# AI Training Datasets: Structure, Licenses and Files Source: https://savrn.com/datasets Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub # AI Training Datasets Each dataset with its card, its structure (every split, row count and column type), its files, its license, and the models that disclose training on it. Models are only as good as the data behind them, and this section lists the open datasets that models in our catalog are trained and tested on: web corpora, benchmarks, speech and image collections, and robot demonstrations. Each dataset page shows its license, its size and the models that link to it, so you can trace a model back to its data. Check the license before training anything you intend to sell. A permissive dataset license does not always cover the rights in the text, images or recordings it collects, especially for personal data such as voices and faces. 12,870Models 3,655Datasets 291Papers 7,691Publishers 12,980Sourced relationships Updated 2026-10-24 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Datasets Task - [Text generation142](https://savrn.com/datasets?task=Text+generation) - [Robotics123](https://savrn.com/datasets?task=Robotics) - [Text classification52](https://savrn.com/datasets?task=Text+classification) - [Other51](https://savrn.com/datasets?task=Other) - [Text retrieval44](https://savrn.com/datasets?task=Text+retrieval) - [Question answering40](https://savrn.com/datasets?task=Question+answering) - [Speech recognition35](https://savrn.com/datasets?task=Speech+recognition) - [Image to text30](https://savrn.com/datasets?task=Image+to+text) - [Time series forecasting23](https://savrn.com/datasets?task=Time+series+forecasting) - [Tabular classification22](https://savrn.com/datasets?task=Tabular+classification) - [Image classification18](https://savrn.com/datasets?task=Image+classification) - [Visual question answering17](https://savrn.com/datasets?task=Visual+question+answering) - [Object detection16](https://savrn.com/datasets?task=Object+detection) - [Text to speech14](https://savrn.com/datasets?task=Text+to+speech) - [Image to image13](https://savrn.com/datasets?task=Image+to+image) - [Feature extraction12](https://savrn.com/datasets?task=Feature+extraction) - [Image to 3d10](https://savrn.com/datasets?task=Image+to+3d) - [Translation10](https://savrn.com/datasets?task=Translation) - [Image segmentation9](https://savrn.com/datasets?task=Image+segmentation) - [Video classification9](https://savrn.com/datasets?task=Video+classification) - [Audio classification8](https://savrn.com/datasets?task=Audio+classification) - [Audio to audio7](https://savrn.com/datasets?task=Audio+to+audio) - [Reinforcement learning7](https://savrn.com/datasets?task=Reinforcement+learning) - [Text to image7](https://savrn.com/datasets?task=Text+to+image) License - [other319](https://savrn.com/datasets?license=other) - [apache-2.0267](https://savrn.com/datasets?license=apache-2.0) - [mit261](https://savrn.com/datasets?license=mit) - [cc-by-4.0260](https://savrn.com/datasets?license=cc-by-4.0) - [cc-by-nc-4.083](https://savrn.com/datasets?license=cc-by-nc-4.0) - [cc0-1.031](https://savrn.com/datasets?license=cc0-1.0) - [cc-by-sa-4.030](https://savrn.com/datasets?license=cc-by-sa-4.0) - [odc-by27](https://savrn.com/datasets?license=odc-by) - [cc-by-nc-sa-4.025](https://savrn.com/datasets?license=cc-by-nc-sa-4.0) - [unknown16](https://savrn.com/datasets?license=unknown) - [cc-by-sa-3.07](https://savrn.com/datasets?license=cc-by-sa-3.0) - [odbl6](https://savrn.com/datasets?license=odbl) - [gpl-3.05](https://savrn.com/datasets?license=gpl-3.0) - [cc-by-3.04](https://savrn.com/datasets?license=cc-by-3.0) - [cc3](https://savrn.com/datasets?license=cc) - [cc-by-nc-nd-4.03](https://savrn.com/datasets?license=cc-by-nc-nd-4.0) - [gpl3](https://savrn.com/datasets?license=gpl) - [cc-by-2.02](https://savrn.com/datasets?license=cc-by-2.0) - [cc-by-nc-sa-3.02](https://savrn.com/datasets?license=cc-by-nc-sa-3.0) - [cc-by-nd-4.02](https://savrn.com/datasets?license=cc-by-nd-4.0) - [cdla-sharing-1.02](https://savrn.com/datasets?license=cdla-sharing-1.0) - [agpl-3.01](https://savrn.com/datasets?license=agpl-3.0) - [bsl-1.01](https://savrn.com/datasets?license=bsl-1.0) - [c-uda1](https://savrn.com/datasets?license=c-uda) Access - [Publicly accessible3,354](https://savrn.com/datasets?access=open_access) - [Access requested at publisher301](https://savrn.com/datasets?access=gated) Publisher - [Benjamin J Barber36](https://savrn.com/datasets?publisher=endomorphosis) - [Chang Yu24](https://savrn.com/datasets?publisher=g1n0st) - [Ji Xie24](https://savrn.com/datasets?publisher=sanaka87) - [Justicedao19](https://savrn.com/datasets?publisher=justicedao) - [Beifengdo17](https://savrn.com/datasets?publisher=beifengdo) - [Yaseen15](https://savrn.com/datasets?publisher=moaibo) - [Claude Mercer13](https://savrn.com/datasets?publisher=corecds) - [LeDuy13](https://savrn.com/datasets?publisher=duyle2408) - [Nguyen13](https://savrn.com/datasets?publisher=icdn11) - [Nguyen13](https://savrn.com/datasets?publisher=icdn12) - [Nguyen13](https://savrn.com/datasets?publisher=icdn13) - [National Engineering Research Center for Big Data Software13](https://savrn.com/datasets?publisher=thulab) - [Nguyen12](https://savrn.com/datasets?publisher=icdn10) - [Nguyen12](https://savrn.com/datasets?publisher=icdn14) - [Byte I12](https://savrn.com/datasets?publisher=icdn15) - [Rebrowser.net12](https://savrn.com/datasets?publisher=rebrowser) - [Shufan Jiang12](https://savrn.com/datasets?publisher=tsumugii) - [Sota11](https://savrn.com/datasets?publisher=sotaagi2030) - [Ai210](https://savrn.com/datasets?publisher=allenai) - [Ps Yo10](https://savrn.com/datasets?publisher=atokforps) - [Dougall DeepMind10](https://savrn.com/datasets?publisher=dougalldeepmind) - [D1610](https://savrn.com/datasets?publisher=dsixteen) - [Deesced9](https://savrn.com/datasets?publisher=ameluce) - [Jiahao Zhang9](https://savrn.com/datasets?publisher=hi-todayis-jh) Size - [10K1T19](https://savrn.com/datasets?size=n%3E1T) - [1B1M1](https://savrn.com/datasets?size=n%3E1M) Domain - [LeRobot37](https://savrn.com/datasets?domain=LeRobot) - [land-records, deeds, ocr13](https://savrn.com/datasets?domain=land-records%2C+deeds%2C+ocr) - [legal, law, graphrag12](https://savrn.com/datasets?domain=legal%2C+law%2C+graphrag) - [tsfile, timeseries, time-series12](https://savrn.com/datasets?domain=tsfile%2C+timeseries%2C+time-series) - [open-data, data-science, web-scraping9](https://savrn.com/datasets?domain=open-data%2C+data-science%2C+web-scraping) - [seo-cluster, web-nodes9](https://savrn.com/datasets?domain=seo-cluster%2C+web-nodes) - [dpo, rlhf, ultrafeedback8](https://savrn.com/datasets?domain=dpo%2C+rlhf%2C+ultrafeedback) - [mteb, text, image7](https://savrn.com/datasets?domain=mteb%2C+text%2C+image) - [nepali, instruction-tuning, agriculture7](https://savrn.com/datasets?domain=nepali%2C+instruction-tuning%2C+agriculture) - [LeRobot, robotics, lerobot5](https://savrn.com/datasets?domain=LeRobot%2C+robotics%2C+lerobot) - [ml-intern5](https://savrn.com/datasets?domain=ml-intern) - [science, stackexchange, provenance5](https://savrn.com/datasets?domain=science%2C+stackexchange%2C+provenance) - [robotics, bimanual-manipulation, imitation-learning4](https://savrn.com/datasets?domain=robotics%2C+bimanual-manipulation%2C+imitation-learning) - [robotics, openwam4](https://savrn.com/datasets?domain=robotics%2C+openwam) - [robotics, vision-language-action, libero4](https://