Datasets:

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ftopal

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huggingface-datasets-processed

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

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[ "en", "es" ]

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

[ 15 ]

[ "passage: TAGS\n#language-English #language-Spanish #region-us \n" ]

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

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[ "# Model\nURL", "# Dataset\nURL", "# Developed by\nFreeAI Ltd. Tsuginosuke AI Super Computer(A100 80Gx8)\nURL" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# Model\nURL", "# Dataset\nURL", "# Developed by\nFreeAI Ltd. Tsuginosuke AI Super Computer(A100 80Gx8)\nURL" ]

[ 14, 3, 4, 22 ]

[ "passage: TAGS\n#license-apache-2.0 #region-us \n# Model\nURL# Dataset\nURL# Developed by\nFreeAI Ltd. Tsuginosuke AI Super Computer(A100 80Gx8)\nURL" ]

[ "task_categories:conversational", "size_categories:10K<n<100K", "language:ja", "license:apache-2.0", "region:us" ]

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[ "ja" ]

[ "# Based on\nURL", "# Model\nURL", "# Dataset\nURL", "# Developed by\nFreeAI Ltd. Tsuginosuke AI Super Computer(A100 80Gx8)\nURL" ]

[ "TAGS\n#task_categories-conversational #size_categories-10K<n<100K #language-Japanese #license-apache-2.0 #region-us \n", "# Based on\nURL", "# Model\nURL", "# Dataset\nURL", "# Developed by\nFreeAI Ltd. Tsuginosuke AI Super Computer(A100 80Gx8)\nURL" ]

[ 42, 5, 3, 4, 22 ]

[ "passage: TAGS\n#task_categories-conversational #size_categories-10K<n<100K #language-Japanese #license-apache-2.0 #region-us \n# Based on\nURL# Model\nURL# Dataset\nURL# Developed by\nFreeAI Ltd. Tsuginosuke AI Super Computer(A100 80Gx8)\nURL" ]

[ "region:us" ]

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[ "# Dataset Card for \"cpsc2018\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"cpsc2018\"\n\nMore Information needed" ]

[ 6, 13 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"cpsc2018\"\n\nMore Information needed" ]

[ "task_categories:text-generation", "task_categories:conversational", "task_categories:question-answering", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

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[ "TAGS\n#task_categories-text-generation #task_categories-conversational #task_categories-question-answering #language-English #license-apache-2.0 #region-us \n" ]

[ 51 ]

[ "passage: TAGS\n#task_categories-text-generation #task_categories-conversational #task_categories-question-answering #language-English #license-apache-2.0 #region-us \n" ]

[ "size_categories:n<1K", "language:es", "license:apache-2.0", "poesia", "lorca", "region:us" ]

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[ "es" ]

[ "# Federico García Lorca. Canciones, Poemas y Romances" ]

[ "TAGS\n#size_categories-n<1K #language-Spanish #license-apache-2.0 #poesia #lorca #region-us \n", "# Federico García Lorca. Canciones, Poemas y Romances" ]

[ 35, 15 ]

[ "passage: TAGS\n#size_categories-n<1K #language-Spanish #license-apache-2.0 #poesia #lorca #region-us \n# Federico García Lorca. Canciones, Poemas y Romances" ]

[ "region:us" ]

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

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

[ 6, 20 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"pretrain_repeat_paraphrase\"\n\nMore Information needed" ]

[ "region:us" ]

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

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

[ 6, 19 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"bioacoustic_mel_segments\"\n\nMore Information needed" ]

[ "region:us" ]

[]

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[ "# Dataset Card for \"CVEs_100\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"CVEs_100\"\n\nMore Information needed" ]

[ 6, 14 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"CVEs_100\"\n\nMore Information needed" ]

[ "license:mit", "arxiv:2312.09508", "region:us" ]

[ "2312.09508" ]

[]

[ "# IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languages\n\nPaper link: URL\n\nDataset link: URL\n\nModel link: URL", "## Contributors & Acknowledgements\n\nKey Contributors and Team Members: Saiful Haq, Ashutosh Sharma, Pushpak Bhattacharyya", "## Kindly cite our paper, If you are are using our datasets or models:\n\n@article{haq2023indicirsuite,\n title={IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languages},\n author={Haq, Saiful and Sharma, Ashutosh and Bhattacharyya, Pushpak},\n journal={arXiv preprint arXiv:2312.09508},\n year={2023}\n}", "## About\n\nThis repository contains URL and URL files in 11 Indian Languages,\nto train multilingual IR models.", "## Language Code to Language Mapping\n\nasm_Beng: Assamese Language\n\nben_Beng: Bengali Language\n\nguj_Gujr: Gujarati Language\n\nhin_Deva: Hindi Language\n\nkan_Knda: Kannada Language\n\nmal_Mlym: Malyalam Language\n\nmar_Deva: Marathi Language\n\nory_Orya: Oriya Language\n\npan_Guru: Punjabi Language\n\ntam_Taml: Tamil Language\n\ntel_Telu: Telugu Language" ]

[ "TAGS\n#license-mit #arxiv-2312.09508 #region-us \n", "# IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languages\n\nPaper link: URL\n\nDataset link: URL\n\nModel link: URL", "## Contributors & Acknowledgements\n\nKey Contributors and Team Members: Saiful Haq, Ashutosh Sharma, Pushpak Bhattacharyya", "## Kindly cite our paper, If you are are using our datasets or models:\n\n@article{haq2023indicirsuite,\n title={IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languages},\n author={Haq, Saiful and Sharma, Ashutosh and Bhattacharyya, Pushpak},\n journal={arXiv preprint arXiv:2312.09508},\n year={2023}\n}", "## About\n\nThis repository contains URL and URL files in 11 Indian Languages,\nto train multilingual IR models.", "## Language Code to Language Mapping\n\nasm_Beng: Assamese Language\n\nben_Beng: Bengali Language\n\nguj_Gujr: Gujarati Language\n\nhin_Deva: Hindi Language\n\nkan_Knda: Kannada Language\n\nmal_Mlym: Malyalam Language\n\nmar_Deva: Marathi Language\n\nory_Orya: Oriya Language\n\npan_Guru: Punjabi Language\n\ntam_Taml: Tamil Language\n\ntel_Telu: Telugu Language" ]

[ 21, 35, 33, 106, 26, 94 ]

[ "passage: TAGS\n#license-mit #arxiv-2312.09508 #region-us \n# IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languages\n\nPaper link: URL\n\nDataset link: URL\n\nModel link: URL## Contributors & Acknowledgements\n\nKey Contributors and Team Members: Saiful Haq, Ashutosh Sharma, Pushpak Bhattacharyya## Kindly cite our paper, If you are are using our datasets or models:\n\n@article{haq2023indicirsuite,\n title={IndicIRSuite: Multilingual Dataset and Neural Information Models for Indian Languages},\n author={Haq, Saiful and Sharma, Ashutosh and Bhattacharyya, Pushpak},\n journal={arXiv preprint arXiv:2312.09508},\n year={2023}\n}## About\n\nThis repository contains URL and URL files in 11 Indian Languages,\nto train multilingual IR models.## Language Code to Language Mapping\n\nasm_Beng: Assamese Language\n\nben_Beng: Bengali Language\n\nguj_Gujr: Gujarati Language\n\nhin_Deva: Hindi Language\n\nkan_Knda: Kannada Language\n\nmal_Mlym: Malyalam Language\n\nmar_Deva: Marathi Language\n\nory_Orya: Oriya Language\n\npan_Guru: Punjabi Language\n\ntam_Taml: Tamil Language\n\ntel_Telu: Telugu Language" ]

[ "region:us" ]

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

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

[ 6, 18 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"Therapydataset_formatted\"\n\nMore Information needed" ]

[ "size_categories:1K<n<10K", "license:apache-2.0", "arxiv:2401.01651", "region:us" ]

[ "2401.01651" ]

[]

[ "# AIGCBench v1.0\n\nAIGCBench is a novel and comprehensive benchmark designed for evaluating the capabilities of state-of-the-art video generation algorithms. Official dataset for the paper:AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI, *BenchCouncil Transactions on Benchmarks, Standards and Evaluations (TBench)*.\n<a href='URL src='URL", "## Description\n\nThis dataset is intended for the evaluation of video generation tasks. Our dataset includes image-text pairs and video-text pairs. The dataset comprises three parts:\n\n1. 'ours' - A custom generation of image-text samples.\n2. 'webvid val' - A subset of 1000 video samples from the WebVid val dataset.\n3. 'laion-aesthetics' - A subset of LAION dataset that includes 925 curated image-text samples.", "## Data Organization\n\nThe dataset is organized into the following folders and files:\n\n- 't2i_aspect_ratio_625.zip' - Contains images paired with text, adjusted to an aspect ratio of 0.625.\n- 'webvid_eval_1000.txt' - Contains video names for 1000 selected video samples. Considering that the first frame of the video may not contain the main information or might be a bad case, we use the tenth frame of the video as the initial frame.\n- 'Laion-aesthetics_select_samples.txt' - Contains metadata and annotations for 925 image-text samples.", "## Acknowledgments\nWe would like to thank all contributors and organizations behind the data sources, especially the maintainers of WebVid and LAION datasets.", "## Contact Information\nfanfanda@URL and jianfengzhan.benchcouncil@URL\n\nIf you find our work useful in your research, please consider citing our paper:" ]

[ "TAGS\n#size_categories-1K<n<10K #license-apache-2.0 #arxiv-2401.01651 #region-us \n", "# AIGCBench v1.0\n\nAIGCBench is a novel and comprehensive benchmark designed for evaluating the capabilities of state-of-the-art video generation algorithms. Official dataset for the paper:AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI, *BenchCouncil Transactions on Benchmarks, Standards and Evaluations (TBench)*.\n<a href='URL src='URL", "## Description\n\nThis dataset is intended for the evaluation of video generation tasks. Our dataset includes image-text pairs and video-text pairs. The dataset comprises three parts:\n\n1. 'ours' - A custom generation of image-text samples.\n2. 'webvid val' - A subset of 1000 video samples from the WebVid val dataset.\n3. 'laion-aesthetics' - A subset of LAION dataset that includes 925 curated image-text samples.", "## Data Organization\n\nThe dataset is organized into the following folders and files:\n\n- 't2i_aspect_ratio_625.zip' - Contains images paired with text, adjusted to an aspect ratio of 0.625.\n- 'webvid_eval_1000.txt' - Contains video names for 1000 selected video samples. Considering that the first frame of the video may not contain the main information or might be a bad case, we use the tenth frame of the video as the initial frame.\n- 'Laion-aesthetics_select_samples.txt' - Contains metadata and annotations for 925 image-text samples.", "## Acknowledgments\nWe would like to thank all contributors and organizations behind the data sources, especially the maintainers of WebVid and LAION datasets.", "## Contact Information\nfanfanda@URL and jianfengzhan.benchcouncil@URL\n\nIf you find our work useful in your research, please consider citing our paper:" ]

[ 35, 104, 110, 148, 37, 40 ]

[ "passage: TAGS\n#size_categories-1K<n<10K #license-apache-2.0 #arxiv-2401.01651 #region-us \n# AIGCBench v1.0\n\nAIGCBench is a novel and comprehensive benchmark designed for evaluating the capabilities of state-of-the-art video generation algorithms. Official dataset for the paper:AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI, *BenchCouncil Transactions on Benchmarks, Standards and Evaluations (TBench)*.\n<a href='URL src='URL## Description\n\nThis dataset is intended for the evaluation of video generation tasks. Our dataset includes image-text pairs and video-text pairs. The dataset comprises three parts:\n\n1. 'ours' - A custom generation of image-text samples.\n2. 'webvid val' - A subset of 1000 video samples from the WebVid val dataset.\n3. 'laion-aesthetics' - A subset of LAION dataset that includes 925 curated image-text samples.## Data Organization\n\nThe dataset is organized into the following folders and files:\n\n- 't2i_aspect_ratio_625.zip' - Contains images paired with text, adjusted to an aspect ratio of 0.625.\n- 'webvid_eval_1000.txt' - Contains video names for 1000 selected video samples. Considering that the first frame of the video may not contain the main information or might be a bad case, we use the tenth frame of the video as the initial frame.\n- 'Laion-aesthetics_select_samples.txt' - Contains metadata and annotations for 925 image-text samples.## Acknowledgments\nWe would like to thank all contributors and organizations behind the data sources, especially the maintainers of WebVid and LAION datasets.## Contact Information\nfanfanda@URL and jianfengzhan.benchcouncil@URL\n\nIf you find our work useful in your research, please consider citing our paper:" ]

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

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[ "en" ]

[]

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

[ 18 ]

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

[ "region:us" ]

[]

[]

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

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

[ 6, 15 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"asqa_origin\"\n\nMore Information needed" ]

[ "task_categories:table-to-text", "annotations_creators:none", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-sa-4.0", "data-to-text", "region:us" ]

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[ "en" ]

[ "# Dataset Card for GEM/viggo", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: URL\n- Leaderboard: N/A\n- Point of Contact: Juraj Juraska", "### Link to Main Data Card\n\nYou can find the main data card on the GEM Website.", "### Dataset Summary \n\nViGGO is an English data-to-text generation dataset in the video game domain, with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset is relatively small with about 5,000 datasets but very clean, and can thus serve for evaluating transfer learning, low-resource, or few-shot capabilities of neural models.\n\nYou can load the dataset via:\n\nThe data loader can be found here.", "#### website\nWesbite", "#### paper\nACL Anthology", "#### authors\nJuraj Juraska, Kevin K. Bowden, Marilyn Walker", "## Dataset Overview", "### Where to find the Data and its Documentation", "#### Webpage\n\n\n\nWesbite", "#### Paper\n\n\n\nACL Anthology", "#### BibTex", "#### Contact Name\n\n\n\n\nJuraj Juraska", "#### Contact Email\n\n\n\njjuraska@URL", "#### Has a Leaderboard?\n\n\n\nno", "### Languages and Intended Use", "#### Multilingual?\n\n\n\n\nno", "#### Covered Languages\n\n\n\n\n'English'", "#### License\n\n\n\n\ncc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International", "#### Intended Use\n\n\n\nViGGO was designed for the task of data-to-text generation in chatbots (as opposed to task-oriented dialogue systems), with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset, being relatively small and clean, can also serve for demonstrating transfer learning capabilities of neural models.", "#### Primary Task\n\n\n\nData-to-Text", "### Credit", "#### Curation Organization Type(s)\n\n\n\n'academic'", "#### Curation Organization(s)\n\n\n\nUniversity of California, Santa Cruz", "#### Dataset Creators\n\n\n\nJuraj Juraska, Kevin K. Bowden, Marilyn Walker", "#### Who added the Dataset to GEM?\n\n\n\nJuraj Juraska", "### Dataset Structure", "#### Data Fields\n\n\n\nEach example in the dataset has the following two fields:\n\n- 'mr': A meaning representation (MR) that, in a structured format, provides the information to convey, as well as the desired dialogue act (DA) type.\n- 'ref': A reference output, i.e., a corresponding utterance realizing all the information in the MR.\n\nEach MR is a flattened dictionary of attribute-and-value pairs, \"wrapped\" in the dialogue act type indication. This format was chosen primarily for its compactness, but also to allow for easy concatenation of multiple DAs (each with potentially different attributes) in a single MR.\n\nFollowing is the list of all possible attributes (which are also refered to as \"slots\") in ViGGO along with their types/possible values:\n\n- 'name': The name of a video game (e.g., Rise of the Tomb Raider).\n- 'release_year': The year a video game was released in (e.g., 2015).\n- 'exp_release_date': For a not-yet-released game, the date when it is expected to be released (e.g., February 22, 2019). *Note: This slot cannot appear together with 'release_year' in the same dialogue act.*\n- 'developer': The name of the studio/person that created the game (e.g., Crystal Dynamics).\n- 'genres': A list of one or more genre labels from a set of possible values (e.g., action-adventure, shooter).\n- 'player_perspective': A list of one or more perspectives from which the game is/can be played (possible values: first person, third person, side view, bird view).\n- 'platforms': A list of one or more gaming platforms the game was officially released for (possible values: PC, PlayStation, Xbox, Nintendo, Nintendo Switch).\n- 'esrb': A game's content rating as determined by the ESRB (possible values: E (for Everyone), E 10+ (for Everyone 10 and Older), T (for Teen), M (for Mature)).\n- 'rating': Depending on the dialogue act this slot is used with, it is a categorical representation of either the game's average rating or the game's liking (possible values: excellent, good, average, poor).\n- 'has_multiplayer': Indicates whether a game supports multiplayer or can only be played in single-player mode (possible values: yes, no).\n- 'available_on_steam': Indicates whether a game can be purchased through the Steam digital distribution service (possible values: yes, no).\n- 'has_linux_release': Indicates whether a game is supported on Linux operating systems (possible values: yes, no).\n- 'has_mac_release': Indicates whether a game is supported on macOS (possible values: yes, no).\n- 'specifier': A game specifier used by the 'request' DA, typically an adjective (e.g., addictive, easiest, overrated, visually impressive).\n\nEach MR in the dataset has 3 distinct reference utterances, which are represented as 3 separate examples with the same MR.", "#### Reason for Structure\n\n\n\nThe dataset structure mostly follows the format of the popular E2E dataset, however, with added dialogue act type indications, new list-type attributes introduced, and unified naming convention for multi-word attribute names.", "#### Example Instance", "#### Data Splits\n\n\n\nViGGO is split into 3 partitions, with no MRs in common between the training set and either of the validation and the test set (and that *after* delexicalizing the 'name' and 'developer' slots). The ratio of examples in the partitions is approximately 7.5 : 1 : 1.5, with their exact sizes listed below:\n\n- Train: 5,103 (1,675 unique MRs)\n- Validation: 714 (238 unique MRs)\n- Test: 1,083 (359 unique MRs)\n- TOTAL: 6,900 (2,253 unique MRs)\n\n*Note: The reason why the number of unique MRs is not exactly one third of all examples is that for each 'request_attribute' DA (which only has one slot, and that without a value) 12 reference utterances were collected instead of 3.*", "#### Splitting Criteria\n\n\n\nA similar MR length and slot distribution was preserved across the partitions. The distribution of DA types, on the other hand, is skewed slightly toward fewer 'inform' DA instances (the most prevalent DA type) and a higher proportion of the less prevalent DAs in the validation and the test set.", "####", "## Dataset in GEM", "### Rationale for Inclusion in GEM", "#### Why is the Dataset in GEM?\n\n\n\nViGGO is a fairly small dataset but includes a greater variety of utterance types than most other datasets for NLG from structured meaning representations. This makes it more interesting from the perspective of model evaluation, since models have to learn to differentiate between various dialogue act types that share the same slots.", "#### Similar Datasets\n\n\n\nyes", "#### Unique Language Coverage\n\n\n\nno", "#### Difference from other GEM datasets\n\n\n\nViGGO's language is more casual and conversational -- as opposed to information-seeking -- which differentiates it from the majority of popular datasets for the same type of data-to-text task. Moreover, the video game domain is a rather uncommon one in the NLG community, despite being very well-suited for data-to-text generation, considering it offers entities with many attributes to talk about, which can be described in a structured format.", "### GEM-Specific Curation", "#### Modificatied for GEM?\n\n\n\nno", "#### Additional Splits?\n\n\n\nno", "### Getting Started with the Task", "#### Pointers to Resources\n\n\n\n- E2E NLG Challenge", "#### Technical Terms\n\n\n\n- MR = meaning representation\n- DA = dialogue act", "## Previous Results", "### Previous Results", "#### Metrics\n\n\n\n'BLEU', 'METEOR', 'ROUGE', 'BERT-Score', 'BLEURT', 'Other: Other Metrics'", "#### Other Metrics\n\n\n\nSER (slot error rate): Indicates the proportion of missing/incorrect/duplicate/hallucinated slot mentions in the utterances across a test set. The closer to zero a model scores in this metric, the more semantically accurate its outputs are. This metric is typically calculated either manually on a small sample of generated outputs, or heuristically using domain-specific regex rules and gazetteers.", "#### Previous results available?\n\n\n\nyes", "#### Relevant Previous Results\n\n\n\n- Juraska et al., 2019. ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation.\n- Harkous et al., 2020. Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity.\n- Kedzie and McKeown, 2020. Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies.\n- Juraska and Walker, 2021. Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLG.", "## Dataset Curation", "### Original Curation", "#### Original Curation Rationale\n\n\n\nThe primary motivation behind ViGGO was to create a data-to-text corpus in a new but conversational domain, and intended for use in open-domain chatbots rather than task-oriented dialogue systems. To this end, the dataset contains utterances of 9 generalizable and conversational dialogue act types, revolving around various aspects of video games. The idea is that similar, relatively small datasets could fairly easily be collected for other conversational domains -- especially other entertainment domains (such as music or books), but perhaps also topics like animals or food -- to support an open-domain conversational agent with controllable neural NLG.\n\nAnother desired quality of the ViGGO dataset was cleanliness (no typos and grammatical errors) and semantic accuracy, which has often not been the case with other crowdsourced data-to-text corpora. In general, for the data-to-text generation task, there is arguably no need to put the burden on the generation model to figure out the noise, since the noise would not be expected to be there in a real-world system whose dialogue manager that creates the input for the NLG module is usually configurable and tightly controlled.", "#### Communicative Goal\n\n\n\nProduce a response from a structured meaning representation in the context of a conversation about video games. It can be a brief opinion or a description of a game, as well as a request for attribute (e.g., genre, player perspective, or platform) preference/confirmation or an inquiry about liking a particular type of games.", "#### Sourced from Different Sources\n\n\n\nno", "### Language Data", "#### How was Language Data Obtained?\n\n\n\n'Crowdsourced'", "#### Where was it crowdsourced?\n\n\n\n'Amazon Mechanical Turk'", "#### Language Producers\n\n\n\nThe paid crowdworkers who produced the reference utterances were from English-speaking countries, and they had at least 1,000 HITs approved and a HIT approval rate of 98% or more. Furthermore, in the instructions, crowdworkers were discouraged from taking on the task unless they considered themselves a gamer.", "#### Topics Covered\n\n\n\nThe dataset focuses on video games and their various aspects, and hence the language of the utterances may contain video game-specific jargon.", "#### Data Validation\n\n\n\nvalidated by data curator", "#### Data Preprocessing\n\n\n\nFirst, regular expressions were used to enforce several standardization policies regarding special characters, punctuation, and the correction of undesired abbreviations/misspellings of standard domain-specific terms (e.g., terms like \"Play station\" or \"PS4\" would be changed to the uniform \"PlayStation\"). At the same time, hyphens were removed or enforced uniformly in certain terms, for example, \"single-player\". Although phrases such as \"first person\" should correctly have a hyphen when used as adjective, the crowdworkers used this rule very inconsistently. In order to avoid model outputs being penalized during the evaluation by the arbitrary choice of a hyphen presence or absence in the reference utterances, the hyphen was removed in all such phrases regardless of the noun vs. adjective use.\n\nSecond, an extensive set of heuristics was developed to identify slot-related errors. This process revealed the vast majority of missing or incorrect slot mentions, which were subsequently fixed according to the corresponding MRs. This eventually led to the development of a robust, cross-domain, heuristic slot aligner that can be used for automatic slot error rate evaluation. For details, see the appendix in Juraska and Walker, 2021.\n\nCrowdworkers would sometimes also inject a piece of information which was not present in the MR, some of which is not even represented by any of the slots, e.g., plot or main characters. This unsolicited information was removed from the utterances so as to avoid confusing the neural model. Finally, any remaining typos and grammatical errors were resolved.", "#### Was Data Filtered?\n\n\n\nmanually", "#### Filter Criteria\n\n\n\nCompliance with the indicated dialogue act type, semantic accuracy (i.e., all information in the corresponding MR mentioned and that correctly), and minimal extraneous information (e.g., personal experience/opinion). Whenever it was within a reasonable amount of effort, the utterances were manually fixed instead of being discarded/crowdsourced anew.", "### Structured Annotations", "#### Additional Annotations?\n\n\n\n\nnone", "#### Annotation Service?\n\n\n\nno", "### Consent", "#### Any Consent Policy?\n\n\n\nno", "### Private Identifying Information (PII)", "#### Contains PII?\n\n\n\n\nno PII", "#### Justification for no PII\n\n\n\nCrowdworkers were instructed to only express the information in the provided meaning representation, which never prompted them to mention anything about themselves. Occasionally, they would still include a bit of personal experience (e.g., \"I used to like the game as a kid.\") or opinion, but these would be too general to be considered PII.", "### Maintenance", "#### Any Maintenance Plan?\n\n\n\nno", "## Broader Social Context", "### Previous Work on the Social Impact of the Dataset", "#### Usage of Models based on the Data\n\n\n\nno", "### Impact on Under-Served Communities", "#### Addresses needs of underserved Communities?\n\n\n\nno", "### Discussion of Biases", "#### Any Documented Social Biases?\n\n\n\nno", "## Considerations for Using the Data", "### PII Risks and Liability", "### Licenses", "### Known Technical Limitations", "#### Technical Limitations\n\n\n\nThe dataset is limited to a single domain: video games. One caveat of using a language generator trained on this dataset in a dialogue system as-is is that multiple subsequent turns discussing the same video game would be repeating its full name. ViGGO was designed for generation without context, and therefore it is up to the dialogue manager to ensure that pronouns are substituted for the names whenever it would sound more natural in a dialogue. Alternately, the dataset can easily be augmented with automatically constructed samples which omit the 'name' slot in the MR and replace the name with a pronoun in the reference utterance." ]

