi-column convolutional neural network}, author={Zhang, Yingying and Zhou, Desen and Chen, Siqin and Gao, Shenghua and Ma, Yi}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={589--597}, year={2016} }", "## Bias, Risks, and Limitations\n\n\n\nAs we stated before, this dataset is only ment to be used for the demo and cannot be reproduced in any way.", "## Dataset Card Authors\n\nRootstrap", "## Dataset Card Contact\n\ninfo@URL" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of leveldevai/TurdusBeagle-7B\n\n\n\nDataset automatically created during the evaluation run of model leveldevai/TurdusBeagle-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T18:27:55.293799(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 leveldevai/TurdusBeagle-7B\n\n\n\nDataset automatically created during the evaluation run of model leveldevai/TurdusBeagle-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T18:27:55.293799(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "license:cc-by-nc-4.0", "arxiv:2308.03977", "region:us" ]

[ "2308.03977" ]

[]

[ "## PUG: ImageNet\nThe PUG: ImageNet dataset contains 88,328 pre-rendered images based on Unreal Engine using 724 assets representing 151 ImageNet classes with 64 environments, 7 sizes, 9 textures, 18 different camera orientations, 18 different character orientations and 7 light intensities. In contrast to PUG: Animals, PUG: ImageNet was created by varying only a single factor at a time (which explains the lower number of images than PUG: Animals despite using more factors). The main purpose of this dataset is to provide a novel, useful benchmark, paralleling ImageNet, but for fine-grained evaluation of the robustness of image classifiers, along several factors of variation.", "## LICENSE\nThe datasets are distributed under the CC-BY-NC, with the addenda that they should not be used to train Generative AI models.", "## Citing PUG\nIf you use one of the PUG datasets, please cite:", "## To learn more about the PUG datasets:\nPlease visit the website and the github" ]

[ "TAGS\n#license-cc-by-nc-4.0 #arxiv-2308.03977 #region-us \n", "## PUG: ImageNet\nThe PUG: ImageNet dataset contains 88,328 pre-rendered images based on Unreal Engine using 724 assets representing 151 ImageNet classes with 64 environments, 7 sizes, 9 textures, 18 different camera orientations, 18 different character orientations and 7 light intensities. In contrast to PUG: Animals, PUG: ImageNet was created by varying only a single factor at a time (which explains the lower number of images than PUG: Animals despite using more factors). The main purpose of this dataset is to provide a novel, useful benchmark, paralleling ImageNet, but for fine-grained evaluation of the robustness of image classifiers, along several factors of variation.", "## LICENSE\nThe datasets are distributed under the CC-BY-NC, with the addenda that they should not be used to train Generative AI models.", "## Citing PUG\nIf you use one of the PUG datasets, please cite:", "## To learn more about the PUG datasets:\nPlease visit the website and the github" ]

[ "license:mit", "medical", "medical imaging", "image segmentation", "deep learning", "machine learning", "computer vision", "healthcare", "liver", "liver segmentation", "region:us" ]

[]

[]

[ "### Liver Segmentation Datasets\nThis is a batch of 100 CT scans, where you can find the volumes (the scans) and their segmentation to train a deep learning model for image segmentation." ]

[ "TAGS\n#license-mit #medical #medical imaging #image segmentation #deep learning #machine learning #computer vision #healthcare #liver #liver segmentation #region-us \n", "### Liver Segmentation Datasets\nThis is a batch of 100 CT scans, where you can find the volumes (the scans) and their segmentation to train a deep learning model for image segmentation." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of nlpguy/Hermes-low-tune-3.1\n\n\n\nDataset automatically created during the evaluation run of model nlpguy/Hermes-low-tune-3.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T19:43:57.257898(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of nlpguy/Hermes-low-tune-3.1\n\n\n\nDataset automatically created during the evaluation run of model nlpguy/Hermes-low-tune-3.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T19:43:57.257898(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 intervitens/internlm2-base-20b-llama\n\n\n\nDataset automatically created during the evaluation run of model intervitens/internlm2-base-20b-llama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T19:55:30.426442(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 intervitens/internlm2-base-20b-llama\n\n\n\nDataset automatically created during the evaluation run of model intervitens/internlm2-base-20b-llama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T19:55:30.426442(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 Kquant03/Prokaryote-8x7B-bf16\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Prokaryote-8x7B-bf16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T20:11:57.513943(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Kquant03/Prokaryote-8x7B-bf16\n\n\n\nDataset automatically created during the evaluation run of model Kquant03/Prokaryote-8x7B-bf16 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T20:11:57.513943(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:text-classification", "task_categories:sentence-similarity", "language:en", "medical", "region:us" ]