savrn.com/datasets?domain=robotics%2C+vision-language-action%2C+libero) - [AMASS, Retarget, Robotics3](https://savrn.com/datasets?domain=AMASS%2C+Retarget%2C+Robotics) - [benchmark, agents, terminal3](https://savrn.com/datasets?domain=benchmark%2C+agents%2C+terminal) - [climate3](https://savrn.com/datasets?domain=climate) - [code3](https://savrn.com/datasets?domain=code) - [crypto, cryptocurrency, finance3](https://savrn.com/datasets?domain=crypto%2C+cryptocurrency%2C+finance) - [data-attribution, influence-functions, interpretability3](https://savrn.com/datasets?domain=data-attribution%2C+influence-functions%2C+interpretability) - [embeddinggemma-2, embeddings3](https://savrn.com/datasets?domain=embeddinggemma-2%2C+embeddings) - [fireviewer, fire, smoke3](https://savrn.com/datasets?domain=fireviewer%2C+fire%2C+smoke) - [video3](https://savrn.com/datasets?domain=video) [Most downloaded](https://savrn.com/datasets?sort=downloads)[Most liked](https://savrn.com/datasets?sort=likes)[Recently updated](https://savrn.com/datasets?sort=updated)[Name](https://savrn.com/datasets?sort=name) 3,655 datasets, sorted by most downloaded. Dataset · Text classification ### [FineFineWeb](https://savrn.com/datasets/finefineweb) [Multimodal Art Projection](https://savrn.com/model-publishers/m-a-p) The data construction workflow can be summarized as follows: 1. Deduplicate: The FineWeb dataset is deduplicated using exact deduplication and MinHash techniques to remove redundant data. 2. URL Labeling: Root URLs from FineWeb are counted, and the top 1 million URLs are labeled using GPT-4. This step generates DoI (Domain-of-Interest) Coarse-Grained URLs and DoNI (Domain-of-Non-Interest) Coarse-Grained URLs as seed data sources. 3. Coarse Recall: a. Based on the labeled root URLs, data is sampled for each domain. b. The sampled data is labeled using Qwen2-7B-Instruct, producing 500K DoI Positive Data and 500K DoI Negative Data (note that for N>1 iterations, each 500K samples are composed… Publicly accessible apache-2.0 n>1T [View dataset](https://savrn.com/datasets/finefineweb) Dataset ### [documentation-images](https://savrn.com/datasets/documentation-images) [Hugging Face](https://savrn.com/model-publishers/huggingface) HF Team: Please make sure you optimize the assets before uploading them. My favorite tool for this is https://tinypng.com/. Publicly accessible cc-by-nc-sa-4.0 [View dataset](https://savrn.com/datasets/documentation-images) Dataset · Text generation ### [wikitext](https://savrn.com/datasets/wikitext) [Salesforce AI Research](https://savrn.com/model-publishers/salesforce) The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License. Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over 110 times larger. The WikiText dataset also features a far larger vocabulary and retains the original case, punctuation and numbers - all of which are removed in PTB. As it is composed of full articles, the dataset is well suited for models that can take advantage of long term dependencies. Each subset comes in two different variants… Publicly accessible cc-by-sa-3.0 1M1T [View dataset](https://savrn.com/datasets/banned-historical-archives) Dataset ### [hd_tmp](https://savrn.com/datasets/hd-tmp) [Sdkjv](https://savrn.com/model-publishers/ayuo) Publicly accessible [View dataset](https://savrn.com/datasets/hd-tmp) Dataset · Image to 3d ### [uniocc](https://savrn.com/datasets/uniocc) [Trustworthy Autonomous Systems Laboratory](https://savrn.com/model-publishers/tasl-lab) UniOcc is a unified framework for occupancy forecasting, single-frame occupancy prediction, and occupancy flow estimation in autonomous driving. By integrating multiple real-world (nuScenes, Waymo) and synthetic (CARLA, OpenCOOD) datasets, UniOcc enables