# Datasets, Page 63 of 132
Source: https://savrn.com/datasets/page/63
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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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.

11,122Models

3,164Datasets

285Papers

6,792Publishers

11,852Sourced relationships

Updated 2026-10-18 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Datasets

Task

- [Text generation127](https://savrn.com/datasets?task=Text+generation)
- [Robotics106](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 answering36](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 text17](https://savrn.com/datasets?task=Image+to+text)
- [Image classification16](https://savrn.com/datasets?task=Image+classification)
- [Object detection14](https://savrn.com/datasets?task=Object+detection)
- [Text to speech14](https://savrn.com/datasets?task=Text+to+speech)
- [Visual question answering14](https://savrn.com/datasets?task=Visual+question+answering)
- [Image to image11](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)
- [Audio classification7](https://savrn.com/datasets?task=Audio+classification)
- [Feature extraction7](https://savrn.com/datasets?task=Feature+extraction)
- [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

- [other250](https://savrn.com/datasets?license=other)
- [apache-2.0236](https://savrn.com/datasets?license=apache-2.0)
- [mit232](https://savrn.com/datasets?license=mit)
- [cc-by-4.0224](https://savrn.com/datasets?license=cc-by-4.0)
- [cc-by-nc-4.077](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-4.020](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-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)
- [cc-by-nc-3.01](https://savrn.com/datasets?license=cc-by-nc-3.0)

Access

- [Publicly accessible2,933](https://savrn.com/datasets?access=open_access)
- [Access requested at publisher231](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 Xie23](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)
- [Ai210](https://savrn.com/datasets?publisher=allenai)
- [Ps Yo10](https://savrn.com/datasets?publisher=atokforps)
- [Dougall DeepMind10](https://savrn.com/datasets?publisher=dougalldeepmind)
- [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)
- [Sota9](https://savrn.com/datasets?publisher=sotaagi2030)
- [Sunsetcanyon9](https://savrn.com/datasets?publisher=sunsetcanyon)

Size

- [10K<n<100K125](https://savrn.com/datasets?size=10K%3Cn%3C100K)
- [1K<n<10K106](https://savrn.com/datasets?size=1K%3Cn%3C10K)
- [n<1K100](https://savrn.com/datasets?size=n%3C1K)
- [100K<n<1M70](https://savrn.com/datasets?size=100K%3Cn%3C1M)
- [1M<n<10M51](https://savrn.com/datasets?size=1M%3Cn%3C10M)
- [10M<n<100M33](https://savrn.com/datasets?size=10M%3Cn%3C100M)
- [100M<n<1B21](https://savrn.com/datasets?size=100M%3Cn%3C1B)
- [n>1T16](https://savrn.com/datasets?size=n%3E1T)
- [1B<n<10B8](https://savrn.com/datasets?size=1B%3Cn%3C10B)
- [100B<n<1T5](https://savrn.com/datasets?size=100B%3Cn%3C1T)
- [10B<n<100B2](https://savrn.com/datasets?size=10B%3Cn%3C100B)
- [10GB<n<100GB1](https://savrn.com/datasets?size=10GB%3Cn%3C100GB)
- [120,8981](https://savrn.com/datasets?size=120%2C898)
- [1B-tokens1](https://savrn.com/datasets?size=1B-tokens)
- [1T<n<10T1](https://savrn.com/datasets?size=1T%3Cn%3C10T)
- [n1K<n10K1](https://savrn.com/datasets?size=n1K%3Cn10K)
- [n<100K1](https://savrn.com/datasets?size=n%3C100K)
- [n<10K1](https://savrn.com/datasets?size=n%3C10K)
- [n<1GB1](https://savrn.com/datasets?size=n%3C1GB)
- [n>1M1](https://savrn.com/datasets?size=n%3E1M)

Domain

- [LeRobot31](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)
- [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)
- [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)
- [ai-governance, measurement2](https://savrn.com/datasets?domain=ai-governance%2C+measurement)
- [autonomous, agents, evolution2](https://savrn.com/datasets?domain=autonomous%2C+agents%2C+evolution)

[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)

3,164 datasets, sorted by most downloaded.

Dataset

### [DrivAerRim](https://savrn.com/datasets/drivaerrim)

[Chao Xia](https://savrn.com/model-publishers/beyondxia1212)

DrivAerRim DrivAerRim: A full vehicle dataset with rotating wheels for data-driven aerodynamic design of wheel rims DrivAerRim is a full vehicle computational fluid dynamics (CFD) dataset designed to isolate how rim geometry affects rotating wheel flow, component forces, and complete vehicle aerodynamics. It contains steady Reynolds-averaged Navier-Stokes (RANS) results for all 904 rim geometries from the DeepWheel dataset, installed on a fixed, full scale DrivAer estateback.

Publicly accessible cc-by-nc-4.0

[View dataset](https://savrn.com/datasets/drivaerrim)

Dataset

### [ipfs_slovakia_laws_ir](https://savrn.com/datasets/ipfs-slovakia-laws-ir)

[Justicedao](https://savrn.com/model-publishers/justicedao)

Slovakia laws IR release.