[]

[ "en" ]

[ "# SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials", "## Dataset Details", "### Dataset Description\n\nCompiled dataset for SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials.", "### Dataset Sources\n\n\n\n- Repository: [URL\n- Paper: [URL\n- Demo:" ]

[ "TAGS\n#task_categories-text-classification #task_categories-sentence-similarity #language-English #medical #region-us \n", "# SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials", "## Dataset Details", "### Dataset Description\n\nCompiled dataset for SemEval 2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials.", "### Dataset Sources\n\n\n\n- Repository: [URL\n- Paper: [URL\n- Demo:" ]

[ "region:us" ]

[]

[]

[ "### Virtual Food Policy Coach Training Data\nThe Prompt and Completion (P&C) pairs used to train the *Virtual Food Policy Coach*, an AI chatbot developed by the International Food Policy Research Institute (IFPRI) using Coachvox AI. \nTotal of 1,350 P&Cs were generated from 21 documents using GPT-3.5.", "### Resources\n* Virtual Food Policy Coach <URL\n* IFPRI <URL>\n* Coachvox AI <URL>\n* Blog post at Agrilinks <URL\n* Working Paper <URL\n\n---\nlicense: cc-by-4.0\n---" ]

[ "TAGS\n#region-us \n", "### Virtual Food Policy Coach Training Data\nThe Prompt and Completion (P&C) pairs used to train the *Virtual Food Policy Coach*, an AI chatbot developed by the International Food Policy Research Institute (IFPRI) using Coachvox AI. \nTotal of 1,350 P&Cs were generated from 21 documents using GPT-3.5.", "### Resources\n* Virtual Food Policy Coach <URL\n* IFPRI <URL>\n* Coachvox AI <URL>\n* Blog post at Agrilinks <URL\n* Working Paper <URL\n\n---\nlicense: cc-by-4.0\n---" ]

[ "license:cc-by-nc-4.0", "arxiv:2308.03977", "region:us" ]

[ "2308.03977" ]

[]

[ "## PUG: SPAR\nPUG: SPAR (Scene, Position, Attribute, Relation) contains 43,560 test samples, with image-caption pairs that evaluate VLMs scene and object recognition, as well as inter-object and object-attribute relationships respectively. We utilize scenes containing up to two objects in 4 unique spatial relationships and 4 different texture variations.", "## LICENSE\nThe datasets are distributed under the CC-BY-NC, with the addenda that they should not be used to train Generative AI models.", "## Citing PUG\nIf you use one of the PUG datasets, please cite:", "## To learn more about the PUG datasets:\nPlease visit the website and the github" ]

[ "TAGS\n#license-cc-by-nc-4.0 #arxiv-2308.03977 #region-us \n", "## PUG: SPAR\nPUG: SPAR (Scene, Position, Attribute, Relation) contains 43,560 test samples, with image-caption pairs that evaluate VLMs scene and object recognition, as well as inter-object and object-attribute relationships respectively. We utilize scenes containing up to two objects in 4 unique spatial relationships and 4 different texture variations.", "## LICENSE\nThe datasets are distributed under the CC-BY-NC, with the addenda that they should not be used to train Generative AI models.", "## Citing PUG\nIf you use one of the PUG datasets, please cite:", "## To learn more about the PUG datasets:\nPlease visit the website and the github" ]

[ "size_categories:10M<n<100M", "language:en", "license:apache-2.0", "pubmed", "medical", "medicine", "NIH", "science", "region:us" ]

[]

[ "en" ]

[ "# DATACLYSM PATCH 0.0.4: PUBMED", "## USE THE NOTEBOOK TO GET STARTED!\nURL", "# somewheresystems/dataclysm-pubmed\n\nThis dataset comprises of 35.7 million PubMed metadata entries including title and some (~69% with) abstracts, with two new columns added: title-embeddings and abstract-embeddings. These additional columns were generated using the bge-small-en-v1.5 embeddings model. The dataset was sourced from the PubMed Baseline as of December 12, 2023. URL", "# Embeddings Model\n\nWe used URL to embed the 'title' and 'abstract' fields.", "## Contact\n\nPlease contact hi@URL for inquiries." ]

