Datasets:

cfahlgren1
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hub-stats

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6835e8703de5738a2e9af4ae	nvidia/PhysicalAI-Autonomous-Vehicles	nvidia	{"extra_gated_heading": "You must agree to the NVIDIA Autonomous Vehicle Dataset License Agreement to access this dataset.", "extra_gated_prompt": "### NVIDIA Autonomous Vehicle Dataset License Agreement\n\nThis NVIDIA Autonomous Vehicle Dataset License Agreement (\"Agreement\") is a legal agreement between you, whether an individual or entity (\"you\") and NVIDIA Corporation with address 2788 San Tomas Expressway, Santa Clara, California 95051 (\"NVIDIA\") and governs the use of certain datasets, including any annotations and metadata attached to the datasets, provided by NVIDIA (\"Dataset\").\n\nThis Agreement can be accepted only by an adult of legal age of majority in the country in which the Dataset are used.\n\nIf you don't have the required age or authority to accept this Agreement or if you don't accept all the terms and conditions of this Agreement, do not use the Dataset.\n\nYou agree to use the Dataset only for purposes expressly permitted by this Agreement and in accordance with any applicable law or regulation in the relevant jurisdictions.\n\n1. License Grant. Subject to the terms of this Agreement, NVIDIA grants you a non-exclusive, revocable, non-transferable, non-sublicensable (except as expressly granted in Sections 1 and 2 of this Agreement, license to download, use, modify, and reproduce the Dataset, in each case solely for your internal development of autonomous vehicles and automated driving assisted systems using NVIDIA technology (\"Purpose\"). NVIDIA may from time to time update the Dataset. If requested by NVIDIA, you will use the updated version of any such Dataset and delete any prior versions upon NVIDIA's written request.\n\n2. Authorized Users. You may allow your Affiliates' employees and contractors (all such users collectively \"Authorized Users\") to access and use the Dataset from your secure network for the Purpose on your behalf. You are responsible for the compliance with the terms of this Agreement by your authorized users. Any act or omission by your authorized users that if committed by you would constitute a breach of this Agreement will be deemed to constitute a breach of this Agreement. \"Affiliates\" means an entity that owns or controls, is owned or controlled by, or is under common ownership or control with you, where \"control\" is the possession, directly or indirectly, of the power to direct or cause the direction of the management and policies of an entity, whether through ownership of voting securities, by contract or otherwise.\n\n3. Confidentiality. You agree that you will not use, nor authorize others to use, NVIDIA Confidential Information, other than for the Purpose, and that you will not disclose NVIDIA Confidential Information to any third party, except to Authorized Users under this Agreement that have a need to know such Confidential Information for the Purpose, provided that each such recipient is subject to a written agreement that includes confidentiality obligations consistent with the terms. You will protect the NVIDIA Confidential Information with at least the same degree of care that you use to protect your own similar confidential and proprietary information, but no less than a reasonable degree of care, including any appropriate technical, organizational and contractual measures. \"Confidential Information\" means the Dataset including its features and functionality, output, and any results of benchmarking or other competitive analysis or regression or performance data relating to the Dataset.\n\n4. Limitations. Your license to use the Dataset is restricted as follows:\n\n4.1 You will not use the Dataset for the purpose of any surveillance program, service and/or product of public authorities, corporations and/or citizens that monitors the behavior of an individual person or groups of persons in any unethical manner. You will not use the Dataset to directly or indirectly enable law enforcement or any public authority to enforce any rules or regulations including any road traffic laws.