[ "TAGS\n#task_categories-table-to-text #annotations_creators-none #language_creators-unknown #multilinguality-unknown #size_categories-unknown #source_datasets-original #language-English #license-cc-by-sa-4.0 #data-to-text #region-us \n", "# Dataset Card for GEM/viggo", "## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: URL\n- Leaderboard: N/A\n- Point of Contact: Juraj Juraska", "### Link to Main Data Card\n\nYou can find the main data card on the GEM Website.", "### Dataset Summary \n\nViGGO is an English data-to-text generation dataset in the video game domain, with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset is relatively small with about 5,000 datasets but very clean, and can thus serve for evaluating transfer learning, low-resource, or few-shot capabilities of neural models.\n\nYou can load the dataset via:\n\nThe data loader can be found here.", "#### website\nWesbite", "#### paper\nACL Anthology", "#### authors\nJuraj Juraska, Kevin K. Bowden, Marilyn Walker", "## Dataset Overview", "### Where to find the Data and its Documentation", "#### Webpage\n\n\n\nWesbite", "#### Paper\n\n\n\nACL Anthology", "#### BibTex", "#### Contact Name\n\n\n\n\nJuraj Juraska", "#### Contact Email\n\n\n\njjuraska@URL", "#### Has a Leaderboard?\n\n\n\nno", "### Languages and Intended Use", "#### Multilingual?\n\n\n\n\nno", "#### Covered Languages\n\n\n\n\n'English'", "#### License\n\n\n\n\ncc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International", "#### Intended Use\n\n\n\nViGGO was designed for the task of data-to-text generation in chatbots (as opposed to task-oriented dialogue systems), with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset, being relatively small and clean, can also serve for demonstrating transfer learning capabilities of neural models.", "#### Primary Task\n\n\n\nData-to-Text", "### Credit", "#### Curation Organization Type(s)\n\n\n\n'academic'", "#### Curation Organization(s)\n\n\n\nUniversity of California, Santa Cruz", "#### Dataset Creators\n\n\n\nJuraj Juraska, Kevin K. Bowden, Marilyn Walker", "#### Who added the Dataset to GEM?\n\n\n\nJuraj Juraska", "### Dataset Structure", "#### Data Fields\n\n\n\nEach example in the dataset has the following two fields:\n\n- 'mr': A meaning representation (MR) that, in a structured format, provides the information to convey, as well as the desired dialogue act (DA) type.\n- 'ref': A reference output, i.e., a corresponding utterance realizing all the information in the MR.\n\nEach MR is a flattened dictionary of attribute-and-value pairs, \"wrapped\" in the dialogue act type indication. This format was chosen primarily for its compactness, but also to allow for easy concatenation of multiple DAs (each with potentially different attributes) in a single MR.\n\nFollowing is the list of all possible attributes (which are also refered to as \"slots\") in ViGGO along with their types/possible values:\n\n- 'name': The name of a video game (e.g., Rise of the Tomb Raider).\n- 'release_year': The year a video game was released in (e.g., 2015).\n- 'exp_release_date': For a not-yet-released game, the date when it is expected to be released (e.g., February 22, 2019). *Note: This slot cannot appear together with 'release_year' in the same dialogue act.*\n- 'developer': The name of the studio/person that created the game (e.g., Crystal Dynamics).\n- 'genres': A list of one or more genre labels from a set of possible values (e.g., action-adventure, shooter).\n- 'player_perspective': A list of one or more perspectives from which the game is/can be played (possible values: first person, third person, side view, bird view).\n- 'platforms': A list of one or more gaming platforms the game was officially released for (possible values: PC, PlayStation, Xbox, Nintendo, Nintendo Switch).\n- 'esrb': A game's content rating as determined by the ESRB (possible values: E (for Everyone), E 10+ (for Everyone 10 and Older), T (for Teen), M (for Mature)).\n- 'rating': Depending on the dialogue act this slot is used with, it is a categorical representation of either the game's average rating or the game's liking (possible values: excellent, good, average, poor).\n- 'has_multiplayer': Indicates whether a game supports multiplayer or can only be played in single-player mode (possible values: yes, no).\n- 'available_on_steam': Indicates whether a game can be purchased through the Steam digital distribution service (possible values: yes, no).\n- 'has_linux_release': Indicates whether a game is supported on Linux operating systems (possible values: yes, no).\n- 'has_mac_release': Indicates whether a game is supported on macOS (possible values: yes, no).\n- 'specifier': A game specifier used by the 'request' DA, typically an adjective (e.g., addictive, easiest, overrated, visually impressive).\n\nEach MR in the dataset has 3 distinct reference utterances, which are represented as 3 separate examples with the same MR.", "#### Reason for Structure\n\n\n\nThe dataset structure mostly follows the format of the popular E2E dataset, however, with added dialogue act type indications, new list-type attributes introduced, and unified naming convention for multi-word attribute names.", "#### Example Instance", "#### Data Splits\n\n\n\nViGGO is split into 3 partitions, with no MRs in common between the training set and either of the validation and the test set (and that *after* delexicalizing the 'name' and 'developer' slots). The ratio of examples in the partitions is approximately 7.5 : 1 : 1.5, with their exact sizes listed below:\n\n- Train: 5,103 (1,675 unique MRs)\n- Validation: 714 (238 unique MRs)\n- Test: 1,083 (359 unique MRs)\n- TOTAL: 6,900 (2,253 unique MRs)\n\n*Note: The reason why the number of unique MRs is not exactly one third of all examples is that for each 'request_attribute' DA (which only has one slot, and that without a value) 12 reference utterances were collected instead of 3.*", "#### Splitting Criteria\n\n\n\nA similar MR length and slot distribution was preserved across the partitions. The distribution of DA types, on the other hand, is skewed slightly toward fewer 'inform' DA instances (the most prevalent DA type) and a higher proportion of the less prevalent DAs in the validation and the test set.", "####", "## Dataset in GEM", "### Rationale for Inclusion in GEM", "#### Why is the Dataset in GEM?\n\n\n\nViGGO is a fairly small dataset but includes a greater variety of utterance types than most other datasets for NLG from structured meaning representations. This makes it more interesting from the perspective of model evaluation, since models have to learn to differentiate between various dialogue act types that share the same slots.", "#### Similar Datasets\n\n\n\nyes", "#### Unique Language Coverage\n\n\n\nno", "#### Difference from other GEM datasets\n\n\n\nViGGO's language is more casual and conversational -- as opposed to information-seeking -- which differentiates it from the majority of popular datasets for the same type of data-to-text task. Moreover, the video game domain is a rather uncommon one in the NLG community, despite being very well-suited for data-to-text generation, considering it offers entities with many attributes to talk about, which can be described in a structured format.", "### GEM-Specific Curation", "#### Modificatied for GEM?\n\n\n\nno", "#### Additional Splits?\n\n\n\nno", "### Getting Started with the Task", "#### Pointers to Resources\n\n\n\n- E2E NLG Challenge", "#### Technical Terms\n\n\n\n- MR = meaning representation\n- DA = dialogue act", "## Previous Results", "### Previous Results", "#### Metrics\n\n\n\n'BLEU', 'METEOR', 'ROUGE', 'BERT-Score', 'BLEURT', 'Other: Other Metrics'", "#### Other Metrics\n\n\n\nSER (slot error rate): Indicates the proportion of missing/incorrect/duplicate/hallucinated slot mentions in the utterances across a test set. The closer to zero a model scores in this metric, the more semantically accurate its outputs are. This metric is typically calculated either manually on a small sample of generated outputs, or heuristically using domain-specific regex rules and gazetteers.", "#### Previous results available?\n\n\n\nyes", "#### Relevant Previous Results\n\n\n\n- Juraska et al., 2019. ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation.\n- Harkous et al., 2020. Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity.\n- Kedzie and McKeown, 2020. Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies.\n- Juraska and Walker, 2021. Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLG.", "## Dataset Curation", "### Original Curation", "#### Original Curation Rationale\n\n\n\nThe primary motivation behind ViGGO was to create a data-to-text corpus in a new but conversational domain, and intended for use in open-domain chatbots rather than task-oriented dialogue systems. To this end, the dataset contains utterances of 9 generalizable and conversational dialogue act types, revolving around various aspects of video games. The idea is that similar, relatively small datasets could fairly easily be collected for other conversational domains -- especially other entertainment domains (such as music or books), but perhaps also topics like animals or food -- to support an open-domain conversational agent with controllable neural NLG.\n\nAnother desired quality of the ViGGO dataset was cleanliness (no typos and grammatical errors) and semantic accuracy, which has often not been the case with other crowdsourced data-to-text corpora. In general, for the data-to-text generation task, there is arguably no need to put the burden on the generation model to figure out the noise, since the noise would not be expected to be there in a real-world system whose dialogue manager that creates the input for the NLG module is usually configurable and tightly controlled.", "#### Communicative Goal\n\n\n\nProduce a response from a structured meaning representation in the context of a conversation about video games. It can be a brief opinion or a description of a game, as well as a request for attribute (e.g., genre, player perspective, or platform) preference/confirmation or an inquiry about liking a particular type of games.", "#### Sourced from Different Sources\n\n\n\nno", "### Language Data", "#### How was Language Data Obtained?\n\n\n\n'Crowdsourced'", "#### Where was it crowdsourced?\n\n\n\n'Amazon Mechanical Turk'", "#### Language Producers\n\n\n\nThe paid crowdworkers who produced the reference utterances were from English-speaking countries, and they had at least 1,000 HITs approved and a HIT approval rate of 98% or more. Furthermore, in the instructions, crowdworkers were discouraged from taking on the task unless they considered themselves a gamer.", "#### Topics Covered\n\n\n\nThe dataset focuses on video games and their various aspects, and hence the language of the utterances may contain video game-specific jargon.", "#### Data Validation\n\n\n\nvalidated by data curator", "#### Data Preprocessing\n\n\n\nFirst, regular expressions were used to enforce several standardization policies regarding special characters, punctuation, and the correction of undesired abbreviations/misspellings of standard domain-specific terms (e.g., terms like \"Play station\" or \"PS4\" would be changed to the uniform \"PlayStation\"). At the same time, hyphens were removed or enforced uniformly in certain terms, for example, \"single-player\". Although phrases such as \"first person\" should correctly have a hyphen when used as adjective, the crowdworkers used this rule very inconsistently. In order to avoid model outputs being penalized during the evaluation by the arbitrary choice of a hyphen presence or absence in the reference utterances, the hyphen was removed in all such phrases regardless of the noun vs. adjective use.\n\nSecond, an extensive set of heuristics was developed to identify slot-related errors. This process revealed the vast majority of missing or incorrect slot mentions, which were subsequently fixed according to the corresponding MRs. This eventually led to the development of a robust, cross-domain, heuristic slot aligner that can be used for automatic slot error rate evaluation. For details, see the appendix in Juraska and Walker, 2021.\n\nCrowdworkers would sometimes also inject a piece of information which was not present in the MR, some of which is not even represented by any of the slots, e.g., plot or main characters. This unsolicited information was removed from the utterances so as to avoid confusing the neural model. Finally, any remaining typos and grammatical errors were resolved.", "#### Was Data Filtered?\n\n\n\nmanually", "#### Filter Criteria\n\n\n\nCompliance with the indicated dialogue act type, semantic accuracy (i.e., all information in the corresponding MR mentioned and that correctly), and minimal extraneous information (e.g., personal experience/opinion). Whenever it was within a reasonable amount of effort, the utterances were manually fixed instead of being discarded/crowdsourced anew.", "### Structured Annotations", "#### Additional Annotations?\n\n\n\n\nnone", "#### Annotation Service?\n\n\n\nno", "### Consent", "#### Any Consent Policy?\n\n\n\nno", "### Private Identifying Information (PII)", "#### Contains PII?\n\n\n\n\nno PII", "#### Justification for no PII\n\n\n\nCrowdworkers were instructed to only express the information in the provided meaning representation, which never prompted them to mention anything about themselves. Occasionally, they would still include a bit of personal experience (e.g., \"I used to like the game as a kid.\") or opinion, but these would be too general to be considered PII.", "### Maintenance", "#### Any Maintenance Plan?\n\n\n\nno", "## Broader Social Context", "### Previous Work on the Social Impact of the Dataset", "#### Usage of Models based on the Data\n\n\n\nno", "### Impact on Under-Served Communities", "#### Addresses needs of underserved Communities?\n\n\n\nno", "### Discussion of Biases", "#### Any Documented Social Biases?\n\n\n\nno", "## Considerations for Using the Data", "### PII Risks and Liability", "### Licenses", "### Known Technical Limitations", "#### Technical Limitations\n\n\n\nThe dataset is limited to a single domain: video games. One caveat of using a language generator trained on this dataset in a dialogue system as-is is that multiple subsequent turns discussing the same video game would be repeating its full name. ViGGO was designed for generation without context, and therefore it is up to the dialogue manager to ensure that pronouns are substituted for the names whenever it would sound more natural in a dialogue. Alternately, the dataset can easily be augmented with automatically constructed samples which omit the 'name' slot in the MR and replace the name with a pronoun in the reference utterance." ]