[ "TAGS\n#size_categories-10M<n<100M #language-English #license-apache-2.0 #pubmed #medical #medicine #NIH #science #region-us \n", "# DATACLYSM PATCH 0.0.4: PUBMED", "## USE THE NOTEBOOK TO GET STARTED!\nURL", "# somewheresystems/dataclysm-pubmed\n\nThis dataset comprises of 35.7 million PubMed metadata entries including title and some (~69% with) abstracts, with two new columns added: title-embeddings and abstract-embeddings. These additional columns were generated using the bge-small-en-v1.5 embeddings model. The dataset was sourced from the PubMed Baseline as of December 12, 2023. URL", "# Embeddings Model\n\nWe used URL to embed the 'title' and 'abstract' fields.", "## Contact\n\nPlease contact hi@URL for inquiries." ]

[ "task_categories:text-classification", "task_ids:topic-classification", "task_ids:multi-class-classification", "multilinguality:monolingual", "size_categories:10K<n<100K", "language:pl", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "pl" ]

[ "# 8TAGS", "### Dataset Summary\n\nA Polish topic classification dataset consisting of headlines from social media posts. It contains about 50,000 sentences annotated with 8 topic labels: film, history, food, medicine, motorization, work, sport and technology. This dataset was created automatically by extracting sentences from headlines and short descriptions of articles posted on Polish social networking site URL. The service allows users to annotate articles with one or more tags (categories). Dataset represents a selection of article sentences from 8 popular categories. The resulting corpus contains cleaned and tokenized, unambiguous sentences (tagged with only one of the selected categories), and longer than 30 characters.", "### Data Instances\n\nExample instance:", "### Data Fields\n\n- sentence: sentence text\n- label: label identifier corresponding to one of 8 topics" ]

[ "TAGS\n#task_categories-text-classification #task_ids-topic-classification #task_ids-multi-class-classification #multilinguality-monolingual #size_categories-10K<n<100K #language-Polish #license-cc-by-nc-sa-4.0 #region-us \n", "# 8TAGS", "### Dataset Summary\n\nA Polish topic classification dataset consisting of headlines from social media posts. It contains about 50,000 sentences annotated with 8 topic labels: film, history, food, medicine, motorization, work, sport and technology. This dataset was created automatically by extracting sentences from headlines and short descriptions of articles posted on Polish social networking site URL. The service allows users to annotate articles with one or more tags (categories). Dataset represents a selection of article sentences from 8 popular categories. The resulting corpus contains cleaned and tokenized, unambiguous sentences (tagged with only one of the selected categories), and longer than 30 characters.", "### Data Instances\n\nExample instance:", "### Data Fields\n\n- sentence: sentence text\n- label: label identifier corresponding to one of 8 topics" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of freecs/ThetaWave-7B-v0\n\n\n\nDataset automatically created during the evaluation run of model freecs/ThetaWave-7B-v0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T21:58:24.571103(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 freecs/ThetaWave-7B-v0\n\n\n\nDataset automatically created during the evaluation run of model freecs/ThetaWave-7B-v0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T21:58:24.571103(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "task_categories:text-classification", "task_ids:semantic-similarity-classification", "multilinguality:monolingual", "size_categories:1K<n<10K", "language:pl", "license:cc-by-nc-sa-4.0", "region:us" ]

[]

[ "pl" ]

[ "# PPC - Polish Paraphrase Corpus", "### Dataset Summary\n\nPolish Paraphrase Corpus contains 7000 manually labeled sentence pairs. The dataset was divided into training, validation and test splits. The training part includes 5000 examples, while the other parts contain 1000 examples each. The main purpose of creating such a dataset was to verify how machine learning models perform in the challenging problem of paraphrase identification, where most records contain semantically overlapping parts. Technically, this is a three-class classification task, where each record can be assigned to one of the following categories:\n- Exact paraphrases - Sentence pairs that convey exactly the same information. We are interested only in the semantic meaning of the sentence, therefore this category also includes sentences that are semantically identical but, for example, have different emotional emphasis.\n- Close paraphrases - Sentence pairs with similar semantic meaning. In this category we include all pairs which contain the same information, but in addition to it there may be other semantically non-overlapping parts. This category also contains context-dependent paraphrases - sentence pairs that may have the same meaning in some contexts but are different in others.\n- Non-paraphrases - All other cases, including contradictory sentences and semantically unrelated sentences.\n\nThe corpus contains 2911, 1297, and 2792 examples for the above three categories, respectively. The process of annotating the dataset was preceded by an automated generation of candidate pairs, which were then manually labeled. We experimented with two popular techniques of generating possible paraphrases: backtranslation with a set of neural machine translation models and paraphrase mining using a pre-trained multilingual sentence encoder. The extracted sentence pairs are drawn from different data sources: Taboeba, Polish news articles, Wikipedia and Polish version of SICK dataset. Since most of the sentence pairs obtained in this way fell into the first two categories, in order to balance the dataset, some of the examples were manually modified to convey different information. In this way, even negative examples often have high semantic overlap, making this problem difficult for machine learning models.", "### Data Instances\n\nExample instance:", "### Data Fields\n\n- sentence_A: first sentence text\n- sentence_B: second sentence text\n- label: label identifier corresponding to one of three categories" ]