\n\n4.2 You may not change or remove copyright or other proprietary notices in the Dataset.\n\n4.3 The rights granted to you in Section 1 and 2 are for the Purpose only. You may not use the Dataset for any other purpose.\n\n4.4 You may not identify or attempt to identify or profile any individual (including by way of license plate numbers) in the Dataset or de-anonymize or attempt to de-anonymize any Dataset. This includes prohibition against processing of license plate numbers for purpose of tracking or collecting data about a vehicle over time and across different frames.\n\n4.5 You may not: (a) infer, measure, detect or otherwise label the race, ethnicity, gender, age or health (or any other sensitive attributes) of individuals in the Dataset, (b) perform biometric processing of the Dataset, (c) analyze faces, gazes, eye movements, gait, or body movements to uniquely identify persons, or (d) use the Dataset to develop or evaluate any identity, emotion recognition technology or social scoring technology.\n\n4.6 You may not create derivative works of the Dataset, sell, rent, sublicense, transfer, distribute, embed, or host the Dataset (in whole or in part), or otherwise make the Dataset (in whole or in part) available to others.\n\n4.7 You may not bypass, disable or circumvent any technical limitation, encryption, security, digital rights management or authentication mechanism relating to the Dataset.\n\n4.8 You must keep track of any copies of the Dataset. You will keep track of where the Dataset or portions of it are stored to ensure these restrictions follow such Dataset.\n\n4.9 While NVIDIA has exercised reasonable efforts to anonymize the Dataset, you must cooperate with NVIDIA to honor any data subject rights where applicable. You will delete the Dataset upon written notice by NVIDIA and you will promptly notify NVIDIA at https://www.nvidia.com/en-us/support/submit-security-vulnerability/ if you notice that any portion of the Dataset is not sufficiently anonymized.\n\n5. AI Ethics.\n\n5.1 Ethical Use. NVIDIA is committed to safety, trust and transparency in AI development. NVIDIA encourages you to (a) ensure that the product or service you develop, use, offer as a service or distribute meets the legal and ethical requirements of the relevant industry or use case, (b) take reasonable measures to address unintended bias and to mitigate harm to others, including underrepresented or vulnerable groups, and (c) inform users of the nature and limitations of the product or service.\n\n5.2 Prohibited Uses. NVIDIA expressly prohibits the use of its products or services for any purpose in violation of applicable law or regulation, including but not limited to (a) illegal surveillance, (b) illegal collection or processing of biometric information without the consent of the subject where required under applicable law, or (c) illegal harassment, abuse, threatening or bullying of individuals or groups of individuals or intentionally misleading or deceiving others.\n\n6. Ownership. The Dataset, including all intellectual property rights, is and will remain the sole and exclusive property of NVIDIA or its licensors. Except as expressly granted in this Agreement, (i) NVIDIA reserves all rights, interests and remedies in connection with the Dataset, and (ii) no other license or right is granted to you by implication, estoppel or otherwise.\n\n7. Feedback. You may, but are not obligated to, provide suggestions, requests, fixes, modifications, enhancements, or other feedback regarding or in connection with your use of the Dataset (collectively, \"Feedback\"). Feedback, even if designated as confidential by you, will not create any confidentiality obligation for NVIDIA or its affiliates. If you provide Feedback, you hereby grant NVIDIA, its affiliates and its designees a nonexclusive, perpetual, irrevocable, sublicensable, worldwide, royalty-free, fully paid-up and transferable license, under your intellectual property rights, to publicly perform, publicly display, reproduce, use, make, have made, sell, offer for sale, distribute (through multiple tiers of distribution), import, create derivative works of and otherwise commercialize and exploit the Feedback at NVIDIA's discretion.