# Datasets, Page 107 of 136 Source: https://savrn.com/datasets/page/107 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. 11,442Models 3,247Datasets 285Papers 6,976Publishers 12,091Sourced relationships Updated 2026-10-19 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Datasets Task - [Text generation127](https://savrn.com/datasets?task=Text+generation) - [Robotics108](https://savrn.com/datasets?task=Robotics) - [Other48](https://savrn.com/datasets?task=Other) - [Text classification47](https://savrn.com/datasets?task=Text+classification) - [Text retrieval44](https://savrn.com/datasets?task=Text+retrieval) - [Question answering38](https://savrn.com/datasets?task=Question+answering) - [Speech recognition33](https://savrn.com/datasets?task=Speech+recognition) - [Tabular classification21](https://savrn.com/datasets?task=Tabular+classification) - [Time series forecasting21](https://savrn.com/datasets?task=Time+series+forecasting) - [Image to text20](https://savrn.com/datasets?task=Image+to+text) - [Image classification16](https://savrn.com/datasets?task=Image+classification) - [Visual question answering15](https://savrn.com/datasets?task=Visual+question+answering) - [Object detection14](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) - [Image segmentation9](https://savrn.com/datasets?task=Image+segmentation) - [Image to 3d9](https://savrn.com/datasets?task=Image+to+3d) - [Translation9](https://savrn.com/datasets?task=Translation) - [Video classification9](https://savrn.com/datasets?task=Video+classification) - [Feature extraction8](https://savrn.com/datasets?task=Feature+extraction) - [Audio classification7](https://savrn.com/datasets?task=Audio+classification) - [Reinforcement learning7](https://savrn.com/datasets?task=Reinforcement+learning) - [Text to image7](https://savrn.com/datasets?task=Text+to+image) - [Text to video7](https://savrn.com/datasets?task=Text+to+video) License - [other268](https://savrn.com/datasets?license=other) - [apache-2.0238](https://savrn.com/datasets?license=apache-2.0) - [mit237](https://savrn.com/datasets?license=mit) - [cc-by-4.0232](https://savrn.com/datasets?license=cc-by-4.0) - [cc-by-nc-4.078](https://savrn.com/datasets?license=cc-by-nc-4.0) - [cc0-1.030](https://savrn.com/datasets?license=cc0-1.0) - [cc-by-sa-4.029](https://savrn.com/datasets?license=cc-by-sa-4.0) - [odc-by24](https://savrn.com/datasets?license=odc-by) - [cc-by-nc-sa-4.021](https://savrn.com/datasets?license=cc-by-nc-sa-4.0) - [unknown14](https://savrn.com/datasets?license=unknown) - [cc-by-sa-3.06](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 accessible2,994](https://savrn.com/datasets?access=open_access) - [Access requested at publisher253](https://savrn.com/datasets?access=gated) Publisher - [Benjamin J Barber35](https://savrn.com/datasets?publisher=endomorphosis) - [Chang Yu24](https://savrn.com/datasets?publisher=g1n0st) - [Ji Xie24](https://savrn.com/datasets?publisher=sanaka87) - [Justicedao18](https://savrn.com/datasets?publisher=justicedao) - [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) - [Julia Dollis9](https://savrn.com/datasets?publisher=juliadollis) - [Massive Text Embedding Benchmark9](https://savrn.com/datasets?publisher=mteb) - [Pranab Pattanaik9](https://savrn.com/datasets?publisher=p2samapa) Size - [10K1T16](https://savrn.com/datasets?size=n%3E1T) - [1B1M1](https://savrn.com/datasets?size=n%3E1M) Domain - [LeRobot32](https://savrn.com/datasets?domain=LeRobot) - [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) - [science, stackexchange, provenance5](https://savrn.com/datasets?domain=science%2C+stackexchange%2C+provenance) - [LeRobot, robotics, lerobot4](https://savrn.com/datasets?domain=LeRobot%2C+robotics%2C+lerobot) - [ml-intern4](https://savrn.com/datasets?domain=ml-intern) - [robotics, bimanual-manipulation, imitation-learning4](https://savrn.com/datasets?domain=robotics%2C+bimanual-manipulation%2C+imitation-learning) - [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) - [fireviewer, fire, smoke3](https://savrn.com/datasets?domain=fireviewer%2C+fire%2C+smoke) - [land-records, deeds, ocr3](https://savrn.com/datasets?domain=land-records%2C+deeds%2C+ocr) - [video3](https://savrn.com/datasets?domain=video) - [LeRobot, lerobot, grabette2](https://savrn.com/datasets?domain=LeRobot%2C+lerobot%2C+grabette) - [LeRobot, ur7e, robotiq2](https://savrn.com/datasets?domain=LeRobot%2C+ur7e%2C+robotiq) - [agentenv, rl-environment, agents2](https://savrn.com/datasets?domain=agentenv%2C+rl-environment%2C+agents) [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) 