multi-domain training, seamless cross-dataset evaluation, and robust benchmarking across diverse driving environments. We simplify our benchmark so you only need: - Python 3.9 or higher You do not need: - nuscenes-devkit - waymo-open-dataset - tensorflow The UniOcc dataset is available on HuggingFace. The size of each dataset is as follows: To download each dataset, use the following command (recommend you to download only the folders you… Publicly accessible mit [View dataset](https://savrn.com/datasets/uniocc) Dataset · Text generation ### [c4](https://savrn.com/datasets/c4) [Ai2](https://savrn.com/model-publishers/allenai) A colossal, cleaned version of Common Crawl's web crawl corpus. Based on Common Crawl dataset: "https://commoncrawl.org". This is the processed version of Google's C4 dataset We prepared five variants of the data: en, en.noclean, en.noblocklist, realnewslike, and multilingual (mC4). For reference, these are the sizes of the variants: - en.noclean: 2.3TB - en.noblocklist: 380GB - realnewslike: 15GB - multilingual (mC4): 9.7TB (108 subsets, one per language) The en.noblocklist variant is exactly the same as the en variant, except we turned off the so-called "badwords filter", which removes all documents that contain words from the lists at… Publicly accessible odc-by n<1K [View dataset](https://savrn.com/datasets/c4) Dataset · Robotics ### [PhysicalAI-Robotics-GR00T-X-Embodiment-Sim](https://savrn.com/datasets/physicalai-robotics-gr00t-x-embodiment-sim) [NVIDIA](https://savrn.com/model-publishers/nvidia) We provide a set of datasets used for post-training of GR00T N1. Each dataset is a collection of trajectories from different robot embodiments and tasks. Users can download a specific subset of data by specifying the dataset name. 1. Option 1: with huggingface-cli Replace gr1armsonly.CanSort/ with the dataset name you want to download. 2. Option 2: Github LFS Replace the "gr1armswaist.CupToDrawer" with the intended dataset folder Publicly accessible cc-by-4.0 [View dataset](https://savrn.com/datasets/physicalai-robotics-gr00t-x-embodiment-sim) Dataset ### [regions](https://savrn.com/datasets/regions) [Ivanov](https://savrn.com/model-publishers/world-igr-plum) Publicly accessible mit [View dataset](https://savrn.com/datasets/regions) Dataset ### [OpenThoughts-1k-sample](https://savrn.com/datasets/openthoughts-1k-sample) [Ryan Marten](https://savrn.com/model-publishers/ryanmarten) This is a 1k sample of the OpenThoughts-114k dataset. Open synthetic reasoning dataset with high-quality examples covering math, science, code, and puzzles! Inspect the content with rich formatting with Curator Viewer. default subset containing ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models: metadata subset containing extra columns used in dataset construction: - problem - groundtruthsolution - deepseekreasoning - deepseeksolution - domain - source - testcases (code only) - startercode(code only) The numbers reported in the tables below are evaluated with our open-source tool Evalchemy. We are fully open-source. Our model weights, datasets, data… Publicly accessible [View dataset](https://savrn.com/datasets/openthoughts-1k-sample) Dataset · Text generation ### [gsm8k](https://savrn.com/datasets/gsm8k) [OpenAI](https://savrn.com/model-publishers/openai) GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning. - These problems take between 2 and 8 steps to solve. - Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer. - A bright middle school student should be able to solve every problem: from the paper, "Problems require no concepts beyond the level of early Algebra, and the vast majority of problems can be solved without explicitly defining a variable."… Publicly accessible mit 1K