\n\n8. Term and Termination. This Agreement expires twelve (12) months after the date of initial delivery or download of the Dataset. This Agreement will automatically terminate (a) if you fail to comply with any of the terms in this Agreement or (b) if you commence or participate in any legal proceeding against NVIDIA with respect to the Dataset. Upon termination, you must stop using and destroy all copies of the Dataset. Upon written request, you will certify in writing that you have complied with your commitments under this section. All provisions will survive termination, except for the licenses granted to you.\n\n9. Disclaimer of Warranties. THE DATASET IS PROVIDED BY NVIDIA AS-IS AND WITH ALL FAULTS. TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, NVIDIA DISCLAIMS ALL WARRANTIES AND REPRESENTATIONS OF ANY KIND, WHETHER EXPRESS, IMPLIED OR STATUTORY, RELATING TO OR ARISING UNDER THIS AGREEMENT, INCLUDING, WITHOUT LIMITATION, THE WARRANTIES OF TITLE, NONINFRINGEMENT, MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, USAGE OF TRADE AND COURSE OF DEALING.\n\n10. Limitations of Liability. TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE, WILL NVIDIA BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES OF ANY TYPE ARISING OUT OF OR AS A RESULT OF THIS AGREEMENT OR THE USE OR INABILITY TO USE THE SOFTWARE (INCLUDING BUT NOT LIMITED TO DAMAGES FOR LOSS OF GOODWILL, WORK STOPPAGE, COMPUTER FAILURE OR MALFUNCTION, OR ANY AND ALL OTHER DAMAGES OR LOSSES), EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.\n\n11. Governing Law and Jurisdiction. This Agreement will be governed in all respects by the laws of the United States and the laws of the State of Delaware, without regard to conflict of laws principles or the United Nations Convention on Contracts for the International Sale of Goods. The state and federal courts residing in Santa Clara County, California will have exclusive jurisdiction over any dispute or claim arising out of or related to this Agreement, and the parties irrevocably consent to personal jurisdiction and venue in those courts; except that either party may apply for injunctive remedies or an equivalent type of urgent legal relief in any jurisdiction.\n\n12. Indemnity. You agree to defend, indemnify and hold harmless NVIDIA and its affiliates, and their respective employees, contractors, agents, officers and directors, from and against any and all claims, damages, obligations, losses, liabilities, costs or debt, fines, restitutions and expenses (including but not limited to attorney's fees and costs incident to establishing the right of indemnification) arising out of or related to your use of the Dataset outside of the scope of this Agreement, or not in compliance with its terms.\n\n13. General.\n13.1 No Assignment. NVIDIA may assign, delegate or transfer its rights or obligations under this Agreement by any means or operation of law. You may not, without NVIDIA's prior written consent, assign, delegate or transfer any of your rights or obligations under this Agreement by any means or operation of law, and any attempt to do so is null and void.\n\n13.2 No Waiver. No waiver of any term of the Agreement will be deemed a further or continuing waiver of such term or any other term, and NVIDIA's failure to assert any right or provision under the Agreement will not constitute a waiver of such right or provision.\n\n13.3 Trade and Compliance. You agree to with all applicable export, import, trade and economic sanctions laws and regulations, as amended, including without limitation U.S. Export Administration Regulations and Office of Foreign Assets Control regulations. Any violation of such laws by you will void any warranty for the associated products and technologies. You confirm (a) your understanding that export or reexport of certain NVIDIA products or technologies may require a license or other approval from appropriate authorities and (b) that you will not export or reexport any products or technology, directly or indirectly, without first obtaining any required license or other approval from appropriate authorities, (i) to any countries that are subject to any U.S. or local export restrictions (currently including, but not necessarily limited to, Belarus, Cuba, Iran, North Korea, Russia, Syria, the Region of Crimea, Donetsk People's