3,247 datasets, sorted by most downloaded. Dataset · Visual document retrieval ### [Vidore3EnergyBGEm3Reranking.v2](https://savrn.com/datasets/vidore3energybgem3reranking-v2) [Massive Text Embedding Benchmark](https://savrn.com/model-publishers/mteb) This task is used to evaluate the reranking performance for visual document retrieval. The candidates for reranking are the top-50 pages retrieved by the BAAI/bge-m3 model. This dataset, Energy Fr, is a corpus of reports on energy supply in europe, intended for complex-document understanding tasks. Original queries were created in french, then translated to english, german, italian, portuguese and spanish.This version add the OCR'ed markdown to allow for comparison across image-text, image-only and text-only models. You can evaluate an embedding model on this dataset using the following code: To learn more about how to run models on mteb task check out the GitHub repository. If you use this… Publicly accessible cc-by-4.0 [View dataset](https://savrn.com/datasets/vidore3energybgem3reranking-v2) Dataset · Visual document retrieval ### [Vidore3FinanceEnBGEm3Reranking.v2](https://savrn.com/datasets/vidore3financeenbgem3reranking-v2) [Massive Text Embedding Benchmark](https://savrn.com/model-publishers/mteb) This task is used to evaluate the reranking performance for visual document retrieval. The candidates for reranking are the top-50 pages retrieved by the BAAI/bge-m3 model. This task, Finance - EN, is a corpus of reports from american banking companies, intended for long-document understanding tasks. Original queries were created in english, then translated to french, german, italian, portuguese and spanish.This version add the OCR'ed markdown to allow for comparison across image-text, image-only and text-only models. You can evaluate an embedding model on this dataset using the following code: To learn more about how to run models on mteb task check out the GitHub repository. If you use… Publicly accessible cc-by-4.0 [View dataset](https://savrn.com/datasets/vidore3financeenbgem3reranking-v2) Dataset · Visual document retrieval ### [Vidore3FinanceFrBGEm3Reranking.v2](https://savrn.com/datasets/vidore3financefrbgem3reranking-v2) [Massive Text Embedding Benchmark](https://savrn.com/model-publishers/mteb) This task is used to evaluate the reranking performance for visual document retrieval. The candidates for reranking are the top-50 pages retrieved by the BAAI/bge-m3 model. This task, Finance - FR, is a corpus of reports from french companies in the luxury domain, intended for long-document understanding tasks. Original queries were created in french, then translated to english, german, italian, portuguese and spanish.This version add the OCR'ed markdown to allow for comparison across image-text, image-only and text-only models. You can evaluate an embedding model on this dataset using the following code: To learn more about how to run models on mteb task check out the GitHub repository. If… Publicly accessible cc-by-4.0 [View dataset](https://savrn.com/datasets/vidore3financefrbgem3reranking-v2) Dataset · Visual document retrieval ### [Vidore3HrBGEm3Reranking.v2](https://savrn.com/datasets/vidore3hrbgem3reranking-v2) [Massive Text Embedding Benchmark](https://savrn.com/model-publishers/mteb) This task is used to evaluate the reranking performance for visual document retrieval. The candidates for reranking are the top-50 pages retrieved by the BAAI/bge-m3 model. This dataset, HR, is a corpus of reports released by the european union, intended for complex-document understanding tasks. Original queries were created in english, then translated to french, german, italian, portuguese and spanish.This version add the OCR'ed markdown to allow for comparison across image-text, image-only and text-only models. You can evaluate an embedding model on this dataset using the following code: To learn more about how to run models on mteb task check out the GitHub repository. If you use this… Publicly accessible cc-by-4.0 [View dataset](https://savrn.com/datasets/vidore3hrbgem3reranking-v2) Dataset · Visual document retrieval ### [Vidore3IndustrialBGEm3Reranking.v2](https://savrn.com/datasets/vidore3industrialbgem3reranking-v2) [Massive Text Embedding Benchmark](https://savrn.com/model-publishers/mteb) This task is used to evaluate the reranking performance for visual document retrieval. The candidates for reranking are the top-50 pages retrieved by the BAAI/bge-m3 model. This dataset, Industrial reports, is a corpus of