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[ "passage: TAGS\n#task_categories-table-to-text #annotations_creators-none #language_creators-unknown #multilinguality-unknown #size_categories-unknown #source_datasets-original #language-English #license-cc-by-sa-4.0 #data-to-text #region-us \n# Dataset Card for GEM/viggo## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: URL\n- Leaderboard: N/A\n- Point of Contact: Juraj Juraska### Link to Main Data Card\n\nYou can find the main data card on the GEM Website.### Dataset Summary \n\nViGGO is an English data-to-text generation dataset in the video game domain, with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset is relatively small with about 5,000 datasets but very clean, and can thus serve for evaluating transfer learning, low-resource, or few-shot capabilities of neural models.\n\nYou can load the dataset via:\n\nThe data loader can be found here.#### website\nWesbite#### paper\nACL Anthology#### authors\nJuraj Juraska, Kevin K. Bowden, Marilyn Walker## Dataset Overview### Where to find the Data and its Documentation#### Webpage\n\n\n\nWesbite#### Paper\n\n\n\nACL Anthology#### BibTex#### Contact Name\n\n\n\n\nJuraj Juraska#### Contact Email\n\n\n\njjuraska@URL#### Has a Leaderboard?\n\n\n\nno### Languages and Intended Use#### Multilingual?\n\n\n\n\nno#### Covered Languages\n\n\n\n\n'English'#### License\n\n\n\n\ncc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International#### Intended Use\n\n\n\nViGGO was designed for the task of data-to-text generation in chatbots (as opposed to task-oriented dialogue systems), with target responses being more conversational than information-seeking, yet constrained to the information presented in a meaning representation. The dataset, being relatively small and clean, can also serve for demonstrating transfer learning capabilities of neural models.#### Primary Task\n\n\n\nData-to-Text", "passage: ### Credit#### Curation Organization Type(s)\n\n\n\n'academic'#### Curation Organization(s)\n\n\n\nUniversity of California, Santa Cruz#### Dataset Creators\n\n\n\nJuraj Juraska, Kevin K. Bowden, Marilyn Walker#### Who added the Dataset to GEM?\n\n\n\nJuraj Juraska### Dataset Structure", "passage: #### Data Fields\n\n\n\nEach example in the dataset has the following two fields:\n\n- 'mr': A meaning representation (MR) that, in a structured format, provides the information to convey, as well as the desired dialogue act (DA) type.\n- 'ref': A reference output, i.e., a corresponding utterance realizing all the information in the MR.\n\nEach MR is a flattened dictionary of attribute-and-value pairs, \"wrapped\" in the dialogue act type indication. This format was chosen primarily for its compactness, but also to allow for easy concatenation of multiple DAs (each with potentially different attributes) in a single MR.\n\nFollowing is the list of all possible attributes (which are also refered to as \"slots\") in ViGGO along with their types/possible values:\n\n- 'name': The name of a video game (e.g., Rise of the Tomb Raider).\n- 'release_year': The year a video game was released in (e.g., 2015).\n- 'exp_release_date': For a not-yet-released game, the date when it is expected to be released (e.g., February 22, 2019). *Note: This slot cannot appear together with 'release_year' in the same dialogue act.*\n- 'developer': The name of the studio/person that created the game (e.g., Crystal Dynamics).\n- 'genres': A list of one or more genre labels from a set of possible values (e.g., action-adventure, shooter).\n- 'player_perspective': A list of one or more perspectives from which the game is/can be played (possible values: first person, third person, side view, bird view).\n- 'platforms': A list of one or more gaming platforms the game was officially released for (possible values: PC, PlayStation, Xbox, Nintendo, Nintendo Switch).\n- 'esrb': A game's content rating as determined by the ESRB (possible values: E (for Everyone), E 10+ (for Everyone 10 and Older), T (for Teen), M (for Mature)).\n- 'rating': Depending on the dialogue act this slot is used with, it is a categorical representation of either the game's average rating or the game's liking (possible values: excellent, good, average, poor).\n- 'has_multiplayer': Indicates whether a game supports multiplayer or can only be played in single-player mode (possible values: yes, no).\n- 'available_on_steam': Indicates whether a game can be purchased through the Steam digital distribution service (possible values: yes, no).\n- 'has_linux_release': Indicates whether a game is supported on Linux operating systems (possible values: yes, no).\n- 'has_mac_release': Indicates whether a game is supported on macOS (possible values: yes, no).\n- 'specifier': A game specifier used by the 'request' DA, typically an adjective (e.g., addictive, easiest, overrated, visually impressive).\n\nEach MR in the dataset has 3 distinct reference utterances, which are represented as 3 separate examples with the same MR.#### Reason for Structure\n\n\n\nThe dataset structure mostly follows the format of the popular E2E dataset, however, with added dialogue act type indications, new list-type attributes introduced, and unified naming convention for multi-word attribute names.#### Example Instance#### Data Splits\n\n\n\nViGGO is split into 3 partitions, with no MRs in common between the training set and either of the validation and the test set (and that *after* delexicalizing the 'name' and 'developer' slots). The ratio of examples in the partitions is approximately 7.5 : 1 : 1.5, with their exact sizes listed below:\n\n- Train: 5,103 (1,675 unique MRs)\n- Validation: 714 (238 unique MRs)\n- Test: 1,083 (359 unique MRs)\n- TOTAL: 6,900 (2,253 unique MRs)\n\n*Note: The reason why the number of unique MRs is not exactly one third of all examples is that for each 'request_attribute' DA (which only has one slot, and that without a value) 12 reference utterances were collected instead of 3.*#### Splitting Criteria\n\n\n\nA similar MR length and slot distribution was preserved across the partitions. The distribution of DA types, on the other hand, is skewed slightly toward fewer 'inform' DA instances (the most prevalent DA type) and a higher proportion of the less prevalent DAs in the validation and the test set.###### Dataset in GEM### Rationale for Inclusion in GEM#### Why is the Dataset in GEM?\n\n\n\nViGGO is a fairly small dataset but includes a greater variety of utterance types than most other datasets for NLG from structured meaning representations. This makes it more interesting from the perspective of model evaluation, since models have to learn to differentiate between various dialogue act types that share the same slots.#### Similar Datasets\n\n\n\nyes#### Unique Language Coverage\n\n\n\nno", "passage: #### Difference from other GEM datasets\n\n\n\nViGGO's language is more casual and conversational -- as opposed to information-seeking -- which differentiates it from the majority of popular datasets for the same type of data-to-text task. Moreover, the video game domain is a rather uncommon one in the NLG community, despite being very well-suited for data-to-text generation, considering it offers entities with many attributes to talk about, which can be described in a structured format.### GEM-Specific Curation#### Modificatied for GEM?\n\n\n\nno#### Additional Splits?\n\n\n\nno### Getting Started with the Task#### Pointers to Resources\n\n\n\n- E2E NLG Challenge#### Technical Terms\n\n\n\n- MR = meaning representation\n- DA = dialogue act## Previous Results### Previous Results#### Metrics\n\n\n\n'BLEU', 'METEOR', 'ROUGE', 'BERT-Score', 'BLEURT', 'Other: Other Metrics'#### Other Metrics\n\n\n\nSER (slot error rate): Indicates the proportion of missing/incorrect/duplicate/hallucinated slot mentions in the utterances across a test set. The closer to zero a model scores in this metric, the more semantically accurate its outputs are. This metric is typically calculated either manually on a small sample of generated outputs, or heuristically using domain-specific regex rules and gazetteers.#### Previous results available?\n\n\n\nyes#### Relevant Previous Results\n\n\n\n- Juraska et al., 2019. ViGGO: A Video Game Corpus for Data-To-Text Generation in Open-Domain Conversation.\n- Harkous et al., 2020. Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity.\n- Kedzie and McKeown, 2020. Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies.\n- Juraska and Walker, 2021. Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLG.## Dataset Curation### Original Curation", "passage: #### Original Curation Rationale\n\n\n\nThe primary motivation behind ViGGO was to create a data-to-text corpus in a new but conversational domain, and intended for use in open-domain chatbots rather than task-oriented dialogue systems. To this end, the dataset contains utterances of 9 generalizable and conversational dialogue act types, revolving around various aspects of video games. The idea is that similar, relatively small datasets could fairly easily be collected for other conversational domains -- especially other entertainment domains (such as music or books), but perhaps also topics like animals or food -- to support an open-domain conversational agent with controllable neural NLG.\n\nAnother desired quality of the ViGGO dataset was cleanliness (no typos and grammatical errors) and semantic accuracy, which has often not been the case with other crowdsourced data-to-text corpora. In general, for the data-to-text generation task, there is arguably no need to put the burden on the generation model to figure out the noise, since the noise would not be expected to be there in a real-world system whose dialogue manager that creates the input for the NLG module is usually configurable and tightly controlled.#### Communicative Goal\n\n\n\nProduce a response from a structured meaning representation in the context of a conversation about video games. It can be a brief opinion or a description of a game, as well as a request for attribute (e.g., genre, player perspective, or platform) preference/confirmation or an inquiry about liking a particular type of games.#### Sourced from Different Sources\n\n\n\nno### Language Data#### How was Language Data Obtained?\n\n\n\n'Crowdsourced'#### Where was it crowdsourced?\n\n\n\n'Amazon Mechanical Turk'#### Language Producers\n\n\n\nThe paid crowdworkers who produced the reference utterances were from English-speaking countries, and they had at least 1,000 HITs approved and a HIT approval rate of 98% or more. Furthermore, in the instructions, crowdworkers were discouraged from taking on the task unless they considered themselves a gamer.#### Topics Covered\n\n\n\nThe dataset focuses on video games and their various aspects, and hence the language of the utterances may contain video game-specific jargon.#### Data Validation\n\n\n\nvalidated by data curator" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"training_v0.0.6-public\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"training_v0.0.6-public\"\n\nMore Information needed" ]

[ 6, 18 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"training_v0.0.6-public\"\n\nMore Information needed" ]

[ "task_categories:conversational", "task_categories:text-generation", "size_categories:10K<n<100K", "language:zh", "license:apache-2.0", "region:us" ]

[]

[ "zh" ]

[]

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

[ 52 ]

[ "passage: TAGS\n#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-Chinese #license-apache-2.0 #region-us \n" ]

[ "license:mit", "arxiv:2310.08232", "region:us" ]

[ "2310.08232" ]

[]

[]

[ "TAGS\n#license-mit #arxiv-2310.08232 #region-us \n" ]

[ 20 ]

[ "passage: TAGS\n#license-mit #arxiv-2310.08232 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "### Access on Hugging Face\n\n\nWe are also providing downloads on Hugging Face. You must first request a download from the Meta website using the same email address as your Hugging Face account. After doing so, you can request access to any of the models on Hugging Face and within 1-2 days your account will be granted access to all versions.\n\n\nQuick Start\n-----------\n\n\nYou can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the Llama 2 recipes repository.\n\n\n1. In a conda env with PyTorch / CUDA available clone and download this repository.\n2. In the top level directory run:\n3. Visit the Meta website and register to download the model/s.\n4. Once registered, you will get an email with a URL to download the models. You will need this URL when you run the URL script.\n5. Once you get the email, navigate to your downloaded llama repository and run the URL script.\n\n\n\t* Make sure to grant execution permissions to the URL script\n\t* During this process, you will be prompted to enter the URL from the email.\n\t* Do not use the “Copy Link” option but rather make sure to manually copy the link from the email.\n6. Once the model/s you want have been downloaded, you can run the model locally using the command below:\n\n\nNote\n\n\n* Replace 'llama-2-7b-chat/' with the path to your checkpoint directory and 'URL' with the path to your tokenizer model.\n* The '–nproc\\_per\\_node' should be set to the MP value for the model you are using.\n* Adjust the 'max\\_seq\\_len' and 'max\\_batch\\_size' parameters as needed.\n* This example runs the example\\_chat\\_completion.py found in this repository but you can change that to a different .py file.\n\n\nInference\n---------\n\n\nDifferent models require different model-parallel (MP) values:\n\n\n\nAll models support sequence length up to 4096 tokens, but we pre-allocate the cache according to 'max\\_seq\\_len' and 'max\\_batch\\_size' values. So set those according to your hardware.", "### Pretrained Models\n\n\nThese models are not finetuned for chat or Q&A. They should be prompted so that the expected answer is the natural continuation of the prompt.\n\n\nSee 'example\\_text\\_completion.py' for some examples. To illustrate, see the command below to run it with the llama-2-7b model ('nproc\\_per\\_node' needs to be set to the 'MP' value):", "### Fine-tuned Chat Models\n\n\nThe fine-tuned models were trained for dialogue applications. To get the expected features and performance for them, a specific formatting defined in 'chat\\_completion'\nneeds to be followed, including the 'INST' and '<>' tags, 'BOS' and 'EOS' tokens, and the whitespaces and breaklines in between (we recommend calling 'strip()' on inputs to avoid double-spaces).\n\n\nYou can also deploy additional classifiers for filtering out inputs and outputs that are deemed unsafe. See the llama-recipes repo for an example of how to add a safety checker to the inputs and outputs of your inference code.\n\n\nExamples using llama-2-7b-chat:\n\n\nLlama 2 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios.\nIn order to help developers address these risks, we have created the Responsible Use Guide. More details can be found in our research paper as well.\n\n\nIssues\n------\n\n\nPlease report any software “bug”, or other problems with the models through one of the following means:\n\n\n* Reporting issues with the model: URL\n* Reporting risky content generated by the model: URL\n* Reporting bugs and security concerns: URL\n\n\nModel Card\n----------\n\n\nSee MODEL\\_CARD.md.\n\n\nLicense\n-------\n\n\nOur model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.\n\n\nSee the LICENSE file, as well as our accompanying Acceptable Use Policy\n\n\nReferences\n----------\n\n\n1. Research Paper\n2. Llama 2 technical overview\n3. Open Innovation AI Research Community\n\n\nFor common questions, the FAQ can be found here which will be kept up to date over time as new questions arise.\n\n\nOriginal LLaMA\n--------------\n\n\nThe repo for the original llama release is in the 'llama\\_v1' branch." ]

[ "TAGS\n#region-us \n", "### Access on Hugging Face\n\n\nWe are also providing downloads on Hugging Face. You must first request a download from the Meta website using the same email address as your Hugging Face account. After doing so, you can request access to any of the models on Hugging Face and within 1-2 days your account will be granted access to all versions.\n\n\nQuick Start\n-----------\n\n\nYou can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the Llama 2 recipes repository.\n\n\n1. In a conda env with PyTorch / CUDA available clone and download this repository.\n2. In the top level directory run:\n3. Visit the Meta website and register to download the model/s.\n4. Once registered, you will get an email with a URL to download the models. You will need this URL when you run the URL script.\n5. Once you get the email, navigate to your downloaded llama repository and run the URL script.\n\n\n\t* Make sure to grant execution permissions to the URL script\n\t* During this process, you will be prompted to enter the URL from the email.\n\t* Do not use the “Copy Link” option but rather make sure to manually copy the link from the email.\n6. Once the model/s you want have been downloaded, you can run the model locally using the command below:\n\n\nNote\n\n\n* Replace 'llama-2-7b-chat/' with the path to your checkpoint directory and 'URL' with the path to your tokenizer model.\n* The '–nproc\\_per\\_node' should be set to the MP value for the model you are using.\n* Adjust the 'max\\_seq\\_len' and 'max\\_batch\\_size' parameters as needed.\n* This example runs the example\\_chat\\_completion.py found in this repository but you can change that to a different .py file.\n\n\nInference\n---------\n\n\nDifferent models require different model-parallel (MP) values:\n\n\n\nAll models support sequence length up to 4096 tokens, but we pre-allocate the cache according to 'max\\_seq\\_len' and 'max\\_batch\\_size' values. So set those according to your hardware.", "### Pretrained Models\n\n\nThese models are not finetuned for chat or Q&A. They should be prompted so that the expected answer is the natural continuation of the prompt.\n\n\nSee 'example\\_text\\_completion.py' for some examples. To illustrate, see the command below to run it with the llama-2-7b model ('nproc\\_per\\_node' needs to be set to the 'MP' value):", "### Fine-tuned Chat Models\n\n\nThe fine-tuned models were trained for dialogue applications. To get the expected features and performance for them, a specific formatting defined in 'chat\\_completion'\nneeds to be followed, including the 'INST' and '<>' tags, 'BOS' and 'EOS' tokens, and the whitespaces and breaklines in between (we recommend calling 'strip()' on inputs to avoid double-spaces).\n\n\nYou can also deploy additional classifiers for filtering out inputs and outputs that are deemed unsafe. See the llama-recipes repo for an example of how to add a safety checker to the inputs and outputs of your inference code.\n\n\nExamples using llama-2-7b-chat:\n\n\nLlama 2 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios.\nIn order to help developers address these risks, we have created the Responsible Use Guide. More details can be found in our research paper as well.\n\n\nIssues\n------\n\n\nPlease report any software “bug”, or other problems with the models through one of the following means:\n\n\n* Reporting issues with the model: URL\n* Reporting risky content generated by the model: URL\n* Reporting bugs and security concerns: URL\n\n\nModel Card\n----------\n\n\nSee MODEL\\_CARD.md.\n\n\nLicense\n-------\n\n\nOur model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.\n\n\nSee the LICENSE file, as well as our accompanying Acceptable Use Policy\n\n\nReferences\n----------\n\n\n1. Research Paper\n2. Llama 2 technical overview\n3. Open Innovation AI Research Community\n\n\nFor common questions, the FAQ can be found here which will be kept up to date over time as new questions arise.\n\n\nOriginal LLaMA\n--------------\n\n\nThe repo for the original llama release is in the 'llama\\_v1' branch." ]

[ 6, 507, 102, 463 ]

[ "passage: TAGS\n#region-us \n", "passage: ### Access on Hugging Face\n\n\nWe are also providing downloads on Hugging Face. You must first request a download from the Meta website using the same email address as your Hugging Face account. After doing so, you can request access to any of the models on Hugging Face and within 1-2 days your account will be granted access to all versions.\n\n\nQuick Start\n-----------\n\n\nYou can follow the steps below to quickly get up and running with Llama 2 models. These steps will let you run quick inference locally. For more examples, see the Llama 2 recipes repository.\n\n\n1. In a conda env with PyTorch / CUDA available clone and download this repository.\n2. In the top level directory run:\n3. Visit the Meta website and register to download the model/s.\n4. Once registered, you will get an email with a URL to download the models. You will need this URL when you run the URL script.\n5. Once you get the email, navigate to your downloaded llama repository and run the URL script.\n\n\n\t* Make sure to grant execution permissions to the URL script\n\t* During this process, you will be prompted to enter the URL from the email.\n\t* Do not use the “Copy Link” option but rather make sure to manually copy the link from the email.\n6. Once the model/s you want have been downloaded, you can run the model locally using the command below:\n\n\nNote\n\n\n* Replace 'llama-2-7b-chat/' with the path to your checkpoint directory and 'URL' with the path to your tokenizer model.\n* The '–nproc\\_per\\_node' should be set to the MP value for the model you are using.\n* Adjust the 'max\\_seq\\_len' and 'max\\_batch\\_size' parameters as needed.\n* This example runs the example\\_chat\\_completion.py found in this repository but you can change that to a different .py file.\n\n\nInference\n---------\n\n\nDifferent models require different model-parallel (MP) values:\n\n\n\nAll models support sequence length up to 4096 tokens, but we pre-allocate the cache according to 'max\\_seq\\_len' and 'max\\_batch\\_size' values. So set those according to your hardware.### Pretrained Models\n\n\nThese models are not finetuned for chat or Q&A. They should be prompted so that the expected answer is the natural continuation of the prompt.\n\n\nSee 'example\\_text\\_completion.py' for some examples. To illustrate, see the command below to run it with the llama-2-7b model ('nproc\\_per\\_node' needs to be set to the 'MP' value):" ]

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

[]

[ "en" ]