Republic Region and Luhansk People's Republic Region); (ii) to any end-user who it knows or has reason to know will utilize them in the design, development or production of nuclear, chemical or biological weapons, missiles, rocket systems, unmanned air vehicles capable of a maximum range of at least 300 kilometers, regardless of payload, or intended for military end-use, or any weapons of mass destruction; (iii) to any end-user who has been prohibited from participating in the U.S. or local export transactions by any governing authority; or (iv) to any known military or military-intelligence end-user or for any known military or military-intelligence end-use in accordance with U.S. trade compliance laws and regulations..\n\n13.4 Notices. Please direct your legal notices or other correspondence to NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, California 95051, United States of America, Attention: Legal Department, with a copy emailed to [email protected]. If NVIDIA needs to contact you about the Dataset, you consent to receive the notices by email and agree that such notices will satisfy any legal communication requirements.\n\n13.5 Force Majeure. Neither party will be liable during any period where an event or circumstance prevents or delays that party from performing its obligations under this Agreement and that event or circumstance: (i) is not within the reasonable control of that party and is not the result of that party's negligence, and (ii) cannot be overcome or avoided by that party using reasonably diligent efforts.\n\n13.6 Severability and Amendment. If a court of competent jurisdiction rules that a provision of this Agreement is unenforceable, that provision will be deemed modified to the extent necessary to make it enforceable and the remainder of this Agreement will continue in full force and effect. Any amendment to this Agreement must be in writing and signed by authorized representatives of both parties.\n\n13.7 Independent Contractors. The parties are independent contractors, and this Agreement does not create a joint venture, partnership, agency or other form of business association between the parties. Neither party will have the power to bind the other party or incur any obligation on its behalf without the other party's prior written consent.\n\n13.8 Construction. The headings in the Agreement are included solely for convenience and are not intended to affect the meaning or interpretation of the Agreement. As required by the context of the Agreement, the singular of a term includes the plural and vice versa.\n\n13.9 Entire Agreement. Regarding the subject matter of this Agreement, the parties agree that (i) this Agreement constitutes the entire and exclusive agreement between the parties and supersedes all prior and contemporaneous communications and (ii) any additional or different terms or conditions, whether contained in purchase orders, order acknowledgments, invoices or otherwise, will not be binding and are null and void.", "extra_gated_button_content": "I accept the terms of the NVIDIA Autonomous Vehicle Dataset License Agreement", "license": "other", "license_name": "nvidia-av-dataset", "license_link": "https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles/blob/main/LICENSE.pdf", "viewer": false}	false	auto	2025-10-28T20:31:51	92	92	false	0c8e5b7813562ab6e907e55db6ead3351922073f	PhysicalAI Autonomous Vehicles Dataset Description The PhysicalAI-Autonomous-Vehicles dataset provides one of the largest, most geographically diverse collections of multi-sensor data empowering AV researchers to build the next generation of Physical AI based end-to-end driving systems. This dataset has a total of 1727 hours of driving recorded from planned data-collection drives in 25 countries and 2500+ cities. The data captures diverse traffic, weather conditions… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles.	1,363	1,376	[ "license:other", "region:us" ]	2025-05-27T16:29:36	null	null