[ "TAGS\n#task_categories-text-classification #task_ids-semantic-similarity-classification #multilinguality-monolingual #size_categories-1K<n<10K #language-Polish #license-cc-by-nc-sa-4.0 #region-us \n", "# PPC - Polish Paraphrase Corpus", "### Dataset Summary\n\nPolish Paraphrase Corpus contains 7000 manually labeled sentence pairs. The dataset was divided into training, validation and test splits. The training part includes 5000 examples, while the other parts contain 1000 examples each. The main purpose of creating such a dataset was to verify how machine learning models perform in the challenging problem of paraphrase identification, where most records contain semantically overlapping parts. Technically, this is a three-class classification task, where each record can be assigned to one of the following categories:\n- Exact paraphrases - Sentence pairs that convey exactly the same information. We are interested only in the semantic meaning of the sentence, therefore this category also includes sentences that are semantically identical but, for example, have different emotional emphasis.\n- Close paraphrases - Sentence pairs with similar semantic meaning. In this category we include all pairs which contain the same information, but in addition to it there may be other semantically non-overlapping parts. This category also contains context-dependent paraphrases - sentence pairs that may have the same meaning in some contexts but are different in others.\n- Non-paraphrases - All other cases, including contradictory sentences and semantically unrelated sentences.\n\nThe corpus contains 2911, 1297, and 2792 examples for the above three categories, respectively. The process of annotating the dataset was preceded by an automated generation of candidate pairs, which were then manually labeled. We experimented with two popular techniques of generating possible paraphrases: backtranslation with a set of neural machine translation models and paraphrase mining using a pre-trained multilingual sentence encoder. The extracted sentence pairs are drawn from different data sources: Taboeba, Polish news articles, Wikipedia and Polish version of SICK dataset. Since most of the sentence pairs obtained in this way fell into the first two categories, in order to balance the dataset, some of the examples were manually modified to convey different information. In this way, even negative examples often have high semantic overlap, making this problem difficult for machine learning models.", "### Data Instances\n\nExample instance:", "### Data Fields\n\n- sentence_A: first sentence text\n- sentence_B: second sentence text\n- label: label identifier corresponding to one of three categories" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of TeeZee/Kyllene-v1.0\n\n\n\nDataset automatically created during the evaluation run of model TeeZee/Kyllene-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T22:11:48.814453(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 TeeZee/Kyllene-v1.0\n\n\n\nDataset automatically created during the evaluation run of model TeeZee/Kyllene-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T22:11:48.814453(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ValiantLabs/Fireplace-13b\n\n\n\nDataset automatically created during the evaluation run of model ValiantLabs/Fireplace-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T22:29:29.742832(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ValiantLabs/Fireplace-13b\n\n\n\nDataset automatically created during the evaluation run of model ValiantLabs/Fireplace-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T22:29:29.742832(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"cybersec_embedding_llama_chat\"\n\nMore Information needed" ]

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

[ "task_categories:visual-question-answering", "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "license:cc-by-nc-sa-4.0", "arxiv:2312.07886", "arxiv:2305.14836", "region:us" ]

[ "2312.07886", "2305.14836" ]

[ "en" ]