[ "# Dataset Card for Python Q/A pair\n\n\n\nThis dataset card provides information about the Python Q/A pair dataset.", "## Dataset Details", "### Dataset Description\n\nThe Python Q/A pair dataset is a preprocessed version of a Python Q/A dataset from StackOverflow, which was originally hosted on Kaggle. The dataset contains high-ranked questions and their corresponding high-ranked answers, sorted from high to low rank.\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use\n\nThis dataset can be used for tasks such as question answering, text generation, and conversational AI research and development.", "### Out-of-Scope Use\n\nThis dataset should not be used for tasks outside of natural language processing, such as image recognition or voice recognition.", "## Dataset Structure\n\nThe dataset contains 100k rows of high-ranked questions and their corresponding high-ranked answers from StackOverflow.", "## Dataset Creation", "### Curation Rationale\n\nThe dataset was curated to provide a resource for developing and testing natural language processing models, particularly in the domain of question answering and text generation.", "### Source Data\n\nThe data in this dataset comes from StackOverflow Q/A pairs that were ranked 1 or above. The raw form of this dataset is hosted on Kaggle.", "#### Data Collection and Processing\n\nThe data was collected from StackOverflow and preprocessed to include only high-ranked questions and their corresponding high-ranked answers.", "#### Who are the source data producers?\n\nThe source data was produced by users of StackOverflow.", "### Annotations [optional]\n\nThis dataset does not contain any additional annotations.", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information\n\nThe dataset does not contain any personal or sensitive information as it was derived from publicly available data on StackOverflow.", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#task_categories-question-answering #task_categories-text-generation #task_categories-conversational #size_categories-n<1K #language-English #license-apache-2.0 #python #region-us \n", "# Dataset Card for Python Q/A pair\n\n\n\nThis dataset card provides information about the Python Q/A pair dataset.", "## Dataset Details", "### Dataset Description\n\nThe Python Q/A pair dataset is a preprocessed version of a Python Q/A dataset from StackOverflow, which was originally hosted on Kaggle. The dataset contains high-ranked questions and their corresponding high-ranked answers, sorted from high to low rank.\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use\n\nThis dataset can be used for tasks such as question answering, text generation, and conversational AI research and development.", "### Out-of-Scope Use\n\nThis dataset should not be used for tasks outside of natural language processing, such as image recognition or voice recognition.", "## Dataset Structure\n\nThe dataset contains 100k rows of high-ranked questions and their corresponding high-ranked answers from StackOverflow.", "## Dataset Creation", "### Curation Rationale\n\nThe dataset was curated to provide a resource for developing and testing natural language processing models, particularly in the domain of question answering and text generation.", "### Source Data\n\nThe data in this dataset comes from StackOverflow Q/A pairs that were ranked 1 or above. The raw form of this dataset is hosted on Kaggle.", "#### Data Collection and Processing\n\nThe data was collected from StackOverflow and preprocessed to include only high-ranked questions and their corresponding high-ranked answers.", "#### Who are the source data producers?\n\nThe source data was produced by users of StackOverflow.", "### Annotations [optional]\n\nThis dataset does not contain any additional annotations.", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information\n\nThe dataset does not contain any personal or sensitive information as it was derived from publicly available data on StackOverflow.", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 64, 26, 4, 107, 29, 3, 30, 34, 38, 5, 39, 43, 40, 23, 21, 5, 9, 35, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#task_categories-question-answering #task_categories-text-generation #task_categories-conversational #size_categories-n<1K #language-English #license-apache-2.0 #python #region-us \n# Dataset Card for Python Q/A pair\n\n\n\nThis dataset card provides information about the Python Q/A pair dataset.## Dataset Details### Dataset Description\n\nThe Python Q/A pair dataset is a preprocessed version of a Python Q/A dataset from StackOverflow, which was originally hosted on Kaggle. The dataset contains high-ranked questions and their corresponding high-ranked answers, sorted from high to low rank.\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use\n\nThis dataset can be used for tasks such as question answering, text generation, and conversational AI research and development.### Out-of-Scope Use\n\nThis dataset should not be used for tasks outside of natural language processing, such as image recognition or voice recognition.## Dataset Structure\n\nThe dataset contains 100k rows of high-ranked questions and their corresponding high-ranked answers from StackOverflow.## Dataset Creation### Curation Rationale\n\nThe dataset was curated to provide a resource for developing and testing natural language processing models, particularly in the domain of question answering and text generation.### Source Data\n\nThe data in this dataset comes from StackOverflow Q/A pairs that were ranked 1 or above. The raw form of this dataset is hosted on Kaggle.#### Data Collection and Processing\n\nThe data was collected from StackOverflow and preprocessed to include only high-ranked questions and their corresponding high-ranked answers.#### Who are the source data producers?\n\nThe source data was produced by users of StackOverflow.### Annotations [optional]\n\nThis dataset does not contain any additional annotations." ]

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

[]

[]

[ "# License\n\nCC-0" ]

[ "TAGS\n#license-cc0-1.0 #ChatGPT #region-us \n", "# License\n\nCC-0" ]

[ 18, 4 ]

[ "passage: TAGS\n#license-cc0-1.0 #ChatGPT #region-us \n# License\n\nCC-0" ]

[ "deep-reinforcement-learning", "reinforcement-learning", "godot-rl", "environments", "video-games", "region:us" ]

[]

[]

[ "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n", "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ 32, 20 ]

[ "passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 12 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"sven\"\n\nMore Information needed" ]

[ "task_categories:feature-extraction", "task_categories:text-generation", "task_categories:translation", "task_categories:fill-mask", "task_categories:sentence-similarity", "size_categories:n>1T", "art", "climate", "region:us" ]

[]

[]

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

[ "TAGS\n#task_categories-feature-extraction #task_categories-text-generation #task_categories-translation #task_categories-fill-mask #task_categories-sentence-similarity #size_categories-n>1T #art #climate #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" ]

[ 78, 34, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#task_categories-feature-extraction #task_categories-text-generation #task_categories-translation #task_categories-fill-mask #task_categories-sentence-similarity #size_categories-n>1T #art #climate #region-us \n# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "task_categories:text-to-image", "size_categories:1M<n<10M", "language:en", "license:apache-2.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-to-image #size_categories-1M<n<10M #language-English #license-apache-2.0 #region-us \n" ]

[ 42 ]

[ "passage: TAGS\n#task_categories-text-to-image #size_categories-1M<n<10M #language-English #license-apache-2.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of xaviviro/FLOR-6.3B-xat\n\n\n\nDataset automatically created during the evaluation run of model xaviviro/FLOR-6.3B-xat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T11:25:36.448416(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 xaviviro/FLOR-6.3B-xat\n\n\n\nDataset automatically created during the evaluation run of model xaviviro/FLOR-6.3B-xat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T11:25:36.448416(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 185, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of xaviviro/FLOR-6.3B-xat\n\n\n\nDataset automatically created during the evaluation run of model xaviviro/FLOR-6.3B-xat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-07T11:25:36.448416(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 \"araproje_arc_tr_conf_mgpt_nearestscore_true_y\"\n\nMore Information needed" ]

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

[ 6, 34 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_arc_tr_conf_mgpt_nearestscore_true_y\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 32 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_arc_tr_conf_mgpt_nearestscore_true\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 34 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_arc_en_conf_llama_nearestscore_true_y\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 32 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_arc_en_conf_llama_nearestscore_true\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 34 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_arc_en_conf_llama_nearestscore_true_x\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 34 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_arc_tr_conf_mgpt_nearestscore_true_x\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 34 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_mmlu_tr_conf_mgpt_nearestscore_true_y\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 34 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_mmlu_tr_conf_mgpt_nearestscore_true_x\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 32 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_mmlu_tr_conf_mgpt_nearestscore_true\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 34 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_mmlu_en_conf_llama_nearestscore_true_y\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 34 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_mmlu_en_conf_llama_nearestscore_true_x\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

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

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

[ 6, 32 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"araproje_mmlu_en_conf_llama_nearestscore_true\"\n\nMore Information needed" ]

[ "size_categories:n<1K", "region:us" ]

[]

[]

[]

[ "TAGS\n#size_categories-n<1K #region-us \n" ]

[ 16 ]

[ "passage: TAGS\n#size_categories-n<1K #region-us \n" ]

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

[]

[ "zh" ]

[ "# 2000 Chinese RoleCards from IMDB_250 Movies and PIPPA\n\n用于拓展zero-shot角色扮演的角色卡片。\n\n其中870个角色来自电影字幕总结(id为movie_xx)，其中406张翻译成了简体中文，剩下的没翻（所以有些繁体或者英文混杂）\n\n1270个角色来自于对PIPPA数据集的翻译\n\n- 凌云志@伯恩茅斯大学 使用射手api爬取了电影的字幕\n\n- 李鲁鲁 完成了从字幕到角色卡片的总结，以及对数据的翻译(openai)", "# 后续\n\n我们后续打算用这些卡片 从openai, CharacterGLM, KoboldAI的api中，利用Baize的方式去获得数据。\n\n项目主页 URL\n\n\n如果你要讨论加入我们的项目\n\n可以把你的联系方式私信发给 URL" ]

[ "TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-Chinese #license-apache-2.0 #region-us \n", "# 2000 Chinese RoleCards from IMDB_250 Movies and PIPPA\n\n用于拓展zero-shot角色扮演的角色卡片。\n\n其中870个角色来自电影字幕总结(id为movie_xx)，其中406张翻译成了简体中文，剩下的没翻（所以有些繁体或者英文混杂）\n\n1270个角色来自于对PIPPA数据集的翻译\n\n- 凌云志@伯恩茅斯大学 使用射手api爬取了电影的字幕\n\n- 李鲁鲁 完成了从字幕到角色卡片的总结，以及对数据的翻译(openai)", "# 后续\n\n我们后续打算用这些卡片 从openai, CharacterGLM, KoboldAI的api中，利用Baize的方式去获得数据。\n\n项目主页 URL\n\n\n如果你要讨论加入我们的项目\n\n可以把你的联系方式私信发给 URL" ]

[ 42, 124, 57 ]

[ "passage: TAGS\n#task_categories-text-generation #size_categories-1K<n<10K #language-Chinese #license-apache-2.0 #region-us \n# 2000 Chinese RoleCards from IMDB_250 Movies and PIPPA\n\n用于拓展zero-shot角色扮演的角色卡片。\n\n其中870个角色来自电影字幕总结(id为movie_xx)，其中406张翻译成了简体中文，剩下的没翻（所以有些繁体或者英文混杂）\n\n1270个角色来自于对PIPPA数据集的翻译\n\n- 凌云志@伯恩茅斯大学 使用射手api爬取了电影的字幕\n\n- 李鲁鲁 完成了从字幕到角色卡片的总结，以及对数据的翻译(openai)# 后续\n\n我们后续打算用这些卡片 从openai, CharacterGLM, KoboldAI的api中，利用Baize的方式去获得数据。\n\n项目主页 URL\n\n\n如果你要讨论加入我们的项目\n\n可以把你的联系方式私信发给 URL" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "multilinguality:multilingual", "language:de", "language:en", "language:es", "language:fr", "language:it", "language:nl", "language:pl", "language:pt", "language:ru", "language:ar", "language:id", "license:cc-by-sa-4.0", "region:us" ]

[]

[ "de", "en", "es", "fr", "it", "nl", "pl", "pt", "ru", "ar", "id" ]

[]

[ "TAGS\n#multilinguality-multilingual #language-German #language-English #language-Spanish #language-French #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Russian #language-Arabic #language-Indonesian #license-cc-by-sa-4.0 #region-us \n" ]

[ 81 ]

[ "passage: TAGS\n#multilinguality-multilingual #language-German #language-English #language-Spanish #language-French #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Russian #language-Arabic #language-Indonesian #license-cc-by-sa-4.0 #region-us \n" ]

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

[]

[]

[ "## Description\n\n<b>rawrr_v1</b> is highly-experimental pairs style dataset that was created to help with de-contamination of so-called \"base\" models. \\\nField 'chosen' contains outputs from base models that weren't instruct tuned and were released directly after pre-training, in a raw format. Some of those outputs are just completions of a prompt, while some are answers to the prompt. \\\nField 'rejected' contains outputs from models that were contaminated before public release. \n\nTo my knowledge, this dataset doesn't contain any toxic, hateful content. \\\nTo my knowledge, this dataset doesn't contain any content that could be deemed illegal in totalitarian countries, but I don't know every piece of law, so it's best if you still exercise proper caution when dealing with malicious regime. \\\nTo my knowledge, all of the prompts in no_robots dataset are pretty benign.\n\nMix of publicly available models was used for creation of this dataset.\n\nMore and more base models nowadays aren't released straight after pre-training the model. Instead, model authors sneak in additional instruct fine-tuning and release only that fine-tuned model, calling it a base model. \\\nMy aim is to try to reverse that process so that researchers and community can posses models resembling the raw model that are primarily aimed at completion instead of instruct. \\\nOf course, my attempts are not very sophisticated since I am using just my private PC for the dataset generation, so I can't create complex multi-GB synthetic datasets in reasonable time-frames, but I think running DPO with this dataset could still help with this issue.\n\nThis dataset is based on HuggingFaceH4/no_robots and winglian/no_robots_rlhf \\\nFields 'prompt', 'source' and 'id' have been kept from base datasets, 'chosen' and 'rejected' fields have been replaced used synthetic output.\nField 'system' has been overwritten with \"A chat.\"\nOriginal dataset released with cc-by-nc-4.0 dataset, so I am keeping it this way.\n\nI used following generation parameters\n\n\n\n\n\n\n ## Issues in v1\n\n To keep the generation time reasonable, I set max_tokens in output to 600. Because of this, some generations in field 'chosen' are cut off mid-sentence. I will see whether it's an issue after doing DPO and maybe try to make the max_tokens limit longer for my next attempt or remove those broken replies from this version. \\\n Also, many responses in 'rejected' field start from \"I \". Will this be an issue later down the road and will model be unable to respond with first-person view after fine-tuning? Maybe, I don't know. \\\n no_robots is a dataset with relatively non-permissive cc-by-nc-4.0 license. If you know any ethically sourced permissive human-made dataset that I could use for next version - let me know! \\\n I thinked about using OpenAssistant dataset for this, but it's file structure is a mess I didn't want to dive into." ]

[ "TAGS\n#license-cc-by-nc-4.0 #region-us \n", "## Description\n\n<b>rawrr_v1</b> is highly-experimental pairs style dataset that was created to help with de-contamination of so-called \"base\" models. \\\nField 'chosen' contains outputs from base models that weren't instruct tuned and were released directly after pre-training, in a raw format. Some of those outputs are just completions of a prompt, while some are answers to the prompt. \\\nField 'rejected' contains outputs from models that were contaminated before public release. \n\nTo my knowledge, this dataset doesn't contain any toxic, hateful content. \\\nTo my knowledge, this dataset doesn't contain any content that could be deemed illegal in totalitarian countries, but I don't know every piece of law, so it's best if you still exercise proper caution when dealing with malicious regime. \\\nTo my knowledge, all of the prompts in no_robots dataset are pretty benign.\n\nMix of publicly available models was used for creation of this dataset.\n\nMore and more base models nowadays aren't released straight after pre-training the model. Instead, model authors sneak in additional instruct fine-tuning and release only that fine-tuned model, calling it a base model. \\\nMy aim is to try to reverse that process so that researchers and community can posses models resembling the raw model that are primarily aimed at completion instead of instruct. \\\nOf course, my attempts are not very sophisticated since I am using just my private PC for the dataset generation, so I can't create complex multi-GB synthetic datasets in reasonable time-frames, but I think running DPO with this dataset could still help with this issue.\n\nThis dataset is based on HuggingFaceH4/no_robots and winglian/no_robots_rlhf \\\nFields 'prompt', 'source' and 'id' have been kept from base datasets, 'chosen' and 'rejected' fields have been replaced used synthetic output.\nField 'system' has been overwritten with \"A chat.\"\nOriginal dataset released with cc-by-nc-4.0 dataset, so I am keeping it this way.\n\nI used following generation parameters\n\n\n\n\n\n\n ## Issues in v1\n\n To keep the generation time reasonable, I set max_tokens in output to 600. Because of this, some generations in field 'chosen' are cut off mid-sentence. I will see whether it's an issue after doing DPO and maybe try to make the max_tokens limit longer for my next attempt or remove those broken replies from this version. \\\n Also, many responses in 'rejected' field start from \"I \". Will this be an issue later down the road and will model be unable to respond with first-person view after fine-tuning? Maybe, I don't know. \\\n no_robots is a dataset with relatively non-permissive cc-by-nc-4.0 license. If you know any ethically sourced permissive human-made dataset that I could use for next version - let me know! \\\n I thinked about using OpenAssistant dataset for this, but it's file structure is a mess I didn't want to dive into." ]

[ 17, 750 ]

[ "passage: TAGS\n#license-cc-by-nc-4.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of AA051610/A0106\n\n\n\nDataset automatically created during the evaluation run of model AA051610/A0106 on the Open LLM Leaderboard.\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 2024-01-07T23:07:28.080056(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 AA051610/A0106\n\n\n\nDataset automatically created during the evaluation run of model AA051610/A0106 on the Open LLM Leaderboard.\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 2024-01-07T23:07:28.080056(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 177, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AA051610/A0106\n\n\n\nDataset automatically created during the evaluation run of model AA051610/A0106 on the Open LLM Leaderboard.\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 2024-01-07T23:07:28.080056(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "task_categories:conversational", "task_categories:text-generation", "language:tr", "license:cc-by-nc-4.0", "arxiv:2203.02155", "region:us" ]

[ "2203.02155" ]

[ "tr" ]

[ "### Özet\n\n\nNo Robots 10000 komut ve gösterimden oluşan, profesyonel etiketleyiciler tarafından oluşturulmuş bir verisetidir. Çevirisi Google Cloud Platform Translation API ile yapıldı. Bu veriset LLM'lere komut takibi öğretmek için kullanılabilir. (Instruction Supervised Fine-tuning - SFT)\nNo Robots veriseti OpenAI'ın InstructGPT makalesinden esinlenerek oluşturulmuştur ve aşağıdaki kategorilere sahiptir:", "### Diller\n\n\nBu verisetinde sadece Türkçe var.\n\n\nVeriseti Yapısı\n---------------\n\n\nBu verisetini CSV olarak yükledim. Örneklerin neye benzediğini görmek istiyorsanız widget'a bakın.", "### Veri Alanları\n\n\nKolonlar aşağıdaki gibidir:\n\n\n* 'prompt': Modelin takip etmesi gereken komutu belirler.\n* 'prompt\\_id': Unique identifier.\n* 'messages': Dictionary'ler içeren liste, her dictionary bir mesajı (key: content) ve o mesajı kimin gönderdiğini (key: role) açıklar.\n* 'category': Görevin kategorisi, bunu çevirmedim.", "### Split'ler", "### Lisans\n\n\nBu veriseti ne yazık ki açık kaynak değil açık erişimli. Lisansı Creative Commons NonCommercial (CC BY-NC 4.0).\nEğer verisetinin kendisi açık kaynak olursa bu veriseti de açık kaynak olacaktır, çünkü çevirisini çeviriler üstünde fikri mülkiyet istemeyen GCP tarafından yaptım." ]

[ "TAGS\n#task_categories-conversational #task_categories-text-generation #language-Turkish #license-cc-by-nc-4.0 #arxiv-2203.02155 #region-us \n", "### Özet\n\n\nNo Robots 10000 komut ve gösterimden oluşan, profesyonel etiketleyiciler tarafından oluşturulmuş bir verisetidir. Çevirisi Google Cloud Platform Translation API ile yapıldı. Bu veriset LLM'lere komut takibi öğretmek için kullanılabilir. (Instruction Supervised Fine-tuning - SFT)\nNo Robots veriseti OpenAI'ın InstructGPT makalesinden esinlenerek oluşturulmuştur ve aşağıdaki kategorilere sahiptir:", "### Diller\n\n\nBu verisetinde sadece Türkçe var.\n\n\nVeriseti Yapısı\n---------------\n\n\nBu verisetini CSV olarak yükledim. Örneklerin neye benzediğini görmek istiyorsanız widget'a bakın.", "### Veri Alanları\n\n\nKolonlar aşağıdaki gibidir:\n\n\n* 'prompt': Modelin takip etmesi gereken komutu belirler.\n* 'prompt\\_id': Unique identifier.\n* 'messages': Dictionary'ler içeren liste, her dictionary bir mesajı (key: content) ve o mesajı kimin gönderdiğini (key: role) açıklar.\n* 'category': Görevin kategorisi, bunu çevirmedim.", "### Split'ler", "### Lisans\n\n\nBu veriseti ne yazık ki açık kaynak değil açık erişimli. Lisansı Creative Commons NonCommercial (CC BY-NC 4.0).\nEğer verisetinin kendisi açık kaynak olursa bu veriseti de açık kaynak olacaktır, çünkü çevirisini çeviriler üstünde fikri mülkiyet istemeyen GCP tarafından yaptım." ]

[ 52, 99, 46, 102, 5, 68 ]