technical documents on military aircraft (fueling, mechanics...), intended for complex-document understanding tasks. Original queries were created in english, then translated to french, german, italian, portuguese and spanish.This version add the OCR'ed markdown to allow for comparison across image-text, image-only and text-only models. You can evaluate an embedding model on this dataset using the following code: To learn more about how to run models on mteb task check out… Publicly accessible cc-by-4.0 [View dataset](https://savrn.com/datasets/vidore3industrialbgem3reranking-v2) Dataset · Visual document retrieval ### [Vidore3PharmaceuticalsBGEm3Reranking.v2](https://savrn.com/datasets/vidore3pharmaceuticalsbgem3reranking-v2) [Massive Text Embedding Benchmark](https://savrn.com/model-publishers/mteb) This task is used to evaluate the reranking performance for visual document retrieval. The candidates for reranking are the top-50 pages retrieved by the BAAI/bge-m3 model. This dataset, Pharmaceutical, is a corpus of slides from the FDA, intended for long-document understanding tasks. Original queries were created in english, then translated to french, german, italian, portuguese and spanish.This version add the OCR'ed markdown to allow for comparison across image-text, image-only and text-only models. You can evaluate an embedding model on this dataset using the following code: To learn more about how to run models on mteb task check out the GitHub repository. If you use this dataset… Publicly accessible cc-by-4.0 [View dataset](https://savrn.com/datasets/vidore3pharmaceuticalsbgem3reranking-v2) Dataset ### [Vidore3PhysicsBGEm3Reranking.v2](https://savrn.com/datasets/vidore3physicsbgem3reranking-v2) [Massive Text Embedding Benchmark](https://savrn.com/model-publishers/mteb) Publicly accessible [View dataset](https://savrn.com/datasets/vidore3physicsbgem3reranking-v2) Dataset ### [WarmRoleplay](https://savrn.com/datasets/warmroleplay) [Minas Fostieris](https://savrn.com/model-publishers/minas2025) Publicly accessible [View dataset](https://savrn.com/datasets/warmroleplay) Dataset ### [Weibo-raw-0002](https://savrn.com/datasets/weibo-raw-0002) [StevenZhou](https://savrn.com/model-publishers/stevenzhou0825) This repository is the immutable audit copy of raw Weibo API responses collected by a private archive. Original gzip snapshots are stored unchanged inside bounded TAR shards with SHA-256 manifests. Older snapshots remain individually addressable. The database records each repository, entry and byte range. The parsed and authoritative text lives in the operator's PostgreSQL database; these files exist for audit and re-processing without re-fetching from Weibo. This dataset uses Hugging Face manual gated access. Access requests are reviewed by the repository owner. Do not mirror, redistribute, bulk-download, or use the responses to identify or contact individuals. Source content may contain… Access requested at publisher other [View dataset](https://savrn.com/datasets/weibo-raw-0002) Dataset ### [Windows10_1709](https://savrn.com/datasets/windows10-1709) [Công Bằng](https://savrn.com/model-publishers/yingg91) Publicly accessible [View dataset](https://savrn.com/datasets/windows10-1709) Dataset ### [Windows10_1803](https://savrn.com/datasets/windows10-1803) [Công Bằng](https://savrn.com/model-publishers/yingg91) Publicly accessible [View dataset](https://savrn.com/datasets/windows10-1803) Dataset ### [Workflows](https://savrn.com/datasets/workflows) [Hemanth](https://savrn.com/model-publishers/koiladakk) Publicly accessible [View dataset](https://savrn.com/datasets/workflows) Dataset ### [WorldRover-styles](https://savrn.com/datasets/worldrover-styles) [Alaya Lab](https://savrn.com/model-publishers/alayalab) Publicly accessible [View dataset](https://savrn.com/datasets/worldrover-styles) Dataset ### [X2I_Recap](https://savrn.com/datasets/x2i-recap) [Yiming Hao](https://savrn.com/model-publishers/ymhao) Access requested at publisher apache-2.0 [View dataset](https://savrn.com/datasets/x2i-recap) Dataset ### [XianKa-Cat-4D-Data](https://savrn.com/datasets/xianka-cat-4d-data) [Xianka-Cat](https://savrn.com/model-publishers/xianka-zhong) 两位抖音博主的视频数据集 + SAM 2.1 语义分割结果，按博主（抖音号/UID）分别打包。 视频文件名为 序号作品ID.mp4；metadata.json 中 awemeid 与作品 ID 对应，含标题/点赞/评论等元数据。 tar 均为未压缩归档（纯打包，字节无损），解压即得与原始一致的目录树。 - frameXXXseg.png：分割叠加图（原始帧 + 彩色实例掩码） - masks.npz：键 frameXXX 对应 (Nmasks, H, W) bool 掩码数组 - report.json：每帧掩码数量与耗时统计 掩码为类无关实例分割；需要类别标签可自行接 CLIP / Grounding DINO。 recon.npz 内含每帧 pred{i}pts（点云）与 view{i}img（RGB）等，可用于点云可视化/深度/位姿估计。 