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"data_files": [{"split": "train", "path": "data/viwiki/"}]}, {"config_name": "vls", "data_files": [{"split": "train", "path": "data/vlswiki/"}]}, {"config_name": "vo", "data_files": [{"split": "train", "path": "data/vowiki/"}]}, {"config_name": "war", "data_files": [{"split": "train", "path": "data/warwiki/"}]}, {"config_name": "wa", "data_files": [{"split": "train", "path": "data/wawiki/"}]}, {"config_name": "wo", "data_files": [{"split": "train", "path": "data/wowiki/"}]}, {"config_name": "wuu", "data_files": [{"split": "train", "path": "data/wuuwiki/"}]}, {"config_name": "xal", "data_files": [{"split": "train", "path": "data/xalwiki/"}]}, {"config_name": "xh", "data_files": [{"split": "train", "path": "data/xhwiki/"}]}, {"config_name": "xmf", "data_files": [{"split": "train", "path": "data/xmfwiki/"}]}, {"config_name": "yi", "data_files": [{"split": "train", "path": "data/yiwiki/"}]}, {"config_name": "yo", "data_files": [{"split": "train", "path": "data/yowiki/"}]}, {"config_name": "za", "data_files": [{"split": "train", "path": "data/zawiki/"}]}, {"config_name": "zea", "data_files": [{"split": "train", "path": "data/zeawiki/"}]}, {"config_name": "zgh", "data_files": [{"split": "train", "path": "data/zghwiki/"}]}, {"config_name": "zh_classical", "data_files": [{"split": "train", "path": "data/zh_classicalwiki/"}]}, {"config_name": "zh_min_nan", "data_files": [{"split": "train", "path": "data/zh_min_nanwiki/"}]}, {"config_name": "zh_yue", "data_files": [{"split": "train", "path": "data/zh_yuewiki/"}]}, {"config_name": "zh", "data_files": [{"split": "train", "path": "data/zhwiki/"}]}, {"config_name": "zu", "data_files": [{"split": "train", "path": "data/zuwiki/*"}]}]}	false	False	2025-10-22T11:02:22	177	81	false	8bd13e72e6a002407649b3e898535f42ceb1aeb9	This is an updated and better extracted version of the wikimedia/Wikipedia dataset originally released in 2023. We carefully parsed Wikipedia HTML dumps from August of 2025 covering 325 languages. This dataset: fully renders templates as it was extracted from HTML and not markdown dumps removes redirects, disambiguation, and other non main article pages includes detailed metadata such as page ID, title, last modified date, wikidate ID, version and markdown version of the textpreserves… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/finewiki.	10,114	10,114	[ "task_categories:text-generation", "license:cc-by-sa-4.0", "license:gfdl", "size_categories:10M<n<100M", "format:parquet", "modality:tabular", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	2025-10-13T12:22:30	null	null
685584aa0077eddadd99b004	neulab/agent-data-collection	neulab	{"language": ["en"], "task_categories": ["text-generation"], "configs": [{"config_name": "agenttuning_os", "data_files": [{"split": "train", "path": "agenttuning_os/agenttuning_os.json"}]}, {"config_name": "agenttuning_kg", "data_files": [{"split": "train", "path": "agenttuning_kg/agenttuning_kg.json"}]}, {"config_name": "openhands", "data_files": [{"split": "train", "path": "openhands/openhands.json"}]}, {"config_name": "SWE-Gym_OpenHands-Sampled-Trajectories", "data_files": [{"split": "train", "path": "SWE-Gym_OpenHands-Sampled-Trajectories/SWE-Gym_OpenHands-Sampled-Trajectories.json"}]}, {"config_name": "nebius_SWE-agent-trajectories", "data_files": [{"split": "train", "path": "nebius_SWE-agent-trajectories/nebius_SWE-agent-trajectories.json"}]}, {"config_name": "mind2web", "data_files": [{"split": "train", "path": "mind2web/mind2web.json"}]}, {"config_name": "synatra", "data_files": [{"split": "train", "path": "synatra/synatra.json"}]}, {"config_name": "nnetnav-live", "data_files": [{"split": "train", "path": "nnetnav-live/nnetnav-live.json"}]}, {"config_name": "agenttuning_alfworld", "data_files": [{"split": "train", "path": "agenttuning_alfworld/agenttuning_alfworld.json"}]}, {"config_name": "nnetnav-wa", "data_files": [{"split": "train", "path": "nnetnav-wa/nnetnav-wa.json"}]}, {"config_name": "codeactinstruct", "data_files": [{"split": "train", "path": "codeactinstruct/codeactinstruct.json"}]}, {"config_name": "SWE-smith_5kTrajectories", "data_files": [{"split": "train", "path": "SWE-smith_5kTrajectories/SWE-smith_5kTrajectories.json"}]}, {"config_name": "orca_agentinstruct", "data_files": [{"split": "train", "path": "orca_agentinstruct/orca_agentinstruct.json"}]}, {"config_name": "agenttuning_db", "data_files": [{"split": "train", "path": "agenttuning_db/agenttuning_db.json"}]}, {"config_name": "agenttuning_mind2web", "data_files": [{"split": "train", "path": "agenttuning_mind2web/agenttuning_mind2web.json"}]}, {"config_name": "code_feedback", "data_files": [{"split": "train", "path": "code_feedback/code_feedback.json"}]}, {"config_name": "agenttuning_webshop", "data_files": [{"split": "train", "path": "agenttuning_webshop/agenttuning_webshop.json"}]}, {"config_name": "go-browse-wa", "data_files": [{"split": "train", "path": "go-browse-wa/go-browse-wa.json"}]}]}	