[ "passage: TAGS\n#task_categories-conversational #task_categories-text-generation #language-Turkish #license-cc-by-nc-4.0 #arxiv-2203.02155 #region-us \n### Özet\n\n\nNo Robots 10000 komut ve gösterimden oluşan, profesyonel etiketleyiciler tarafından oluşturulmuş bir verisetidir. Çevirisi Google Cloud Platform Translation API ile yapıldı. Bu veriset LLM'lere komut takibi öğretmek için kullanılabilir. (Instruction Supervised Fine-tuning - SFT)\nNo Robots veriseti OpenAI'ın InstructGPT makalesinden esinlenerek oluşturulmuştur ve aşağıdaki kategorilere sahiptir:### Diller\n\n\nBu verisetinde sadece Türkçe var.\n\n\nVeriseti Yapısı\n---------------\n\n\nBu verisetini CSV olarak yükledim. Örneklerin neye benzediğini görmek istiyorsanız widget'a bakın.### Veri Alanları\n\n\nKolonlar aşağıdaki gibidir:\n\n\n* 'prompt': Modelin takip etmesi gereken komutu belirler.\n* 'prompt\\_id': Unique identifier.\n* 'messages': Dictionary'ler içeren liste, her dictionary bir mesajı (key: content) ve o mesajı kimin gönderdiğini (key: role) açıklar.\n* 'category': Görevin kategorisi, bunu çevirmedim.### Split'ler### Lisans\n\n\nBu veriseti ne yazık ki açık kaynak değil açık erişimli. Lisansı Creative Commons NonCommercial (CC BY-NC 4.0).\nEğer verisetinin kendisi açık kaynak olursa bu veriseti de açık kaynak olacaktır, çünkü çevirisini çeviriler üstünde fikri mülkiyet istemeyen GCP tarafından yaptım." ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "language:zh", "license:apache-2.0", "not-for-all-audiences", "region:us" ]

[]

[ "zh" ]

[]

[ "TAGS\n#language-Chinese #license-apache-2.0 #not-for-all-audiences #region-us \n" ]

[ 28 ]

[ "passage: TAGS\n#language-Chinese #license-apache-2.0 #not-for-all-audiences #region-us \n" ]

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

[]

[ "en" ]

[ "# Dataset Card for \"NWGI_test\"", "## Dataset Class:" ]

[ "TAGS\n#language-English #license-mit #region-us \n", "# Dataset Card for \"NWGI_test\"", "## Dataset Class:" ]

[ 15, 11, 5 ]

[ "passage: TAGS\n#language-English #license-mit #region-us \n# Dataset Card for \"NWGI_test\"## Dataset Class:" ]

[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:ru", "license:apache-2.0", "region:us" ]

[]

[ "ru" ]

[]

[ "TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Russian #license-apache-2.0 #region-us \n" ]

[ 42 ]

[ "passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-Russian #license-apache-2.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:open-domain-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pl", "license:cc-by-4.0", "region:us" ]

[]

[ "pl" ]

[]

[ "TAGS\n#task_categories-question-answering #task_ids-extractive-qa #task_ids-open-domain-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Polish #license-cc-by-4.0 #region-us \n" ]

[ 104 ]

[ "passage: TAGS\n#task_categories-question-answering #task_ids-extractive-qa #task_ids-open-domain-qa #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Polish #license-cc-by-4.0 #region-us \n" ]

[ "language:en", "license:apache-2.0", "rlaif", "dpo", "rlhf", "distilabel", "synthetic", "region:us" ]

[]

[ "en" ]

[ "# distilabel Orca Pairs for DPO\n\nThe dataset is a \"distilabeled\" version of the widely used dataset: Intel/orca_dpo_pairs. The original dataset has been used by 100s of open-source practitioners and models. We knew from fixing UltraFeedback (and before that, Alpacas and Dollys) that this dataset could be highly improved.\n\nContinuing with our mission to build the best alignment datasets for open-source LLMs and the community, we spent a few hours improving it with distilabel. \n\nThis was our main intuition: the original dataset just assumes gpt4/3.5-turbo are always the best response. We know from UltraFeedback that's not always the case. Moreover, DPO fine-tuning benefits from the diversity of preference pairs. \n\nAdditionally, we have added a new column indicating whether the question in the dataset is part of the train set of gsm8k (there were no examples from the test set). See the reproduction section for more details.", "## Using this dataset\nThis dataset is useful for preference tuning and we recommend using it instead of the original. It's already prepared in the \"standard\" chosen, rejected format with additional information for further filtering and experimentation. \n\nThe main changes are:\n\n1. ~2K pairs have been swapped: rejected become the chosen response. We have kept the original chosen and rejected on two new columns 'original_*' for reproducibility purposes.\n2. 4K pairs have been identified as 'tie': equally bad or good.\n3. Chosen scores have been added: you can now filter out based on a threshold (see our distilabeled Hermes 2.5 model for an example)\n4. We have kept the ratings and rationales generated with gpt-4-turbo and distilabel so you can prepare the data differently if you want.\n5. We have added a column to indicate if the input is part of gsm8k train set.\n\nIn our experiments, we have got very good results by reducing the size of the dataset by more than 50%. Here's an example of how to achieve that:\n\n\nThis results in '5,922' instead of '12,859' samples (54% reduction) and leads to better performance than the same model tuned with 100% of the samples in the original dataset.\n\n> We'd love to hear about your experiments! If you want to try this out, consider joining our Slack community and let's build some open datasets and models together.", "## Reproducing the dataset\nIn this section, we outline the steps to reproduce this dataset.", "### Rate original dataset pairs\n\nBuild a preference dataset with distilabel using the original dataset:\n\n\n\nIf you want to further filter and curate the dataset, you can push the dataset to Argilla as follows:\n\n\nYou get a nice UI with a lot of pre-computed metadata to explore and curate the dataset:\n\n!image/png\n\nThe resulting dataset is now much more useful: we know which response is preferred (by gpt-4-turbo), which ones have low scores, and we even have natural language explanations. But what did we find? Was our intuition confirmed?\n\n!image/png\n\nThe above chart shows the following: \n\n* ~4,000 pairs were given the same rating (a tie).\n* ~7,000 pairs were correct according to our AI judge ('unchanged').\n* and ~2,000 times the rejected response was preferred ('swapped').\n\n\n\nNow the next question is: can we build better models with this new knowledge? The answer is the \"distilabeled Hermes\" model, check it out!", "### Post-processing to add useful information\n\n\nSwap rejected and chosen, and add chosen scores and status:", "### gsm8k \"decontamination\"\nThe basic approach for finding duplicated examples. We didn't find any from the test sets. We experimented with lower thresholds but below 0.8 they introduced false positives:\n\nResult:" ]

[ "TAGS\n#language-English #license-apache-2.0 #rlaif #dpo #rlhf #distilabel #synthetic #region-us \n", "# distilabel Orca Pairs for DPO\n\nThe dataset is a \"distilabeled\" version of the widely used dataset: Intel/orca_dpo_pairs. The original dataset has been used by 100s of open-source practitioners and models. We knew from fixing UltraFeedback (and before that, Alpacas and Dollys) that this dataset could be highly improved.\n\nContinuing with our mission to build the best alignment datasets for open-source LLMs and the community, we spent a few hours improving it with distilabel. \n\nThis was our main intuition: the original dataset just assumes gpt4/3.5-turbo are always the best response. We know from UltraFeedback that's not always the case. Moreover, DPO fine-tuning benefits from the diversity of preference pairs. \n\nAdditionally, we have added a new column indicating whether the question in the dataset is part of the train set of gsm8k (there were no examples from the test set). See the reproduction section for more details.", "## Using this dataset\nThis dataset is useful for preference tuning and we recommend using it instead of the original. It's already prepared in the \"standard\" chosen, rejected format with additional information for further filtering and experimentation. \n\nThe main changes are:\n\n1. ~2K pairs have been swapped: rejected become the chosen response. We have kept the original chosen and rejected on two new columns 'original_*' for reproducibility purposes.\n2. 4K pairs have been identified as 'tie': equally bad or good.\n3. Chosen scores have been added: you can now filter out based on a threshold (see our distilabeled Hermes 2.5 model for an example)\n4. We have kept the ratings and rationales generated with gpt-4-turbo and distilabel so you can prepare the data differently if you want.\n5. We have added a column to indicate if the input is part of gsm8k train set.\n\nIn our experiments, we have got very good results by reducing the size of the dataset by more than 50%. Here's an example of how to achieve that:\n\n\nThis results in '5,922' instead of '12,859' samples (54% reduction) and leads to better performance than the same model tuned with 100% of the samples in the original dataset.\n\n> We'd love to hear about your experiments! If you want to try this out, consider joining our Slack community and let's build some open datasets and models together.", "## Reproducing the dataset\nIn this section, we outline the steps to reproduce this dataset.", "### Rate original dataset pairs\n\nBuild a preference dataset with distilabel using the original dataset:\n\n\n\nIf you want to further filter and curate the dataset, you can push the dataset to Argilla as follows:\n\n\nYou get a nice UI with a lot of pre-computed metadata to explore and curate the dataset:\n\n!image/png\n\nThe resulting dataset is now much more useful: we know which response is preferred (by gpt-4-turbo), which ones have low scores, and we even have natural language explanations. But what did we find? Was our intuition confirmed?\n\n!image/png\n\nThe above chart shows the following: \n\n* ~4,000 pairs were given the same rating (a tie).\n* ~7,000 pairs were correct according to our AI judge ('unchanged').\n* and ~2,000 times the rejected response was preferred ('swapped').\n\n\n\nNow the next question is: can we build better models with this new knowledge? The answer is the \"distilabeled Hermes\" model, check it out!", "### Post-processing to add useful information\n\n\nSwap rejected and chosen, and add chosen scores and status:", "### gsm8k \"decontamination\"\nThe basic approach for finding duplicated examples. We didn't find any from the test sets. We experimented with lower thresholds but below 0.8 they introduced false positives:\n\nResult:" ]

[ 36, 249, 342, 22, 233, 28, 56 ]

[ "passage: TAGS\n#language-English #license-apache-2.0 #rlaif #dpo #rlhf #distilabel #synthetic #region-us \n# distilabel Orca Pairs for DPO\n\nThe dataset is a \"distilabeled\" version of the widely used dataset: Intel/orca_dpo_pairs. The original dataset has been used by 100s of open-source practitioners and models. We knew from fixing UltraFeedback (and before that, Alpacas and Dollys) that this dataset could be highly improved.\n\nContinuing with our mission to build the best alignment datasets for open-source LLMs and the community, we spent a few hours improving it with distilabel. \n\nThis was our main intuition: the original dataset just assumes gpt4/3.5-turbo are always the best response. We know from UltraFeedback that's not always the case. Moreover, DPO fine-tuning benefits from the diversity of preference pairs. \n\nAdditionally, we have added a new column indicating whether the question in the dataset is part of the train set of gsm8k (there were no examples from the test set). See the reproduction section for more details." ]

[ "deep-reinforcement-learning", "reinforcement-learning", "godot-rl", "environments", "video-games", "region:us" ]

[]

[]

[ "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n", "## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ 32, 20 ]

[ "passage: TAGS\n#deep-reinforcement-learning #reinforcement-learning #godot-rl #environments #video-games #region-us \n## Downloading the environment \n\nAfter installing Godot RL Agents, download the environment with:" ]

[ "source_datasets:deepset/germanquad", "language:de", "license:cc-by-4.0", "arxiv:2104.12741", "region:us" ]

[ "2104.12741" ]

[ "de" ]

[]

[ "TAGS\n#source_datasets-deepset/germanquad #language-German #license-cc-by-4.0 #arxiv-2104.12741 #region-us \n" ]

[ 40 ]

[ "passage: TAGS\n#source_datasets-deepset/germanquad #language-German #license-cc-by-4.0 #arxiv-2104.12741 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"multi-pooled\"\n\nMore Information needed" ]

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

[ 6, 14 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"multi-pooled\"\n\nMore Information needed" ]

[ "size_categories:10K<n<100K", "rlfh", "argilla", "human-feedback", "region:us" ]

[]

[]

[ "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ input is of type 'text'.\n\t+ generations-1 is of type 'text'.\n\t+ generations-2 is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ generations-1-rating is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].\n\t+ generations-2-rating is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].\n\t+ rating-rationale is of type 'text'.\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) generations-1-rating-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].\n\t+ (optional) generations-2-rating-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].\n\t+ (optional) rating-rationale-suggestion is of type 'text'.\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ "TAGS\n#size_categories-10K<n<100K #rlfh #argilla #human-feedback #region-us \n", "### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.", "### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:", "### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:", "### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.", "### Languages\n\n\nDataset Structure\n-----------------", "### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.", "### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:", "### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ input is of type 'text'.\n\t+ generations-1 is of type 'text'.\n\t+ generations-2 is of type 'text'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ generations-1-rating is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].\n\t+ generations-2-rating is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].\n\t+ rating-rationale is of type 'text'.\n* Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) generations-1-rating-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].\n\t+ (optional) generations-2-rating-suggestion is of type 'rating' with the following allowed values [1, 2, 3, 4, 5, 6, 7, 8, 9, 10].\n\t+ (optional) rating-rationale-suggestion is of type 'text'.\n\n\nAdditionally, we also have two more fields that are optional and are the following:\n\n\n* metadata: This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.", "### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------", "### Curation Rationale", "### Source Data", "#### Initial Data Collection and Normalization", "#### Who are the source language producers?", "### Annotations", "#### Annotation guidelines", "#### Annotation process", "#### Who are the annotators?", "### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------", "### Social Impact of Dataset", "### Discussion of Biases", "### Other Known Limitations\n\n\nAdditional Information\n----------------------", "### Dataset Curators", "### Licensing Information", "### Contributions" ]

[ 29, 162, 40, 53, 68, 11, 404, 40, 632, 27, 7, 4, 10, 10, 5, 5, 5, 9, 18, 7, 8, 14, 6, 6, 5 ]

[ "passage: TAGS\n#size_categories-10K<n<100K #rlfh #argilla #human-feedback #region-us \n### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.### Languages\n\n\nDataset Structure\n-----------------", "passage: ### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, metadata, vectors, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label\\_selection, multi\\_label\\_selection, or ranking.\n\n\n\nThe suggestions are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with \"-suggestion\" and the metadata is appended with \"-suggestion-metadata\".\n\n\nThe metadata is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the 'metadata\\_properties' defined in the dataset configuration file in 'URL'.\n\n\n\nThe guidelines, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:" ]

[ "region:us" ]

[]

[]

[ "# OSCAR EU 6x3M Dataset", "## Overview\nThe OSCAR EU 6x3M dataset is a carefully curated subset of the larger OSCAR corpus, specifically focusing on the main European languages. This dataset includes a balanced representation of six languages: English (en), German (de), Spanish (es), Italian (it), French (fr), and Russian (ru). The \"6x3M\" in the name signifies that each language is represented with approximately 3 million randomly sampled documents, providing a comprehensive and diverse linguistic resource.", "## Dataset Description\n- Languages Included: English, German, Spanish, Italian, French, Russian\n- Number of Documents: Approximately 18 million (3 million per language)\n- Data Source: The dataset is derived from the OSCAR corpus, a large multilingual corpus created from the Common Crawl.", "## Use Cases\nThis dataset is ideal for a variety of natural language processing applications, including but not limited to:\n- Multilingual language modeling\n- Cross-linguistic transfer learning\n- Language identification and classification\n- Comparative linguistic studies", "## Accessing the Dataset\nThe dataset is available through the HuggingFace Datasets library. You can load the dataset using the following code snippet:\n'''python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"oscar_eu_6x3M\")" ]

[ "TAGS\n#region-us \n", "# OSCAR EU 6x3M Dataset", "## Overview\nThe OSCAR EU 6x3M dataset is a carefully curated subset of the larger OSCAR corpus, specifically focusing on the main European languages. This dataset includes a balanced representation of six languages: English (en), German (de), Spanish (es), Italian (it), French (fr), and Russian (ru). The \"6x3M\" in the name signifies that each language is represented with approximately 3 million randomly sampled documents, providing a comprehensive and diverse linguistic resource.", "## Dataset Description\n- Languages Included: English, German, Spanish, Italian, French, Russian\n- Number of Documents: Approximately 18 million (3 million per language)\n- Data Source: The dataset is derived from the OSCAR corpus, a large multilingual corpus created from the Common Crawl.", "## Use Cases\nThis dataset is ideal for a variety of natural language processing applications, including but not limited to:\n- Multilingual language modeling\n- Cross-linguistic transfer learning\n- Language identification and classification\n- Comparative linguistic studies", "## Accessing the Dataset\nThe dataset is available through the HuggingFace Datasets library. You can load the dataset using the following code snippet:\n'''python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"oscar_eu_6x3M\")" ]

[ 6, 10, 113, 68, 52, 69 ]

[ "passage: TAGS\n#region-us \n# OSCAR EU 6x3M Dataset## Overview\nThe OSCAR EU 6x3M dataset is a carefully curated subset of the larger OSCAR corpus, specifically focusing on the main European languages. This dataset includes a balanced representation of six languages: English (en), German (de), Spanish (es), Italian (it), French (fr), and Russian (ru). The \"6x3M\" in the name signifies that each language is represented with approximately 3 million randomly sampled documents, providing a comprehensive and diverse linguistic resource.## Dataset Description\n- Languages Included: English, German, Spanish, Italian, French, Russian\n- Number of Documents: Approximately 18 million (3 million per language)\n- Data Source: The dataset is derived from the OSCAR corpus, a large multilingual corpus created from the Common Crawl.## Use Cases\nThis dataset is ideal for a variety of natural language processing applications, including but not limited to:\n- Multilingual language modeling\n- Cross-linguistic transfer learning\n- Language identification and classification\n- Comparative linguistic studies## Accessing the Dataset\nThe dataset is available through the HuggingFace Datasets library. You can load the dataset using the following code snippet:\n'''python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"oscar_eu_6x3M\")" ]

[ "task_categories:sentence-similarity", "size_categories:100K<n<1M", "source_datasets:BeIR", "language:en", "license:apache-2.0", "text", "region:us" ]

[]

[ "en" ]

[ "# Big Hard Negatives Dataset\n\nA dataset for training embedding models for semantic search.\n\nTODO: add desc\n\nA dataset in a nixietune compatible format:", "## Usage\n\nTo use with HF datasets:", "## License\n\nApache 2.0" ]

[ "TAGS\n#task_categories-sentence-similarity #size_categories-100K<n<1M #source_datasets-BeIR #language-English #license-apache-2.0 #text #region-us \n", "# Big Hard Negatives Dataset\n\nA dataset for training embedding models for semantic search.\n\nTODO: add desc\n\nA dataset in a nixietune compatible format:", "## Usage\n\nTo use with HF datasets:", "## License\n\nApache 2.0" ]

[ 54, 38, 12, 5 ]

[ "passage: TAGS\n#task_categories-sentence-similarity #size_categories-100K<n<1M #source_datasets-BeIR #language-English #license-apache-2.0 #text #region-us \n# Big Hard Negatives Dataset\n\nA dataset for training embedding models for semantic search.\n\nTODO: add desc\n\nA dataset in a nixietune compatible format:## Usage\n\nTo use with HF datasets:## License\n\nApache 2.0" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of Locutusque/TinyMistral-248M-v2\n\n\n\nDataset automatically created during the evaluation run of model Locutusque/TinyMistral-248M-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T20:59:32.750418(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of Locutusque/TinyMistral-248M-v2\n\n\n\nDataset automatically created during the evaluation run of model Locutusque/TinyMistral-248M-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T20:59:32.750418(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 189, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Locutusque/TinyMistral-248M-v2\n\n\n\nDataset automatically created during the evaluation run of model Locutusque/TinyMistral-248M-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-07T20:59:32.750418(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]" ]

[ "task_categories:question-answering", "size_categories:n<1K", "language:en", "license:mit", "rejection-sampling", "phi-2", "question-answering", "region:us" ]

[]

[ "en" ]