四个 tar 相互独立、内部路径均相对 data/，可并行下载解压。 Publicly accessible [View dataset](https://savrn.com/datasets/xianka-cat-4d-data) Dataset ### [_natural_s0_e100_original_1_medium_0.7_16384_gpt-6_1-sol](https://savrn.com/datasets/natural-s0-e100-original-1-medium-0-7-16384-gpt-6-1-sol) [Connections Dev](https://savrn.com/model-publishers/connections-dev) This dataset was generated using the inference script with the following configuration: The dataset contains the following key columns: - query: The original query - entitya: Starting entity - entityb: Target entity - relb: Target relationship - pathprediction: Generated connection paths - promptforpath: Prompts used for path generation You can load this dataset using: This dataset was generated on 2026-10-09 23:45:16 using the inference pipeline with the above configuration. Publicly accessible mit [View dataset](https://savrn.com/datasets/natural-s0-e100-original-1-medium-0-7-16384-gpt-6-1-sol) Dataset ### [a1-completed-gptoss-extension-raw-signals-20261011](https://savrn.com/datasets/a1-completed-gptoss-extension-raw-signals-20261011) [Dang Hoang Duy](https://savrn.com/model-publishers/diligentpenguinn) Lossless archive of 1,073 completed GPT-OSS raw NPZ files from the A1 PRM800K extension collection, outside the earlier 5,000-trace pilot archive. Original source payload: 11,414,646,710 bytes. This migration retains all existing step features, labels, checkpoints, manifests, completion receipts and reports locally. These are legacy replay signals, separate from the new 198,000-trace date-free GPT-OSS core. This is an archival repository, not a new benchmark or curated training split. Original source licenses and model terms continue to apply; no new license is asserted. This follows the tar-shard layout of the previous PRM800K archive. During migration, each shards/signals-.tar has a… Publicly accessible [View dataset](https://savrn.com/datasets/a1-completed-gptoss-extension-raw-signals-20261011) Dataset ### [a12_cryptoresearch](https://savrn.com/datasets/a12-cryptoresearch) [Voquendot](https://savrn.com/model-publishers/voquendot) a12cryptoresearch · klines spot de Binance (binance.vision) en parquet 1s/symbol= /date= /data.parquet — velas de 1 segundo, particion hive por simbolo y dia UTC. 1m/klines1m.parquet — velas de 1 minuto de todos los simbolos en UN fichero (columna symbol, ordenado por symbol, t; un row group por simbolo). Columnas: t = apertura de la vela en segundos epoch UTC; o,h,l,c precio; v volumen base; qv volumen quote; n trades; tbb / tbq volumen comprador Publicly accessible mit [View dataset](https://savrn.com/datasets/a12-cryptoresearch) Dataset ### [a66182b9e1699324789d3b1f383860495a771efa](https://savrn.com/datasets/a66182b9e1699324789d3b1f383860495a771efa) [Chang Yu](https://savrn.com/model-publishers/g1n0st) Publicly accessible [View dataset](https://savrn.com/datasets/a66182b9e1699324789d3b1f383860495a771efa) Dataset ### [aa2c8a4a118cb15c30d11c333aaba89b8938407c](https://savrn.com/datasets/aa2c8a4a118cb15c30d11c333aaba89b8938407c) [Chang Yu](https://savrn.com/model-publishers/g1n0st) Publicly accessible [View dataset](https://savrn.com/datasets/aa2c8a4a118cb15c30d11c333aaba89b8938407c) Dataset ### [abhilash-angel](https://savrn.com/datasets/abhilash-angel) [Maybe Heisenberg 07](https://savrn.com/model-publishers/maybe-heisenberg-07) Publicly accessible mit [View dataset](https://savrn.com/datasets/abhilash-angel) Dataset ### [abliteration-run002-ingredients](https://savrn.com/datasets/abliteration-run002-ingredients) [S'Bussiso Dube](https://savrn.com/model-publishers/sbussiso) Publicly accessible [View dataset](https://savrn.com/datasets/abliteration-run002-ingredients) Dataset · Image to 3d ### [acid-wds](https://savrn.com/datasets/acid-wds) [Hong Li](https://savrn.com/model-publishers/luh0502) ACID (Aerial Coastline Imagery Dataset) camera trajectories and frames, in the pixelSplat chunk release, repacked as random-access WebDataset shards. Every file in this repository was verified against the source copy (same file set, same byte sizes). WebDataset tar shards (about 3 GB each), one tar row per scene, indexed for random access with ultar.utix files (msgpack rows of entry keys, offsets and sizes, so they can be read without ultar). Every scene row holds: -.tar.utix — the ultar row index (offsets and sizes of every entry). -.tar.centres.bin — float32 camera centres per frame per scene (a small sidecar used for farthest-point view sampling); header DPFV, then a per-scene offset… Publicly accessible mit 10K