[ "# train samples: 2,229, # validation samples: 2,229\nscene: night, # train samples: 659, # validation samples: 659\n\n\n\nDetailed Description\n--------------------\n\n\nThis dataset is built on the nuScenes mini-split, where we obtain the QA pairs from the original nuScenes-QA dataset. The data in the nuScenes-QA dataset is collected from driving scenes in cities of Boston and Singapore with diverse locations, time, and weather conditions.\n\n\n\nEach data sample contains 6-view RGB camera captures, a 5D LiDAR point cloud, and a corresponding text QA pair. Each LiDAR point cloud includes 5 dimensions of data about distance, intensity, X, Y, and Z axes. In this dataset, the questions are generally difficult, and may require multiple hops of reasoning over the RGB and LiDAR data. For example, to answer the sample question in the above figure, the ML model needs to first identify in which direction the “construction vehicle” appears, and then counts the number of “parked trucks” in that direction. In our evaluations, we further cast the question-answering (QA) as an open-ended text generation task. This is more challenging than the evaluation setup in the original nuScenes-QA paper, where an answer set is predefined and the QA task is a classification task over this predefined answer set.\n\n\n\nIn most RGB images in the nuScenes dataset, as shown in the above figure - Left, the lighting conditions in night scenes are still abundant (e.g., with street lights), and we hence further reduce the brightness of RGB captures in night scenes by 80% and apply Gaussian blur with a radius of 7, as shown in the above figure - Right. By applying such preprocessing to the RGB views in night scenes, we obtain the training and validation splits of night scenes with 659 samples for each split. On the other hand, the RGB views in daytime scenes remain as the origin. The day split contains 2,229 for training and 2,229 for validation respectively.\n\n\nHow to Use\n----------\n\n\nIf you find our dataset useful, please consider citing\n\n\nLicense\n=======\n\n\n[](URL)\n\n\nBeing aligned with original nuScenes' license, this work is licensed under a\n[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](URL).\n\n\n[](URL)" ]

[ "TAGS\n#task_categories-visual-question-answering #task_categories-text-generation #size_categories-1K<n<10K #language-English #license-cc-by-nc-sa-4.0 #arxiv-2312.07886 #arxiv-2305.14836 #region-us \n", "# train samples: 2,229, # validation samples: 2,229\nscene: night, # train samples: 659, # validation samples: 659\n\n\n\nDetailed Description\n--------------------\n\n\nThis dataset is built on the nuScenes mini-split, where we obtain the QA pairs from the original nuScenes-QA dataset. The data in the nuScenes-QA dataset is collected from driving scenes in cities of Boston and Singapore with diverse locations, time, and weather conditions.\n\n\n\nEach data sample contains 6-view RGB camera captures, a 5D LiDAR point cloud, and a corresponding text QA pair. Each LiDAR point cloud includes 5 dimensions of data about distance, intensity, X, Y, and Z axes. In this dataset, the questions are generally difficult, and may require multiple hops of reasoning over the RGB and LiDAR data. For example, to answer the sample question in the above figure, the ML model needs to first identify in which direction the “construction vehicle” appears, and then counts the number of “parked trucks” in that direction. In our evaluations, we further cast the question-answering (QA) as an open-ended text generation task. This is more challenging than the evaluation setup in the original nuScenes-QA paper, where an answer set is predefined and the QA task is a classification task over this predefined answer set.\n\n\n\nIn most RGB images in the nuScenes dataset, as shown in the above figure - Left, the lighting conditions in night scenes are still abundant (e.g., with street lights), and we hence further reduce the brightness of RGB captures in night scenes by 80% and apply Gaussian blur with a radius of 7, as shown in the above figure - Right. By applying such preprocessing to the RGB views in night scenes, we obtain the training and validation splits of night scenes with 659 samples for each split. On the other hand, the RGB views in daytime scenes remain as the origin. The day split contains 2,229 for training and 2,229 for validation respectively.\n\n\nHow to Use\n----------\n\n\nIf you find our dataset useful, please consider citing\n\n\nLicense\n=======\n\n\n[](URL)\n\n\nBeing aligned with original nuScenes' license, this work is licensed under a\n[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](URL).\n\n\n[](URL)" ]

[ "region:us" ]

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[ "# Dataset Card for \"cai-conversation-dev1705620799\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"cai-conversation-dev1705620799\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"cai-conversation-dev1705620998\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"cai-conversation-dev1705620998\"\n\nMore Information needed" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of AA051611/A0119\n\n\n\nDataset automatically created during the evaluation run of model AA051611/A0119 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T23:36:49.726923(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of AA051611/A0119\n\n\n\nDataset automatically created during the evaluation run of model AA051611/A0119 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-18T23:36:49.726923(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]