[ "# Phi-2 Rejection Sampling\n\nThe Phi-2 Rejection Sampling dataset is an English-language dataset consisting of 10 prompts and responses generated by Phi-2 and graded by the OpenAssistant's reward model.", "## Dataset Details", "### Dataset Description\n\nThe Phi-2 Rejection Sampling dataset is a small (n = 10) English-language dataset. This dataset was created with the purpose was to demonstrate a feedback pipeline where in which Phi-2 would interact with the OpenAssistant reward model to discover \"good\" responses to given prompts. For more information about how this dataset is intended to be used and how it was created, please refer to their respective sections.\n\n- Curated by: Tanush Chopra\n- Language(s) (NLP): English\n- License: MIT License", "## Uses\n\nThis is intended to be used to fine-tuning and improving Phi-2's responses.", "### Direct Use\n\nAs stated above, this is intended to be used to fine-tune Phi-2 and improve its responses for the use cases captured by the selected prompts.", "### Out-of-Scope Use\n\nWhile this could potentially be used for other LLMs, caution should be observed as each LLM is different and has different strengths and weaknesses. Given that, if you were to use the dataset to finetune other LLMs you may observe unintended behavior and negative impacts on the responses post-finetuning.", "## Dataset Structure\n\nThere are only 2 fields to observe in this dataset: prompt and response\n- prompt: string\n- response: string\n\nThey are in a .tsv format, meaning you can expect them structured as such:\n\nPROMPT\\tRESPONSE \\\n{PROMPT_NUMBER_1}\\t{RESPONSE_NUMBER_1} \\\n... \\\n{PROMPT_NUMBER_10}\\t{RESPONSE_NUMBER_10}\n\nYou can find the dataset under URL.", "## Dataset Creation", "### Curation Rationale\n\nThe dataset was curated with the intent of compiling a set of prompt-response pairs that could then be used to fine-tune Phi-2, improving its responses to similar such prompts and styles of prompts.", "### Source Data\n\n\n\nPrompts were curated by Tanush Chopra by looking over various prompts, styles, and domains used in datasets (e.g. MMLU, HellaSwag), various papers on ArXiv on prompting styles (e.g. Chain of Thought) and prompt repositories (e.g. FlowGPT, repo).\\\nResponses were generated and evaluated using Phi-2 and OpenAssistant's reward model.", "#### Data Collection and Processing\n\n\n\nPrompts were curated with 3 main areas in mind:\n- Domain Usage (Arithmetic, Role-Playing, Commonsense, Conversation, Task/Planning)\n- Prompting Style (Chain Of Thought, ELI5)\n- Alignment (Hallucination, Bias and Toxicity, Power-Seeking, Moderation)\n\nEach of these areas were kept in mind with the crafting of each prompt in order to ensure a representative usage of how Phi-2 or other similar LLMs may be used or misused. Do keep in mind that this list of prompts is nowhere near comprehensive, however, it is a good start.\\\nAfter prompts were crafted, a hyperparameter search for the optimal temperature (according to the average reward value) was found for the prompts with Phi-2 and OpenAssistant's reward model. The optimal temperature was found to be 0.4.\\\nAfter this was completed, Phi-2 was then prompted 8 times on each prompt to get 8 unique responses. Each of these responses were then \"graded\" by the reward model and the \"best\" response (according to the reward model) was saved in the dataset you see for potential fine-tuning.", "#### Who are the source data producers?\n\n\n\nTanush Chopra\n- Ethnicity: Indian\n- Nationality: American\n- Age: 18", "## Bias, Risks, and Limitations\n\n\n\nBy nature of this activity (for an interview), the number of prompts is limited to 10. While the prompts have been carefully selected, they are nowhere near comprehensive and, as such, are limited with the potential scope of prompts and responses it can affect. I will revisit this at a later time to make this dataset more comprehensive. \\\nIn addition, it should be noted that due to this using a reward model instead of human feedback, the results of the responses may not be what \"should\" be enforced. For instance, if the model hallucinating is not caught by the reward model this could potentially be reinforced in the model, which is not good to say the least.", "### Recommendations\n\n\n\nIf you wish to execute a similar feedback pipeline, I would suggest scoring each response with multiple diverse and domain-independent comprehensive reward models instead of just one in order to mitigate potential biases in datasets used for training the reward models. In addition, using a vector DB of facts would be helpful to ensure factual accuracy.", "## Dataset Card Authors [optional]\n\nTanush Chopra", "## Dataset Card Contact\n\ntanushchop@URL" ]

[ "TAGS\n#task_categories-question-answering #size_categories-n<1K #language-English #license-mit #rejection-sampling #phi-2 #question-answering #region-us \n", "# Phi-2 Rejection Sampling\n\nThe Phi-2 Rejection Sampling dataset is an English-language dataset consisting of 10 prompts and responses generated by Phi-2 and graded by the OpenAssistant's reward model.", "## Dataset Details", "### Dataset Description\n\nThe Phi-2 Rejection Sampling dataset is a small (n = 10) English-language dataset. This dataset was created with the purpose was to demonstrate a feedback pipeline where in which Phi-2 would interact with the OpenAssistant reward model to discover \"good\" responses to given prompts. For more information about how this dataset is intended to be used and how it was created, please refer to their respective sections.\n\n- Curated by: Tanush Chopra\n- Language(s) (NLP): English\n- License: MIT License", "## Uses\n\nThis is intended to be used to fine-tuning and improving Phi-2's responses.", "### Direct Use\n\nAs stated above, this is intended to be used to fine-tune Phi-2 and improve its responses for the use cases captured by the selected prompts.", "### Out-of-Scope Use\n\nWhile this could potentially be used for other LLMs, caution should be observed as each LLM is different and has different strengths and weaknesses. Given that, if you were to use the dataset to finetune other LLMs you may observe unintended behavior and negative impacts on the responses post-finetuning.", "## Dataset Structure\n\nThere are only 2 fields to observe in this dataset: prompt and response\n- prompt: string\n- response: string\n\nThey are in a .tsv format, meaning you can expect them structured as such:\n\nPROMPT\\tRESPONSE \\\n{PROMPT_NUMBER_1}\\t{RESPONSE_NUMBER_1} \\\n... \\\n{PROMPT_NUMBER_10}\\t{RESPONSE_NUMBER_10}\n\nYou can find the dataset under URL.", "## Dataset Creation", "### Curation Rationale\n\nThe dataset was curated with the intent of compiling a set of prompt-response pairs that could then be used to fine-tune Phi-2, improving its responses to similar such prompts and styles of prompts.", "### Source Data\n\n\n\nPrompts were curated by Tanush Chopra by looking over various prompts, styles, and domains used in datasets (e.g. MMLU, HellaSwag), various papers on ArXiv on prompting styles (e.g. Chain of Thought) and prompt repositories (e.g. FlowGPT, repo).\\\nResponses were generated and evaluated using Phi-2 and OpenAssistant's reward model.", "#### Data Collection and Processing\n\n\n\nPrompts were curated with 3 main areas in mind:\n- Domain Usage (Arithmetic, Role-Playing, Commonsense, Conversation, Task/Planning)\n- Prompting Style (Chain Of Thought, ELI5)\n- Alignment (Hallucination, Bias and Toxicity, Power-Seeking, Moderation)\n\nEach of these areas were kept in mind with the crafting of each prompt in order to ensure a representative usage of how Phi-2 or other similar LLMs may be used or misused. Do keep in mind that this list of prompts is nowhere near comprehensive, however, it is a good start.\\\nAfter prompts were crafted, a hyperparameter search for the optimal temperature (according to the average reward value) was found for the prompts with Phi-2 and OpenAssistant's reward model. The optimal temperature was found to be 0.4.\\\nAfter this was completed, Phi-2 was then prompted 8 times on each prompt to get 8 unique responses. Each of these responses were then \"graded\" by the reward model and the \"best\" response (according to the reward model) was saved in the dataset you see for potential fine-tuning.", "#### Who are the source data producers?\n\n\n\nTanush Chopra\n- Ethnicity: Indian\n- Nationality: American\n- Age: 18", "## Bias, Risks, and Limitations\n\n\n\nBy nature of this activity (for an interview), the number of prompts is limited to 10. While the prompts have been carefully selected, they are nowhere near comprehensive and, as such, are limited with the potential scope of prompts and responses it can affect. I will revisit this at a later time to make this dataset more comprehensive. \\\nIn addition, it should be noted that due to this using a reward model instead of human feedback, the results of the responses may not be what \"should\" be enforced. For instance, if the model hallucinating is not caught by the reward model this could potentially be reinforced in the model, which is not good to say the least.", "### Recommendations\n\n\n\nIf you wish to execute a similar feedback pipeline, I would suggest scoring each response with multiple diverse and domain-independent comprehensive reward models instead of just one in order to mitigate potential biases in datasets used for training the reward models. In addition, using a vector DB of facts would be helpful to ensure factual accuracy.", "## Dataset Card Authors [optional]\n\nTanush Chopra", "## Dataset Card Contact\n\ntanushchop@URL" ]

[ 53, 55, 4, 124, 23, 38, 85, 116, 5, 56, 108, 277, 29, 162, 83, 14, 10 ]

[ "passage: TAGS\n#task_categories-question-answering #size_categories-n<1K #language-English #license-mit #rejection-sampling #phi-2 #question-answering #region-us \n# Phi-2 Rejection Sampling\n\nThe Phi-2 Rejection Sampling dataset is an English-language dataset consisting of 10 prompts and responses generated by Phi-2 and graded by the OpenAssistant's reward model.## Dataset Details### Dataset Description\n\nThe Phi-2 Rejection Sampling dataset is a small (n = 10) English-language dataset. This dataset was created with the purpose was to demonstrate a feedback pipeline where in which Phi-2 would interact with the OpenAssistant reward model to discover \"good\" responses to given prompts. For more information about how this dataset is intended to be used and how it was created, please refer to their respective sections.\n\n- Curated by: Tanush Chopra\n- Language(s) (NLP): English\n- License: MIT License## Uses\n\nThis is intended to be used to fine-tuning and improving Phi-2's responses.### Direct Use\n\nAs stated above, this is intended to be used to fine-tune Phi-2 and improve its responses for the use cases captured by the selected prompts.### Out-of-Scope Use\n\nWhile this could potentially be used for other LLMs, caution should be observed as each LLM is different and has different strengths and weaknesses. Given that, if you were to use the dataset to finetune other LLMs you may observe unintended behavior and negative impacts on the responses post-finetuning.## Dataset Structure\n\nThere are only 2 fields to observe in this dataset: prompt and response\n- prompt: string\n- response: string\n\nThey are in a .tsv format, meaning you can expect them structured as such:\n\nPROMPT\\tRESPONSE \\\n{PROMPT_NUMBER_1}\\t{RESPONSE_NUMBER_1} \\\n... \\\n{PROMPT_NUMBER_10}\\t{RESPONSE_NUMBER_10}\n\nYou can find the dataset under URL.## Dataset Creation", "passage: ### Curation Rationale\n\nThe dataset was curated with the intent of compiling a set of prompt-response pairs that could then be used to fine-tune Phi-2, improving its responses to similar such prompts and styles of prompts.### Source Data\n\n\n\nPrompts were curated by Tanush Chopra by looking over various prompts, styles, and domains used in datasets (e.g. MMLU, HellaSwag), various papers on ArXiv on prompting styles (e.g. Chain of Thought) and prompt repositories (e.g. FlowGPT, repo).\\\nResponses were generated and evaluated using Phi-2 and OpenAssistant's reward model.#### Data Collection and Processing\n\n\n\nPrompts were curated with 3 main areas in mind:\n- Domain Usage (Arithmetic, Role-Playing, Commonsense, Conversation, Task/Planning)\n- Prompting Style (Chain Of Thought, ELI5)\n- Alignment (Hallucination, Bias and Toxicity, Power-Seeking, Moderation)\n\nEach of these areas were kept in mind with the crafting of each prompt in order to ensure a representative usage of how Phi-2 or other similar LLMs may be used or misused. Do keep in mind that this list of prompts is nowhere near comprehensive, however, it is a good start.\\\nAfter prompts were crafted, a hyperparameter search for the optimal temperature (according to the average reward value) was found for the prompts with Phi-2 and OpenAssistant's reward model. The optimal temperature was found to be 0.4.\\\nAfter this was completed, Phi-2 was then prompted 8 times on each prompt to get 8 unique responses. Each of these responses were then \"graded\" by the reward model and the \"best\" response (according to the reward model) was saved in the dataset you see for potential fine-tuning.#### Who are the source data producers?\n\n\n\nTanush Chopra\n- Ethnicity: Indian\n- Nationality: American\n- Age: 18" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of xaviviro/FLOR-1.3B-xat\n\n\n\nDataset automatically created during the evaluation run of model xaviviro/FLOR-1.3B-xat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T21:25:58.106311(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 xaviviro/FLOR-1.3B-xat\n\n\n\nDataset automatically created during the evaluation run of model xaviviro/FLOR-1.3B-xat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T21:25:58.106311(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 185, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of xaviviro/FLOR-1.3B-xat\n\n\n\nDataset automatically created during the evaluation run of model xaviviro/FLOR-1.3B-xat on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-07T21:25:58.106311(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 jilp00/Hermes-2-SOLAR-10.7B-Symbolic\n\n\n\nDataset automatically created during the evaluation run of model jilp00/Hermes-2-SOLAR-10.7B-Symbolic on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T21:33:05.098650(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 jilp00/Hermes-2-SOLAR-10.7B-Symbolic\n\n\n\nDataset automatically created during the evaluation run of model jilp00/Hermes-2-SOLAR-10.7B-Symbolic on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T21:33:05.098650(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 197, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of jilp00/Hermes-2-SOLAR-10.7B-Symbolic\n\n\n\nDataset automatically created during the evaluation run of model jilp00/Hermes-2-SOLAR-10.7B-Symbolic on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-07T21:33:05.098650(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of bhavinjawade/SOLAR-10B-OrcaDPO-Jawade\n\n\n\nDataset automatically created during the evaluation run of model bhavinjawade/SOLAR-10B-OrcaDPO-Jawade on the Open LLM Leaderboard.\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 2024-01-08T00:26:00.493175(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 bhavinjawade/SOLAR-10B-OrcaDPO-Jawade\n\n\n\nDataset automatically created during the evaluation run of model bhavinjawade/SOLAR-10B-OrcaDPO-Jawade on the Open LLM Leaderboard.\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 2024-01-08T00:26:00.493175(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 199, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of bhavinjawade/SOLAR-10B-OrcaDPO-Jawade\n\n\n\nDataset automatically created during the evaluation run of model bhavinjawade/SOLAR-10B-OrcaDPO-Jawade on the Open LLM Leaderboard.\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 2024-01-08T00:26:00.493175(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]

[ "source_datasets:deepset/germanquad", "language:de", "license:cc-by-4.0", "arxiv:2104.12741", "region:us" ]

[ "2104.12741" ]

[ "de" ]

[]

[ "TAGS\n#source_datasets-deepset/germanquad #language-German #license-cc-by-4.0 #arxiv-2104.12741 #region-us \n" ]

[ 40 ]

[ "passage: TAGS\n#source_datasets-deepset/germanquad #language-German #license-cc-by-4.0 #arxiv-2104.12741 #region-us \n" ]

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

[]

[]

[ "# 605MB" ]

[ "TAGS\n#license-apache-2.0 #region-us \n", "# 605MB" ]

[ 14, 4 ]

[ "passage: TAGS\n#license-apache-2.0 #region-us \n# 605MB" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of AtAndDev/CapybaraMarcoroni-7B\n\n\n\nDataset automatically created during the evaluation run of model AtAndDev/CapybaraMarcoroni-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 2024-01-07T21:50:49.600700(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 AtAndDev/CapybaraMarcoroni-7B\n\n\n\nDataset automatically created during the evaluation run of model AtAndDev/CapybaraMarcoroni-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 2024-01-07T21:50:49.600700(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 185, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AtAndDev/CapybaraMarcoroni-7B\n\n\n\nDataset automatically created during the evaluation run of model AtAndDev/CapybaraMarcoroni-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 2024-01-07T21:50:49.600700(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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" ]

[ 6 ]

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

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of HenryJJ/Instruct_Yi-6B_Dolly_CodeAlpaca\n\n\n\nDataset automatically created during the evaluation run of model HenryJJ/Instruct_Yi-6B_Dolly_CodeAlpaca on the Open LLM Leaderboard.\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 2024-01-08T03:36:26.320528(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 HenryJJ/Instruct_Yi-6B_Dolly_CodeAlpaca\n\n\n\nDataset automatically created during the evaluation run of model HenryJJ/Instruct_Yi-6B_Dolly_CodeAlpaca on the Open LLM Leaderboard.\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 2024-01-08T03:36:26.320528(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 197, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of HenryJJ/Instruct_Yi-6B_Dolly_CodeAlpaca\n\n\n\nDataset automatically created during the evaluation run of model HenryJJ/Instruct_Yi-6B_Dolly_CodeAlpaca on the Open LLM Leaderboard.\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 2024-01-08T03:36:26.320528(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of mlabonne/NeuralMarcoro14-7B\n\n\n\nDataset automatically created during the evaluation run of model mlabonne/NeuralMarcoro14-7B 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 2024-01-07T22:13:05.321347(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 mlabonne/NeuralMarcoro14-7B\n\n\n\nDataset automatically created during the evaluation run of model mlabonne/NeuralMarcoro14-7B 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 2024-01-07T22:13:05.321347(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 183, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of mlabonne/NeuralMarcoro14-7B\n\n\n\nDataset automatically created during the evaluation run of model mlabonne/NeuralMarcoro14-7B 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 2024-01-07T22:13:05.321347(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 alexredna/TinyLlama-1.1B-Chat-v1.0-reasoning-v2-dpo\n\n\n\nDataset automatically created during the evaluation run of model alexredna/TinyLlama-1.1B-Chat-v1.0-reasoning-v2-dpo on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-10T12:31:49.515266(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 alexredna/TinyLlama-1.1B-Chat-v1.0-reasoning-v2-dpo\n\n\n\nDataset automatically created during the evaluation run of model alexredna/TinyLlama-1.1B-Chat-v1.0-reasoning-v2-dpo on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-10T12:31:49.515266(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 213, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of alexredna/TinyLlama-1.1B-Chat-v1.0-reasoning-v2-dpo\n\n\n\nDataset automatically created during the evaluation run of model alexredna/TinyLlama-1.1B-Chat-v1.0-reasoning-v2-dpo on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-10T12:31:49.515266(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:" ]

[ "region:us" ]

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[ "# Dataset Card for Evaluation run of ryandt/MusingCaterpillar\n\n\n\nDataset automatically created during the evaluation run of model ryandt/MusingCaterpillar on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T22:15:17.631393(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 ryandt/MusingCaterpillar\n\n\n\nDataset automatically created during the evaluation run of model ryandt/MusingCaterpillar on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T22:15:17.631393(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 181, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ryandt/MusingCaterpillar\n\n\n\nDataset automatically created during the evaluation run of model ryandt/MusingCaterpillar on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-07T22:15:17.631393(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 NeverSleep/Noromaid-13b-v0.3\n\n\n\nDataset automatically created during the evaluation run of model NeverSleep/Noromaid-13b-v0.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2024-01-08T08:43:54.536488(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of NeverSleep/Noromaid-13b-v0.3\n\n\n\nDataset automatically created during the evaluation run of model NeverSleep/Noromaid-13b-v0.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2024-01-08T08:43:54.536488(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 187, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of NeverSleep/Noromaid-13b-v0.3\n\n\n\nDataset automatically created during the evaluation run of model NeverSleep/Noromaid-13b-v0.3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2024-01-08T08:43:54.536488(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 sreeramajay/TinyLlama-1.1B-orca-v1.0\n\n\n\nDataset automatically created during the evaluation run of model sreeramajay/TinyLlama-1.1B-orca-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 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 2024-01-08T02:53:34.794924(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 sreeramajay/TinyLlama-1.1B-orca-v1.0\n\n\n\nDataset automatically created during the evaluation run of model sreeramajay/TinyLlama-1.1B-orca-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 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 2024-01-08T02:53:34.794924(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 199, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of sreeramajay/TinyLlama-1.1B-orca-v1.0\n\n\n\nDataset automatically created during the evaluation run of model sreeramajay/TinyLlama-1.1B-orca-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 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 2024-01-08T02:53:34.794924(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of aloobun/falcon-1b-cot-t2\n\n\n\nDataset automatically created during the evaluation run of model aloobun/falcon-1b-cot-t2 on the Open LLM Leaderboard.\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 2024-01-08T06:26:29.357404(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 aloobun/falcon-1b-cot-t2\n\n\n\nDataset automatically created during the evaluation run of model aloobun/falcon-1b-cot-t2 on the Open LLM Leaderboard.\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 2024-01-08T06:26:29.357404(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 187, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of aloobun/falcon-1b-cot-t2\n\n\n\nDataset automatically created during the evaluation run of model aloobun/falcon-1b-cot-t2 on the Open LLM Leaderboard.\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 2024-01-08T06:26:29.357404(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "task_categories:conversational", "task_categories:text-generation", "size_categories:10K<n<100K", "language:en", "license:mit", "region:us" ]