false	False	2025-09-09T22:04:42	40	37	false	be642a9410da2dbba67ae1f476beb41059487c83	Dataset Card for OpenHands Agent Logs This dataset consists of multi-turn dialogues between a simulated human and an LLM-based agent interacting in a virtual operating system environment. Each conversation involves the agent reasoning about and solving command-line tasks through execute_bash and related actions. Dataset Format Each file in the dataset is a .json file, structured as a list of instances. Each instance contains: id: A unique identifier for the interaction… See the full description on the dataset page: https://huggingface.co/datasets/neulab/agent-data-collection.	2,940	4,851	[ "task_categories:text-generation", "language:en", "size_categories:100K<n<1M", "modality:text", "region:us" ]	2025-06-20T15:56:26	null	null
68ecf696b845b2eff7934675	nvidia/Nemotron-VLM-Dataset-v2	nvidia	{"license": "cc-by-4.0", "task_categories": ["visual-question-answering", "image-text-to-text", "video-text-to-text"], "pretty_name": "Nemotron-VLM-Dataset v2", "size_categories": ["n>1T"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "messages", "sequence": {"struct": [{"name": "role", "dtype": "string"}, {"name": "content", "sequence": {"struct": [{"name": "type", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "video", "dtype": "string"}, {"name": "audio", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "pdf", "dtype": "string"}, {"name": "page_number", "dtype": "int32"}, {"name": "url", "dtype": "string"}, {"name": "yt", "dtype": "string"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "format", "dtype": "string"}, {"name": "mode", "dtype": "string"}, {"name": "video_duration", "dtype": "float32"}, {"name": "video_num_frames", "dtype": "int32"}, {"name": "video_fps", "dtype": "float32"}, {"name": "video_width", "dtype": "int32"}, {"name": "video_height", "dtype": "int32"}]}]}}]}}]}, "configs": [{"config_name": "wiki_de", "data_files": [{"split": "train", "path": "wiki_de/wiki_de.jsonl"}]}, {"config_name": "wiki_en", "data_files": [{"split": "train", "path": "wiki_en/wiki_en.jsonl"}]}, {"config_name": "wiki_es", "data_files": [{"split": "train", "path": "wiki_es/wiki_es.jsonl"}]}, {"config_name": "wiki_fr", "data_files": [{"split": "train", "path": "wiki_fr/wiki_fr.jsonl"}]}, {"config_name": "wiki_it", "data_files": [{"split": "train", "path": "wiki_it/wiki_it.jsonl"}]}, {"config_name": "wiki_ja", "data_files": [{"split": "train", "path": "wiki_ja/wiki_ja.jsonl"}]}, {"config_name": "wiki_ko", "data_files": [{"split": "train", "path": "wiki_ko/wiki_ko.jsonl"}]}, {"config_name": "wiki_nl", "data_files": [{"split": "train", "path": "wiki_nl/wiki_nl.jsonl"}]}, {"config_name": "wiki_pt", "data_files": [{"split": "train", "path": "wiki_pt/wiki_pt.jsonl"}]}, {"config_name": "wiki_zh", "data_files": [{"split": "train", "path": "wiki_zh/wiki_zh.jsonl"}]}, {"config_name": "oi_bbox_3", "data_files": [{"split": "train", "path": "oi_bbox_3/oi_bbox_3.jsonl"}]}, {"config_name": "tabmwp_cot", "data_files": [{"split": "train", "path": "tabmwp_cot/tabmwp_cot.jsonl"}]}, {"config_name": "sparsetables", "data_files": [{"split": "train", "path": "sparsetables/sparsetables.jsonl"}]}, {"config_name": "mulberry_cot_1", "data_files": [{"split": "train", "path": "mulberry_cot_1/mulberry_cot_1.jsonl"}]}, {"config_name": "mulberry_cot_2", "data_files": [{"split": "train", "path": "mulberry_cot_2/mulberry_cot_2.jsonl"}]}, {"config_name": "llava_cot_100k", "data_files": [{"split": "train", "path": "llava_cot_100k/llava_cot_100k.jsonl"}]}, {"config_name": "geomverse_cot", "data_files": [{"split": "train", "path": "geomverse_cot/geomverse_cot.jsonl"}]}, {"config_name": "mapqa_cot", "data_files": [{"split": "train", "path": "mapqa_cot/mapqa_cot.jsonl"}]}, {"config_name": "plotqa_cot", "data_files": [{"split": "train", "path": "plotqa_cot/plotqa_cot.jsonl"}]}, {"config_name": "visual7w_telling_cot", "data_files": [{"split": "train", "path": "visual7w_telling_cot/visual7w_telling_cot.jsonl"}]}, {"config_name": "visual_web_instruct_cot", "data_files": [{"split": "train", "path": "visual_web_instruct_cot/visual_web_instruct_cot.jsonl"}]}, {"config_name": "docvqa_cot", "data_files": [{"split": "train", "path": "docvqa_cot/docvqa_cot.jsonl"}]}, {"config_name": "chartqa_cot", "data_files": [{"split": "train", "path": "chartqa_cot/chartqa_cot.jsonl"}]}, {"config_name": "infographicsvqa_cot", "data_files": [{"split": "train", "path": "infographicsvqa_cot/infographicsvqa_cot.jsonl"}]}, {"config_name": "unigeo_cot", "data_files": [{"split": "train", "path": "unigeo_cot/unigeo_cot.jsonl"}]}, {"config_name": "nights_cot", "data_files": [{"split": "train", "path": "nights_cot/nights_cot.jsonl"}]}, {"config_name": "mantis_instruct_cot", "data_files": [{"split": "train", "path": "mantis_instruct_cot/mantis_instruct_cot.jsonl"}]}, {"config_name": "fintabnet_cot", "data_files": [{"split": "train", "path": "fintabnet_cot/fintabnet_cot.jsonl"}]}, {"config_name": "hiertext", "data_files": [{"split": "train", "path": "hiertext/hiertext.jsonl"}]}, {"config_name": "nextqa", "data_files": [{"split": "train", "path": "nextqa/nextqa.jsonl"}]}, {"config_name": "clevrer", "data_files": [{"split": "train", "path": "clevrer/clevrer.jsonl"}]}, {"config_name": "ego_exo_learn", "data_files": [{"split": "train", "path": "ego_exo_learn/ego_exo_learn.jsonl"}]}, {"config_name": "kinetics_k710", "data_files": [{"split": "train", "path": "kinetics_k710/kinetics_k710.jsonl"}]}, {"config_name": "perception_test_1", "data_files": [{"split": "train", "path": "perception_test_1/perception_test_1.jsonl"}]}, {"config_name": "perception_test_2", "data_files": [{"split": "train", "path": "perception_test_2/perception_test_2.jsonl"}]}, {"config_name": "activity_net_1", "data_files": [{"split": "train", "path": "activity_net_1/activity_net_1.jsonl"}]}, {"config_name": "hacs", "data_files": [{"split": "train", "path": "hacs/hacs.jsonl"}]}, {"config_name": "hirest_1", "data_files": [{"split": "train", "path": "hirest_1/hirest_1.jsonl"}]}, {"config_name": "activity_net_2", "data_files": [{"split": "train", "path": "activity_net_2/activity_net_2.jsonl"}]}, {"config_name": "hirest_2", "data_files": [{"split": "train", "path": "hirest_2/hirest_2.jsonl"}]}, {"config_name": "youcook2_1", "data_files": [{"split": "train", "path": "youcook2_1/youcook2_1.jsonl"}]}, {"config_name": "youcook2_2", "data_files": [{"split": "train", "path": "youcook2_2/youcook2_2.jsonl"}]}, {"config_name": "breakfast_actions", "data_files": [{"split": "train", "path": "breakfast_actions/breakfast_actions.jsonl"}]}, {"config_name": "ccpdf_multipage_1", "data_files": [{"split": "train", "path": "ccpdf_multipage_1/ccpdf_multipage_1.jsonl"}]}, {"config_name": "ccpdf_multipage_2", "data_files": [{"split": "train", "path": "ccpdf_multipage_2/ccpdf_multipage_2.jsonl"}]}, {"config_name": "perception_test_cot", "data_files": [{"split": "train", "path": "perception_test_cot/perception_test_cot.jsonl"}]}]}	false	False	2025-10-30T13:41:39	31	31	false	fca8efa9c6efa1df1e23e5598bfb48d24b203ed4	Nemotron-VLM-Dataset v2 Versions Date Commit Changes 2025-10-28 head Initial Release Dataset Description Following up on Llama Nemotron VLM Dataset V1 with 3 million samples, we are releasing the Nemotron VLM Dataset V2 with almost three times as many high-quality samples. This time, our focus was on three main areas: Adding new data modalities like video, expanding our chain-of-thought reasoning data, and providing the community with a… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-VLM-Dataset-v2.	1,175	1,175	[ "task_categories:visual-question-answering", "task_categories:image-text-to-text", "task_categories:video-text-to-text", "license:cc-by-4.0", "size_categories:1M<n<10M", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us" ]	2025-10-13T12:54:46	null	null