[]

[ "en" ]

[ "# Worldbuild\n\nA dataset focused on worldbuilding and roleplay, mostly well-formatted, high quality data, in the markdown format." ]

[ "TAGS\n#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-mit #region-us \n", "# Worldbuild\n\nA dataset focused on worldbuilding and roleplay, mostly well-formatted, high quality data, in the markdown format." ]

[ 48, 30 ]

[ "passage: TAGS\n#task_categories-conversational #task_categories-text-generation #size_categories-10K<n<100K #language-English #license-mit #region-us \n# Worldbuild\n\nA dataset focused on worldbuilding and roleplay, mostly well-formatted, high quality data, in the markdown format." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of remyxai/localmentor_25K_3epochs_tinyllama\n\n\n\nDataset automatically created during the evaluation run of model remyxai/localmentor_25K_3epochs_tinyllama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T22:25:15.681205(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 remyxai/localmentor_25K_3epochs_tinyllama\n\n\n\nDataset automatically created during the evaluation run of model remyxai/localmentor_25K_3epochs_tinyllama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T22:25:15.681205(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 201, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of remyxai/localmentor_25K_3epochs_tinyllama\n\n\n\nDataset automatically created during the evaluation run of model remyxai/localmentor_25K_3epochs_tinyllama on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-07T22:25:15.681205(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of amu/zen\n\n\n\nDataset automatically created during the evaluation run of model amu/zen on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T22:25:34.311431(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 amu/zen\n\n\n\nDataset automatically created during the evaluation run of model amu/zen on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T22:25:34.311431(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 169, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of amu/zen\n\n\n\nDataset automatically created during the evaluation run of model amu/zen on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-07T22:25:34.311431(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "task_categories:translation", "task_categories:text2text-generation", "annotations_creators:crowdsourced", "size_categories:1M<n<10M", "language:aa", "language:ab", "language:ace", "language:ach", "language:af", "language:afh", "language:aii", "language:ain", "language:ajp", "language:ak", "language:am", "language:an", "language:ang", "language:anp", "language:apc", "language:ar", "language:arn", "language:ars", "language:as", "language:ast", "language:ay", "language:ayc", "language:az", "language:azb", "language:ba", "language:bar", "language:bd", "language:be", "language:bem", "language:ber", "language:bg", "language:bho", "language:bm", "language:bn", "language:bo", "language:bp", "language:bqi", "language:br", "language:brx", "language:bs", "language:bul", "language:by", "language:ca", "language:ce", "language:ceb", "language:ckb", "language:cmn", "language:cn", "language:cnr", "language:co", "language:cr", "language:crh", "language:cs", "language:csb", "language:cv", "language:cy", "language:cz", "language:da", "language:de", "language:dev", "language:doi", "language:dsb", "language:dua", "language:dum", "language:dv", "language:dz", "language:eg", "language:el", "language:en", "language:eng", "language:enm", "language:eo", "language:es", "language:et", "language:eu", "language:ext", "language:fa", "language:fi", "language:fil", "language:fo", "language:fr", "language:fra", "language:frm", "language:frp", "language:frs", "language:fu", "language:fur", "language:fy", "language:ga", "language:gb", "language:gd", "language:gl", "language:glk", "language:gmh", "language:gn", "language:gr", "language:gsw", "language:gu", "language:guc", "language:gug", "language:gum", "language:guw", "language:gv", "language:ha", "language:haw", "language:he", "language:hi", "language:hne", "language:hr", "language:hrx", "language:hsb", "language:ht", "language:hu", "language:hy", "language:hz", "language:ia", "language:id", "language:ie", "language:ig", "language:in", "language:io", "language:is", "language:it", "language:iw", "language:ja", "language:jam", "language:jbo", "language:ji", "language:jp", "language:jpn", "language:jv", "language:ka", "language:kab", "language:kg", "language:kk", "language:kl", "language:km", "language:kmr", "language:kn", "language:ko", "language:kok", "language:kr", "language:krl", "language:ks", "language:ksh", "language:ku", "language:kw", "language:ky", "language:la", "language:lb", "language:lfn", "language:lg", "language:li", "language:lk", "language:ln", "language:lo", "language:lt", "language:ltg", "language:lv", "language:lzh", "language:mai", "language:me", "language:mg", "language:mhr", "language:mi", "language:mjw", "language:mk", "language:ml", "language:mn", "language:mnc", "language:mni", "language:mnw", "language:mo", "language:mr", "language:ms", "language:mt", "language:my", "language:na", "language:nah", "language:nan", "language:nap", "language:nb", "language:nds", "language:ne", "language:nl", "language:nn", "language:no", "language:np", "language:nqo", "language:ny", "language:oc", "language:oj", "language:om", "language:or", "language:os", "language:ota", "language:pa", "language:pam", "language:pap", "language:pbb", "language:peo", "language:pk", "language:pl", "language:pms", "language:pr", "language:prg", "language:ps", "language:pt", "language:pu", "language:qt", "language:rcf", "language:rm", "language:ro", "language:rom", "language:ru", "language:rue", "language:rw", "language:ryu", "language:sa", "language:sah", "language:sai", "language:sat", "language:sc", "language:sco", "language:sd", "language:sdh", "language:se", "language:sh", "language:shn", "language:si", "language:sk", "language:skr", "language:sl", "language:sm", "language:sma", "language:sn", "language:so", "language:sq", "language:sr", "language:st", "language:su", "language:sv", "language:sw", "language:szl", "language:ta", "language:tam", "language:te", "language:tet", "language:tg", "language:th", "language:ti", "language:tk", "language:tl", "language:tlh", "language:tn", "language:to", "language:tok", "language:tr", "language:trv", "language:tt", "language:tum", "language:tw", "language:ty", "language:tzm", "language:ua", "language:udm", "language:ug", "language:uk", "language:und", "language:ur", "language:us", "language:uz", "language:vec", "language:vi", "language:vls", "language:wa", "language:wae", "language:wo", "language:xh", "language:yi", "language:yo", "language:yue", "language:zgh", "language:zh", "language:zu", "region:us" ]

[]

[ "aa", "ab", "ace", "ach", "af", "afh", "aii", "ain", "ajp", "ak", "am", "an", "ang", "anp", "apc", "ar", "arn", "ars", "as", "ast", "ay", "ayc", "az", "azb", "ba", "bar", "bd", "be", "bem", "ber", "bg", "bho", "bm", "bn", "bo", "bp", "bqi", "br", "brx", "bs", "bul", "by", "ca", "ce", "ceb", "ckb", "cmn", "cn", "cnr", "co", "cr", "crh", "cs", "csb", "cv", "cy", "cz", "da", "de", "dev", "doi", "dsb", "dua", "dum", "dv", "dz", "eg", "el", "en", "eng", "enm", "eo", "es", "et", "eu", "ext", "fa", "fi", "fil", "fo", "fr", "fra", "frm", "frp", "frs", "fu", "fur", "fy", "ga", "gb", "gd", "gl", "glk", "gmh", "gn", "gr", "gsw", "gu", "guc", "gug", "gum", "guw", "gv", "ha", "haw", "he", "hi", "hne", "hr", "hrx", "hsb", "ht", "hu", "hy", "hz", "ia", "id", "ie", "ig", "in", "io", "is", "it", "iw", "ja", "jam", "jbo", "ji", "jp", "jpn", "jv", "ka", "kab", "kg", "kk", "kl", "km", "kmr", "kn", "ko", "kok", "kr", "krl", "ks", "ksh", "ku", "kw", "ky", "la", "lb", "lfn", "lg", "li", "lk", "ln", "lo", "lt", "ltg", "lv", "lzh", "mai", "me", "mg", "mhr", "mi", "mjw", "mk", "ml", "mn", "mnc", "mni", "mnw", "mo", "mr", "ms", "mt", "my", "na", "nah", "nan", "nap", "nb", "nds", "ne", "nl", "nn", "no", "np", "nqo", "ny", "oc", "oj", "om", "or", "os", "ota", "pa", "pam", "pap", "pbb", "peo", "pk", "pl", "pms", "pr", "prg", "ps", "pt", "pu", "qt", "rcf", "rm", "ro", "rom", "ru", "rue", "rw", "ryu", "sa", "sah", "sai", "sat", "sc", "sco", "sd", "sdh", "se", "sh", "shn", "si", "sk", "skr", "sl", "sm", "sma", "sn", "so", "sq", "sr", "st", "su", "sv", "sw", "szl", "ta", "tam", "te", "tet", "tg", "th", "ti", "tk", "tl", "tlh", "tn", "to", "tok", "tr", "trv", "tt", "tum", "tw", "ty", "tzm", "ua", "udm", "ug", "uk", "und", "ur", "us", "uz", "vec", "vi", "vls", "wa", "wae", "wo", "xh", "yi", "yo", "yue", "zgh", "zh", "zu" ]

[ "# Dataset Card for Weblate Translations\n\n\n\nA dataset containing strings from projects hosted on Weblate and their translations into other languages.\nPlease consider donating or contributing to Weblate if you find this dataset useful.\n\nTo avoid rows with values like \"None\" and \"N/A\" being interpreted as missing values, pass the keep_default_na parameter like this:", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License: Each sentence pair in the dataset has a corresponding license in the \"license\" column. This license is the one specified in the component or project containing the sentence.", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses\n\n\n- Machine Translation\n- Language Identification", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing\n\n\n\n- Sentence pairs with empty/missing elements were dropped.\n- Identical pairs were dropped.\n- Trailing whitespace was stripped.\n- Rows were deduplicated based on all 3 columns including \"license\", on a config/subset/tsv file basis. Which means that a single config might contain two identical sentence pairs with different licenses. Or a different config/subset might contain the exact same row (most likely a different variant/dialect of the same language(s)).", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?\n\n\n\nWeblate users.", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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-translation #task_categories-text2text-generation #annotations_creators-crowdsourced #size_categories-1M<n<10M #language-Afar #language-Abkhazian #language-Achinese #language-Acoli #language-Afrikaans #language-Afrihili #language-Assyrian Neo-Aramaic #language-Ainu (Japan) #language-South Levantine Arabic #language-Akan #language-Amharic #language-Aragonese #language-Old English (ca. 450-1100) #language-Angika #language-Levantine Arabic #language-Arabic #language-Mapudungun #language-Najdi Arabic #language-Assamese #language-Asturian #language-Aymara #language-Southern Aymara #language-Azerbaijani #language-South Azerbaijani #language-Bashkir #language-Bavarian #language-bd #language-Belarusian #language-Bemba (Zambia) #language-ber #language-Bulgarian #language-Bhojpuri #language-Bambara #language-Bengali #language-Tibetan #language-bp #language-Bakhtiari #language-Breton #language-Bodo (India) #language-Bosnian #language-Bulgarian #language-by #language-Catalan #language-Chechen #language-Cebuano #language-Central Kurdish #language-Mandarin Chinese #language-cn #language-Montenegrin #language-Corsican #language-Cree #language-Crimean Tatar #language-Czech #language-Kashubian #language-Chuvash #language-Welsh #language-cz #language-Danish #language-German #language-Domung #language-Dogri (macrolanguage) #language-Lower Sorbian #language-Duala #language-Middle Dutch (ca. 1050-1350) #language-Dhivehi #language-Dzongkha #language-eg #language-Modern Greek (1453-) #language-English #language-English #language-Middle English (1100-1500) #language-Esperanto #language-Spanish #language-Estonian #language-Basque #language-Extremaduran #language-Persian #language-Finnish #language-Filipino #language-Faroese #language-French #language-French #language-Middle French (ca. 1400-1600) #language-Arpitan #language-Eastern Frisian #language-fu #language-Friulian #language-Western Frisian #language-Irish #language-gb #language-Scottish Gaelic #language-Galician #language-Gilaki #language-Middle High German (ca. 1050-1500) #language-Guarani #language-gr #language-Swiss German #language-Gujarati #language-Wayuu #language-Paraguayan Guaraní #language-Guambiano #language-Gun #language-Manx #language-Hausa #language-Hawaiian #language-Hebrew #language-Hindi #language-Chhattisgarhi #language-Croatian #language-Hunsrik #language-Upper Sorbian #language-Haitian #language-Hungarian #language-Armenian #language-Herero #language-Interlingua (International Auxiliary Language Association) #language-Indonesian #language-Interlingue #language-Igbo #language-in #language-Ido #language-Icelandic #language-Italian #language-iw #language-Japanese #language-Jamaican Creole English #language-Lojban #language-ji #language-jp #language-Japanese #language-Javanese #language-Georgian #language-Kabyle #language-Kongo #language-Kazakh #language-Kalaallisut #language-Khmer #language-Northern Kurdish #language-Kannada #language-Korean #language-Konkani (macrolanguage) #language-Kanuri #language-Karelian #language-Kashmiri #language-Kölsch #language-Kurdish #language-Cornish #language-Kirghiz #language-Latin #language-Luxembourgish #language-Lingua Franca Nova #language-Ganda #language-Limburgan #language-lk #language-Lingala #language-Lao #language-Lithuanian #language-Latgalian #language-Latvian #language-Literary Chinese #language-Maithili #language-me #language-Malagasy #language-Eastern Mari #language-Maori #language-Karbi #language-Macedonian #language-Malayalam #language-Mongolian #language-Manchu #language-Manipuri #language-Mon #language-mo #language-Marathi #language-Malay (macrolanguage) #language-Maltese #language-Burmese #language-Nauru #language-nah #language-Min Nan Chinese #language-Neapolitan #language-Norwegian Bokmål #language-Low German #language-Nepali (macrolanguage) #language-Dutch #language-Norwegian Nynorsk #language-Norwegian #language-np #language-N'Ko #language-Nyanja #language-Occitan (post 1500) #language-Ojibwa #language-Oromo #language-Oriya (macrolanguage) #language-Ossetian #language-Ottoman Turkish (1500-1928) #language-Panjabi #language-Pampanga #language-Papiamento #language-Páez #language-Old Persian (ca. 600-400 B.C.) #language-pk #language-Polish #language-Piemontese #language-pr #language-Prussian #language-Pushto #language-Portuguese #language-pu #language-qt #language-Réunion Creole French #language-Romansh #language-Romanian #language-Romany #language-Russian #language-Rusyn #language-Kinyarwanda #language-Central Okinawan #language-Sanskrit #language-Yakut #language-sai #language-Santali #language-Sardinian #language-Scots #language-Sindhi #language-Southern Kurdish #language-Northern Sami #language-Serbo-Croatian #language-Shan #language-Sinhala #language-Slovak #language-Saraiki #language-Slovenian #language-Samoan #language-Southern Sami #language-Shona #language-Somali #language-Albanian #language-Serbian #language-Southern Sotho #language-Sundanese #language-Swedish #language-Swahili (macrolanguage) #language-Silesian #language-Tamil #language-Tamil #language-Telugu #language-Tetum #language-Tajik #language-Thai #language-Tigrinya #language-Turkmen #language-Tagalog #language-Klingon #language-Tswana #language-Tonga (Tonga Islands) #language-Toki Pona #language-Turkish #language-Sediq #language-Tatar #language-Tumbuka #language-Twi #language-Tahitian #language-Central Atlas Tamazight #language-ua #language-Udmurt #language-Uighur #language-Ukrainian #language-Undetermined #language-Urdu #language-us #language-Uzbek #language-Venetian #language-Vietnamese #language-Vlaams #language-Walloon #language-Walser #language-Wolof #language-Xhosa #language-Yiddish #language-Yoruba #language-Yue Chinese #language-Standard Moroccan Tamazight #language-Chinese #language-Zulu #region-us \n", "# Dataset Card for Weblate Translations\n\n\n\nA dataset containing strings from projects hosted on Weblate and their translations into other languages.\nPlease consider donating or contributing to Weblate if you find this dataset useful.\n\nTo avoid rows with values like \"None\" and \"N/A\" being interpreted as missing values, pass the keep_default_na parameter like this:", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License: Each sentence pair in the dataset has a corresponding license in the \"license\" column. This license is the one specified in the component or project containing the sentence.", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses\n\n\n- Machine Translation\n- Language Identification", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing\n\n\n\n- Sentence pairs with empty/missing elements were dropped.\n- Identical pairs were dropped.\n- Trailing whitespace was stripped.\n- Rows were deduplicated based on all 3 columns including \"license\", on a config/subset/tsv file basis. Which means that a single config might contain two identical sentence pairs with different licenses. Or a different config/subset might contain the exact same row (most likely a different variant/dialect of the same language(s)).", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?\n\n\n\nWeblate users.", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 1802, 86, 4, 79, 29, 10, 4, 9, 6, 5, 7, 4, 127, 10, 9, 5, 13, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: " ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"unit-test-v2\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"unit-test-v2\"\n\nMore Information needed" ]

[ 6, 17 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"unit-test-v2\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of BEE-spoke-data/TinyLlama-3T-1.1bee\n\n\n\nDataset automatically created during the evaluation run of model BEE-spoke-data/TinyLlama-3T-1.1bee on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T23:10:41.874868(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 BEE-spoke-data/TinyLlama-3T-1.1bee\n\n\n\nDataset automatically created during the evaluation run of model BEE-spoke-data/TinyLlama-3T-1.1bee on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-07T23:10:41.874868(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 197, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of BEE-spoke-data/TinyLlama-3T-1.1bee\n\n\n\nDataset automatically created during the evaluation run of model BEE-spoke-data/TinyLlama-3T-1.1bee on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-07T23:10:41.874868(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of NeuralNovel/Aeryth-7B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model NeuralNovel/Aeryth-7B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-14T12:31:11.639995(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of NeuralNovel/Aeryth-7B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model NeuralNovel/Aeryth-7B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-14T12:31:11.639995(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 187, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of NeuralNovel/Aeryth-7B-v0.1\n\n\n\nDataset automatically created during the evaluation run of model NeuralNovel/Aeryth-7B-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-14T12:31:11.639995(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "task_categories:translation", "size_categories:100M<n<1B", "language:zh", "license:mit", "translation", "古文翻译", "文言文翻译", "region:us" ]

[]

[ "zh" ]

[ "# HistoryTrans\n\nHistoryTrans 是一个古文翻译数据集，通过数据预处理和质量控制，来提高古文翻译的质量和实用性。\n\n参考我们的项目主页HistoryTrans古文翻译.", "## 数据集详细信息", "### 数据集来源\n\n- 主体: Classical-Modern\n- 额外补充: ：《二十四史》和《清史稿》中提取", "### 数据集结构\n\n数据集包含以下 JSONL 文件：\n\n- 'train_01_04.jsonl': 训练集，主要用于训练翻译模型。\n- 'val_01_04.jsonl': 验证集，用于训练过程中的模型微调和评估。\n- 'test_01_04.jsonl': 测试集，用于评估最终模型性能。\n\n每个 JSON 对象包括：\n\n- 'inputs': 原始古文\n- 'truth': 准确翻译\n\n例如：" ]

[ "TAGS\n#task_categories-translation #size_categories-100M<n<1B #language-Chinese #license-mit #translation #古文翻译 #文言文翻译 #region-us \n", "# HistoryTrans\n\nHistoryTrans 是一个古文翻译数据集，通过数据预处理和质量控制，来提高古文翻译的质量和实用性。\n\n参考我们的项目主页HistoryTrans古文翻译.", "## 数据集详细信息", "### 数据集来源\n\n- 主体: Classical-Modern\n- 额外补充: ：《二十四史》和《清史稿》中提取", "### 数据集结构\n\n数据集包含以下 JSONL 文件：\n\n- 'train_01_04.jsonl': 训练集，主要用于训练翻译模型。\n- 'val_01_04.jsonl': 验证集，用于训练过程中的模型微调和评估。\n- 'test_01_04.jsonl': 测试集，用于评估最终模型性能。\n\n每个 JSON 对象包括：\n\n- 'inputs': 原始古文\n- 'truth': 准确翻译\n\n例如：" ]

[ 49, 45, 6, 35, 119 ]

[ "passage: TAGS\n#task_categories-translation #size_categories-100M<n<1B #language-Chinese #license-mit #translation #古文翻译 #文言文翻译 #region-us \n# HistoryTrans\n\nHistoryTrans 是一个古文翻译数据集，通过数据预处理和质量控制，来提高古文翻译的质量和实用性。\n\n参考我们的项目主页HistoryTrans古文翻译.## 数据集详细信息### 数据集来源\n\n- 主体: Classical-Modern\n- 额外补充: ：《二十四史》和《清史稿》中提取### 数据集结构\n\n数据集包含以下 JSONL 文件：\n\n- 'train_01_04.jsonl': 训练集，主要用于训练翻译模型。\n- 'val_01_04.jsonl': 验证集，用于训练过程中的模型微调和评估。\n- 'test_01_04.jsonl': 测试集，用于评估最终模型性能。\n\n每个 JSON 对象包括：\n\n- 'inputs': 原始古文\n- 'truth': 准确翻译\n\n例如：" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of princeton-nlp/Sheared-LLaMA-1.3B-ShareGPT\n\n\n\nDataset automatically created during the evaluation run of model princeton-nlp/Sheared-LLaMA-1.3B-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 2024-01-07T23:59:12.319843(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 princeton-nlp/Sheared-LLaMA-1.3B-ShareGPT\n\n\n\nDataset automatically created during the evaluation run of model princeton-nlp/Sheared-LLaMA-1.3B-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 2024-01-07T23:59:12.319843(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 201, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of princeton-nlp/Sheared-LLaMA-1.3B-ShareGPT\n\n\n\nDataset automatically created during the evaluation run of model princeton-nlp/Sheared-LLaMA-1.3B-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 2024-01-07T23:59:12.319843(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"autotree_snnxor_n15_l2_10\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"autotree_snnxor_n15_l2_10\"\n\nMore Information needed" ]

[ 6, 24 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"autotree_snnxor_n15_l2_10\"\n\nMore Information needed" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of maywell/TinyLlama-MoE-Chat-0.1\n\n\n\nDataset automatically created during the evaluation run of model maywell/TinyLlama-MoE-Chat-0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2024-01-08T02:02:06.630482(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 maywell/TinyLlama-MoE-Chat-0.1\n\n\n\nDataset automatically created during the evaluation run of model maywell/TinyLlama-MoE-Chat-0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2024-01-08T02:02:06.630482(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 189, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of maywell/TinyLlama-MoE-Chat-0.1\n\n\n\nDataset automatically created during the evaluation run of model maywell/TinyLlama-MoE-Chat-0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2024-01-08T02:02:06.630482(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]" ]

[ "task_categories:text-generation", "task_categories:translation", "task_categories:text2text-generation", "size_categories:10K<n<100K", "language:ro", "license:apache-2.0", "region:us" ]

[]

[ "ro" ]

[]

[ "TAGS\n#task_categories-text-generation #task_categories-translation #task_categories-text2text-generation #size_categories-10K<n<100K #language-Romanian #license-apache-2.0 #region-us \n" ]

[ 64 ]

[ "passage: TAGS\n#task_categories-text-generation #task_categories-translation #task_categories-text2text-generation #size_categories-10K<n<100K #language-Romanian #license-apache-2.0 #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of openaccess-ai-collective/DPOpenHermes-11B\n\n\n\nDataset automatically created during the evaluation run of model openaccess-ai-collective/DPOpenHermes-11B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-08T01:06:40.626102(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 openaccess-ai-collective/DPOpenHermes-11B\n\n\n\nDataset automatically created during the evaluation run of model openaccess-ai-collective/DPOpenHermes-11B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-08T01:06:40.626102(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 189, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of openaccess-ai-collective/DPOpenHermes-11B\n\n\n\nDataset automatically created during the evaluation run of model openaccess-ai-collective/DPOpenHermes-11B on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-08T01:06:40.626102(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"VNTL-v2-1k\"\n\nMore Information needed" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"VNTL-v2-1k\"\n\nMore Information needed" ]

[ 6, 16 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"VNTL-v2-1k\"\n\nMore Information needed" ]

[ "task_categories:zero-shot-classification", "task_categories:image-to-text", "size_categories:10M<n<100M", "language:en", "license:mit", "arxiv:2208.05516", "region:us" ]

[ "2208.05516" ]

[ "en" ]

[ "## YFCC15M subset used for VLMs\n\nThis dataset contains the ~15M subset of YFCC100M used for training the models in the paper Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP. The metadata provided in this repo contains both the page-urls and image-download-urls for downloading the dataset.\nThis dataset can be easily downloaded with img2dataset:" ]

[ "TAGS\n#task_categories-zero-shot-classification #task_categories-image-to-text #size_categories-10M<n<100M #language-English #license-mit #arxiv-2208.05516 #region-us \n", "## YFCC15M subset used for VLMs\n\nThis dataset contains the ~15M subset of YFCC100M used for training the models in the paper Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP. The metadata provided in this repo contains both the page-urls and image-download-urls for downloading the dataset.\nThis dataset can be easily downloaded with img2dataset:" ]

[ 60, 103 ]

[ "passage: TAGS\n#task_categories-zero-shot-classification #task_categories-image-to-text #size_categories-10M<n<100M #language-English #license-mit #arxiv-2208.05516 #region-us \n## YFCC15M subset used for VLMs\n\nThis dataset contains the ~15M subset of YFCC100M used for training the models in the paper Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP. The metadata provided in this repo contains both the page-urls and image-download-urls for downloading the dataset.\nThis dataset can be easily downloaded with img2dataset:" ]

[ "region:us" ]

[]

[]

[]

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

[ 6 ]

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

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of senseable/33x-coder\n\n\n\nDataset automatically created during the evaluation run of model senseable/33x-coder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-08T01:59:05.167741(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ "TAGS\n#region-us \n", "# Dataset Card for Evaluation run of senseable/33x-coder\n\n\n\nDataset automatically created during the evaluation run of model senseable/33x-coder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-08T01:59:05.167741(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 177, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of senseable/33x-coder\n\n\n\nDataset automatically created during the evaluation run of model senseable/33x-coder on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-08T01:59:05.167741(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 OpenBuddy/openbuddy-deepseekcoder-33b-v16.1-32k\n\n\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-deepseekcoder-33b-v16.1-32k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2024-01-08T05:49:52.384662(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 OpenBuddy/openbuddy-deepseekcoder-33b-v16.1-32k\n\n\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-deepseekcoder-33b-v16.1-32k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2024-01-08T05:49:52.384662(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 205, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of OpenBuddy/openbuddy-deepseekcoder-33b-v16.1-32k\n\n\n\nDataset automatically created during the evaluation run of model OpenBuddy/openbuddy-deepseekcoder-33b-v16.1-32k on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 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 2024-01-08T05:49:52.384662(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of gagan3012/MetaModel_moe_multilingualv1\n\n\n\nDataset automatically created during the evaluation run of model gagan3012/MetaModel_moe_multilingualv1 on the Open LLM Leaderboard.\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 2024-01-08T03:30:47.395820(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 gagan3012/MetaModel_moe_multilingualv1\n\n\n\nDataset automatically created during the evaluation run of model gagan3012/MetaModel_moe_multilingualv1 on the Open LLM Leaderboard.\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 2024-01-08T03:30:47.395820(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 193, 69, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of gagan3012/MetaModel_moe_multilingualv1\n\n\n\nDataset automatically created during the evaluation run of model gagan3012/MetaModel_moe_multilingualv1 on the Open LLM Leaderboard.\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 2024-01-08T03:30:47.395820(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]" ]

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

[]

[]

[]

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

[ 14 ]

[ "passage: TAGS\n#license-cc0-1.0 #region-us \n" ]

[ "license:cc-by-nc-sa-4.0", "arxiv:2310.10083", "region:us" ]

[ "2310.10083" ]

[]

[ "# JJSIMQA : 日本内科学会雑誌の付録確認問題\n\n日本内科学会雑誌107巻〜111巻に掲載されているMultiple Choice Questions（各特集に対する５択の確認問題）全460件を人手で書き起こしました.\n医療言語モデルの性能評価用の利用を想定しています.\n\nJMedLoRAの実験にて利用.", "### 注意\n\nproblem_id 107_64-4は, １つ選ばせる問題ですが, ５つ目の選択肢が「(a)〜(d)の全て」となっているため毛色が異なります. \n\n評価用データセットとして用いる場合には除外推奨.", "### How to cite\n\n本データを利用する場合は以下の文献の引用をご検討ください." ]

[ "TAGS\n#license-cc-by-nc-sa-4.0 #arxiv-2310.10083 #region-us \n", "# JJSIMQA : 日本内科学会雑誌の付録確認問題\n\n日本内科学会雑誌107巻〜111巻に掲載されているMultiple Choice Questions（各特集に対する５択の確認問題）全460件を人手で書き起こしました.\n医療言語モデルの性能評価用の利用を想定しています.\n\nJMedLoRAの実験にて利用.", "### 注意\n\nproblem_id 107_64-4は, １つ選ばせる問題ですが, ５つ目の選択肢が「(a)〜(d)の全て」となっているため毛色が異なります. \n\n評価用データセットとして用いる場合には除外推奨.", "### How to cite\n\n本データを利用する場合は以下の文献の引用をご検討ください." ]

[ 28, 78, 57, 17 ]

[ "passage: TAGS\n#license-cc-by-nc-sa-4.0 #arxiv-2310.10083 #region-us \n# JJSIMQA : 日本内科学会雑誌の付録確認問題\n\n日本内科学会雑誌107巻〜111巻に掲載されているMultiple Choice Questions（各特集に対する５択の確認問題）全460件を人手で書き起こしました.\n医療言語モデルの性能評価用の利用を想定しています.\n\nJMedLoRAの実験にて利用.### 注意\n\nproblem_id 107_64-4は, １つ選ばせる問題ですが, ５つ目の選択肢が「(a)〜(d)の全て」となっているため毛色が異なります. \n\n評価用データセットとして用いる場合には除外推奨.### How to cite\n\n本データを利用する場合は以下の文献の引用をご検討ください." ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for Evaluation run of migtissera/SynthIA-70B-v1.5\n\n\n\nDataset automatically created during the evaluation run of model migtissera/SynthIA-70B-v1.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-08T03:41:45.134305(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 migtissera/SynthIA-70B-v1.5\n\n\n\nDataset automatically created during the evaluation run of model migtissera/SynthIA-70B-v1.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-08T03:41:45.134305(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 189, 67, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of migtissera/SynthIA-70B-v1.5\n\n\n\nDataset automatically created during the evaluation run of model migtissera/SynthIA-70B-v1.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-08T03:41:45.134305(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 GOAT-AI/GOAT-70B-Storytelling\n\n\n\nDataset automatically created during the evaluation run of model GOAT-AI/GOAT-70B-Storytelling on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-08T04:02:16.743914(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further 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 GOAT-AI/GOAT-70B-Storytelling\n\n\n\nDataset automatically created during the evaluation run of model GOAT-AI/GOAT-70B-Storytelling on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:", "## Latest results\n\nThese are the latest results from run 2024-01-08T04:02:16.743914(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):", "## Dataset Details", "### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:", "### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Out-of-Scope Use", "## Dataset Structure", "## Dataset Creation", "### Curation Rationale", "### Source Data", "#### Data Collection and Processing", "#### Who are the source data producers?", "### Annotations [optional]", "#### Annotation process", "#### Who are the annotators?", "#### Personal and Sensitive Information", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Dataset Card Authors [optional]", "## Dataset Card Contact" ]

[ 6, 187, 68, 4, 40, 29, 3, 4, 9, 6, 5, 7, 4, 7, 10, 9, 5, 9, 8, 10, 46, 8, 7, 10, 5 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of GOAT-AI/GOAT-70B-Storytelling\n\n\n\nDataset automatically created during the evaluation run of model GOAT-AI/GOAT-70B-Storytelling on the Open LLM Leaderboard.\n\nThe dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2024-01-08T04:02:16.743914(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Out-of-Scope Use## Dataset Structure## Dataset Creation### Curation Rationale### Source Data#### Data Collection and Processing#### Who are the source data producers?### Annotations [optional]#### Annotation process#### Who are the annotators?#### Personal and Sensitive Information## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact" ]

[ "license:apache-2.0", "arxiv:2401.15947", "region:us" ]

[ "2401.15947" ]

[]

[ "##### \n\n\nNews\n----\n\n\n* [2024.01.30] The paper is released.\n* [2024.01.27] Hugging Face demo and all codes & datasets are available now! Welcome to watch this repository for the latest updates.\n\n\nHighlights\n----------\n\n\nMoE-LLaVA shows excellent performance in multi-modal learning.", "### High performance, but with fewer parameters\n\n\n* with just 3B sparsely activated parameters, MoE-LLaVA demonstrates performance comparable to the LLaVA-1.5-7B on various visual understanding datasets and even surpasses the LLaVA-1.5-13B in object hallucination benchmarks.", "### Simple baseline, learning multi-modal interactions with sparse pathways.\n\n\n* With the addition of a simple MoE tuning stage, we can complete the training of MoE-LLaVA on 8 V100 GPUs within 2 days.\n\n\nDemo\n----", "### Gradio Web UI\n\n\nHighly recommend trying out our web demo by the following command, which incorporates all features currently supported by MoE-LLaVA. We also provide online demo in Huggingface Spaces.", "### CLI Inference\n\n\nModel Zoo\n---------\n\n\n\n️ Requirements and Installation\n-------------------------------\n\n\n* Python >= 3.10\n* Pytorch == 2.0.1\n* CUDA Version >= 11.7\n* Transformers == 4.36.2\n* Tokenizers==0.15.1\n* Install required packages:\n\n\n️ Training & Validating\n-----------------------\n\n\nThe training & validating instruction is in URL & URL.\n\n\nCustomizing your MoE-LLaVA\n--------------------------\n\n\nThe instruction is in URL.\n\n\nVisualization\n-------------\n\n\nThe instruction is in URL.\n\n\nAPI\n---\n\n\nWe open source all codes. If you want to load the model (e.g. ) on local, you can use the following code snippets.\n\n\nUsing the following command to run the code.\n\n\nRelated Projects\n----------------\n\n\n* Video-LLaVA This framework empowers the model to efficiently utilize the united visual tokens.\n* LanguageBind An open source five modalities language-based retrieval framework.\n\n\nAcknowledgement\n---------------\n\n\n* LLaVA The codebase we built upon and it is an efficient large language and vision assistant.\n\n\nLicense\n-------\n\n\n* The majority of this project is released under the Apache 2.0 license as found in the LICENSE file.\n* The service is a research preview intended for non-commercial use only, subject to the model License of LLaMA, Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please contact us if you find any potential violation.\n\n\n️ Citation\n----------\n\n\nIf you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.\n\n\nStar History\n------------\n\n\n![Star History](URL\n\n\nContributors\n------------\n\n\n<a href=\"URL\n<img src=\"URL />" ]

[ "TAGS\n#license-apache-2.0 #arxiv-2401.15947 #region-us \n", "##### \n\n\nNews\n----\n\n\n* [2024.01.30] The paper is released.\n* [2024.01.27] Hugging Face demo and all codes & datasets are available now! Welcome to watch this repository for the latest updates.\n\n\nHighlights\n----------\n\n\nMoE-LLaVA shows excellent performance in multi-modal learning.", "### High performance, but with fewer parameters\n\n\n* with just 3B sparsely activated parameters, MoE-LLaVA demonstrates performance comparable to the LLaVA-1.5-7B on various visual understanding datasets and even surpasses the LLaVA-1.5-13B in object hallucination benchmarks.", "### Simple baseline, learning multi-modal interactions with sparse pathways.\n\n\n* With the addition of a simple MoE tuning stage, we can complete the training of MoE-LLaVA on 8 V100 GPUs within 2 days.\n\n\nDemo\n----", "### Gradio Web UI\n\n\nHighly recommend trying out our web demo by the following command, which incorporates all features currently supported by MoE-LLaVA. We also provide online demo in Huggingface Spaces.", "### CLI Inference\n\n\nModel Zoo\n---------\n\n\n\n️ Requirements and Installation\n-------------------------------\n\n\n* Python >= 3.10\n* Pytorch == 2.0.1\n* CUDA Version >= 11.7\n* Transformers == 4.36.2\n* Tokenizers==0.15.1\n* Install required packages:\n\n\n️ Training & Validating\n-----------------------\n\n\nThe training & validating instruction is in URL & URL.\n\n\nCustomizing your MoE-LLaVA\n--------------------------\n\n\nThe instruction is in URL.\n\n\nVisualization\n-------------\n\n\nThe instruction is in URL.\n\n\nAPI\n---\n\n\nWe open source all codes. If you want to load the model (e.g. ) on local, you can use the following code snippets.\n\n\nUsing the following command to run the code.\n\n\nRelated Projects\n----------------\n\n\n* Video-LLaVA This framework empowers the model to efficiently utilize the united visual tokens.\n* LanguageBind An open source five modalities language-based retrieval framework.\n\n\nAcknowledgement\n---------------\n\n\n* LLaVA The codebase we built upon and it is an efficient large language and vision assistant.\n\n\nLicense\n-------\n\n\n* The majority of this project is released under the Apache 2.0 license as found in the LICENSE file.\n* The service is a research preview intended for non-commercial use only, subject to the model License of LLaMA, Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please contact us if you find any potential violation.\n\n\n️ Citation\n----------\n\n\nIf you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.\n\n\nStar History\n------------\n\n\n![Star History](URL\n\n\nContributors\n------------\n\n\n<a href=\"URL\n<img src=\"URL />" ]

[ 23, 71, 71, 56, 47, 389 ]

[ "passage: TAGS\n#license-apache-2.0 #arxiv-2401.15947 #region-us \n##### \n\n\nNews\n----\n\n\n* [2024.01.30] The paper is released.\n* [2024.01.27] Hugging Face demo and all codes & datasets are available now! Welcome to watch this repository for the latest updates.\n\n\nHighlights\n----------\n\n\nMoE-LLaVA shows excellent performance in multi-modal learning.### High performance, but with fewer parameters\n\n\n* with just 3B sparsely activated parameters, MoE-LLaVA demonstrates performance comparable to the LLaVA-1.5-7B on various visual understanding datasets and even surpasses the LLaVA-1.5-13B in object hallucination benchmarks.### Simple baseline, learning multi-modal interactions with sparse pathways.\n\n\n* With the addition of a simple MoE tuning stage, we can complete the training of MoE-LLaVA on 8 V100 GPUs within 2 days.\n\n\nDemo\n----### Gradio Web UI\n\n\nHighly recommend trying out our web demo by the following command, which incorporates all features currently supported by MoE-LLaVA. We also provide online demo in Huggingface Spaces." ]

[ "task_categories:text-generation", "size_categories:10K<n<100K", "source_datasets:epfl-llm/guidelines", "language:en", "license:other", "medical", "health", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #source_datasets-epfl-llm/guidelines #language-English #license-other #medical #health #region-us \n" ]

[ 58 ]

[ "passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #source_datasets-epfl-llm/guidelines #language-English #license-other #medical #health #region-us \n" ]

[ "region:us" ]

[]

[]

[ "# Dataset Card for \"ScienceEval\"\n\nThe dataset is crawled from STEMEZ website under the science category, which contains mostly university-level science questions from math, biology, chemistry, computer science and physics. The crawling script can be found in URL" ]

[ "TAGS\n#region-us \n", "# Dataset Card for \"ScienceEval\"\n\nThe dataset is crawled from STEMEZ website under the science category, which contains mostly university-level science questions from math, biology, chemistry, computer science and physics. The crawling script can be found in URL" ]

[ 6, 62 ]

[ "passage: TAGS\n#region-us \n# Dataset Card for \"ScienceEval\"\n\nThe dataset is crawled from STEMEZ website under the science category, which contains mostly university-level science questions from math, biology, chemistry, computer science and physics. The crawling script can be found in URL" ]