68fd233979e4e4cfdd0aa2bf	AlicanKiraz0/Turkish-SFT-Dataset-v1.0	AlicanKiraz0	{"license": "mit", "task_categories": ["text-classification", "question-answering", "text-generation"], "language": ["tr"], "tags": ["turkish"], "size_categories": ["1K<n<10K"]}	false	False	2025-10-26T17:22:53	31	31	false	5a5a852ab9067866d72ed92381205518feaae5fb	Turkish-SFT-Dataset-v1.01 Repo: AlicanKiraz0/Turkish-SFT-Dataset-v1.0Sürüm: v1.01Lisans: MITBiçim: jsonl (kolonlar: system, user, assistant)Boyut: ~5500 satır ve satır başına 3.000–4.500 token/satır (≈ 20M+ token)Dil: Türkçe (tr)Görevler: talimat izleme, SFT, muhakeme, güvenli ret, uzun-bağlam ve araç kullanım bilinci 🔎 Özet Bu veri kümesi, Türkçe Denetimli İnce Ayar (SFT) için tasarlanmış, yüksek kaliteli ve uzun çıktılar içeren örneklerden oluşur. İçerik 12 ana… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/Turkish-SFT-Dataset-v1.0.	516	516	[ "task_categories:text-classification", "task_categories:question-answering", "task_categories:text-generation", "language:tr", "license:mit", "size_categories:1K<n<10K", "format:json", "modality:text", "library:datasets", "library:pandas", "library:mlcroissant", "library:polars", "region:us", "turkish" ]	2025-10-25T19:21:29	null	null
68d51eeacf1d80834c0dd71c	karpathy/fineweb-edu-100b-shuffle	karpathy	{"license": "odc-by"}	false	False	2025-09-25T11:25:20	121	29	false	4c8f30d6756da75362432a4d5569e1b229263b71	null	48,957	48,957	[ "license:odc-by", "size_categories:10M<n<100M", "format:parquet", "modality:text", "library:datasets", "library:dask", "library:mlcroissant", "library:polars", "region:us" ]	2025-09-25T10:52:26	null	null
6887a1762cef2ff976d3eeeb	HuggingFaceM4/FineVision	HuggingFaceM4	"{\"dataset_info\": [{\"config_name\": \"CoSyn_400k_chart\", \"features\": [{\"name\": \"images\", \(...TRUNCATED)	false	False	2025-10-21T10:12:56	425	26	false	3c380a731a3429c1d04693d6ec16d7e683def84c	"\n\t\n\t\t\n\t\tFine Vision\n\t\n\n\nFineVision is a massive collection of datasets with 17.3M imag(...TRUNCATED)	247,708	573,640	["size_categories:10M<n<100M","format:parquet","modality:image","modality:text","library:datasets","(...TRUNCATED)	2025-07-28T16:12:38	null	null
68e4da65291b1053541ba85d	nick007x/github-code-2025	nick007x	"{\"license\": \"mit\", \"modalities\": [\"Text\"], \"formats\": [\"parquet\"], \"size\": \"10M - 10(...TRUNCATED)	false	False	2025-10-15T15:38:10	89	25	false	28c34abfd94bcb10af2b41c83723fe9b5473e645	"\n\t\n\t\t\n\t\t🚀 GitHub Code 2025: The Clean Code Manifesto\n\t\n\n\nA meticulously curated dat(...TRUNCATED)	14,116	14,116	["license:mit","size_categories:100M<n<1B","format:parquet","modality:text","library:datasets","libr(...TRUNCATED)	2025-10-07T09:16:21	null	null
68bb43410b54503c335cb3d8	HuggingFaceFW/finepdfs	HuggingFaceFW	"{\"license\": \"odc-by\", \"task_categories\": [\"text-generation\"], \"pretty_name\": \"\\ud83d\\u(...TRUNCATED)	false	False	2025-09-08T13:36:38	642	22	false	909850e0d03f191dced336f4e07d85f0fd1b9528	"\n\nLiberating 3T of the finest tokens from PDFs\n\n\n\t\n\t\t\n\t\tWhat is this?\n\t\n\nAs we run (...TRUNCATED)	50,831	150,695	["task_categories:text-generation","language:aai","language:aak","language:aau","language:aaz","lang(...TRUNCATED)	2025-09-05T20:08:33	null	null
68f208fa0a6494973cb8fd66	RUC-DataLab/DataScience-Instruct-500K	RUC-DataLab	"{\"license\": \"mit\", \"datasets\": [\"RUC-DataLab/DataScience-Instruct-500K\"], \"pipeline_tag\":(...TRUNCATED)	false	False	2025-10-21T03:06:52	36	22	false	f2dd0a62927f49f0a1d2465e32dc4072910e7c89	"\n\n\n\n\n\t\n\t\t\n\t\tDeepAnalyze: Agentic Large Language Models for Autonomous Data Science\n\t\(...TRUNCATED)	5,199	5,199	["license:mit","size_categories:10K<n<100K","format:json","modality:tabular","modality:text","librar(...TRUNCATED)	2025-10-17T09:14:34	null	null

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Changelog

NEW Changes July 25th

added baseModels field to models which shows the models that the user tagged as base models for that model

Example:

{
  "models": [
    {
      "_id": "687de260234339fed21e768a",
      "id": "Qwen/Qwen3-235B-A22B-Instruct-2507"
    }
  ],
  "relation": "quantized"
}

NEW Changes July 9th

Fixed issue with gguf column with integer overflow causing import pipeline to be broken over a few weeks ✅

NEW Changes Feb 27th

Added new fields on the models split: downloadsAllTime, safetensors, gguf
Added new field on the datasets split: downloadsAllTime
Added new split: papers which is all of the Daily Papers

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