Publicly accessible

[View dataset](https://savrn.com/datasets/ipfs-slovakia-laws-ir)

Dataset · Text generation

### [comparia-fr-arena](https://savrn.com/datasets/comparia-fr-arena)

[Ministère de la Culture (SNUM)](https://savrn.com/model-publishers/ministere-culture)

Compar:IA is a public chatbot arena run by the French Ministry of Culture. People chat with two anonymous models side by side and say which answer they prefer. This dataset is the result. Each row is one turn of a conversation: the two models' answers to the same user message, the preference the user gave on that turn (if any), and the full conversation both answers belong to. The prompts come from real users, most of them writing in French. With 675k response pairs and 208k human preferences over 115+ models, it is one of the larger French preference datasets available for training and evaluating chat models. A user sends a prompt and gets back two answers from two anonymous models. The…

Access requested at publisher etalab-2.0 100K<n<1M

[View dataset](https://savrn.com/datasets/comparia-fr-arena)

Dataset

### [isambard-ncp-returns](https://savrn.com/datasets/isambard-ncp-returns)

[Alexander Gurung](https://savrn.com/model-publishers/agurung)

Publicly accessible

[View dataset](https://savrn.com/datasets/isambard-ncp-returns)

Dataset

### [e15d663d10bd47079924dcac38735252](https://savrn.com/datasets/e15d663d10bd47079924dcac38735252)

[Xing Chao](https://savrn.com/model-publishers/darkraider42)

This dataset contains a collection of files with randomized directory and file names. File bytes are preserved unchanged. Names and metadata embedded inside file contents or archives are not removed. Original file extensions are preserved, including.tar for archive shards. Archive contents and internal sample names are unchanged; existing WebDataset shards retain their format. Access requires manual approval by the repository owner. The original-path mapping is held locally by the owner and is not distributed in this repository. Ask the owner for provenance, format guidance, and applicable source licenses before reuse. This repository does not grant additional rights to the underlying…

Access requested at publisher

[View dataset](https://savrn.com/datasets/e15d663d10bd47079924dcac38735252)

Dataset

### [repo](https://savrn.com/datasets/repo)

[Con Cafe](https://savrn.com/model-publishers/arepaconcafe)

Publicly accessible

[View dataset](https://savrn.com/datasets/repo)

Dataset

### [a23f3d7d7914](https://savrn.com/datasets/a23f3d7d7914)

[Linnan](https://savrn.com/model-publishers/linnan98k)

a23f3d7d7914 peru-02 A shell (Nexorasigned.apk)

Publicly accessible mit

[View dataset](https://savrn.com/datasets/a23f3d7d7914)

Dataset

### [2026_HiGO](https://savrn.com/datasets/2026-higo)

[Xufeng Yao](https://savrn.com/model-publishers/xfyao)

Publicly accessible

[View dataset](https://savrn.com/datasets/2026-higo)

Dataset

### [tinyperson_mmdet_yolo_protocol_seed43_runs](https://savrn.com/datasets/tinyperson-mmdet-yolo-protocol-seed43-runs)

[LeDuy](https://savrn.com/model-publishers/duyle2408)

Publicly accessible

[View dataset](https://savrn.com/datasets/tinyperson-mmdet-yolo-protocol-seed43-runs)

Dataset · Robotics

### [SAGE-data](https://savrn.com/datasets/sage-data)

[Letian Cheng](https://savrn.com/model-publishers/cltray)

Manipulation datasets for SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning. Sizes are archive sizes in decimal GB. Training and validation archives are under training/; evaluation archives are under evaluation/. Evaluation archives contain the inputs required by the released query manifests, not a complete training dataset. Simulator assets and model weights are installed separately. Download only the evaluation data you need, for example LIBERO Scene2: To download Scene2 training and validation instead, use allowpatterns=["training/liberoscene2"]. Replace liberoscene2 with liberocaddy or robotwina2b for the other environments. Omitting allowpatterns downloads all…

Publicly accessible

[View dataset](https://savrn.com/datasets/sage-data)

Dataset

### [gt](https://savrn.com/datasets/gt-6)

[Zkong](https://savrn.com/model-publishers/hariicds)

Publicly accessible

[View dataset](https://savrn.com/datasets/gt-6)

Dataset

### [sglang-nightly-precision-baselines](https://savrn.com/datasets/sglang-nightly-precision-baselines)

[SGLang Project](https://savrn.com/model-publishers/sgl-project)

Publicly accessible

[View dataset](https://savrn.com/datasets/sglang-nightly-precision-baselines)

Dataset · Image classification

### [triples-objekts](https://savrn.com/datasets/triples-objekts)

[Fadhil Afif](https://savrn.com/model-publishers/fadhilafif98)

Complete digital collectible photocards (Objekts) dataset for the K-Pop girl group tripleS (Modhaus / COSMO app). All visual assets and trademarks belong to MODHAUS and the COSMO platform. This dataset is compiled strictly for non-commercial fan archival, educational, indexing, and computer vision research purposes.

Publicly accessible other 10K<n<100K

[View dataset](https://savrn.com/datasets/triples-objekts)

Dataset

### [123](https://savrn.com/datasets/123)

[Link](https://savrn.com/model-publishers/zhinanzhen123)

Publicly accessible

[View dataset](https://savrn.com/datasets/123)

Dataset

### [herb-ai-vault](https://savrn.com/datasets/herb-ai-vault)

[Steven](https://savrn.com/model-publishers/steveo223)

Publicly accessible

[View dataset](https://savrn.com/datasets/herb-ai-vault)

Dataset

### [RoboSteer-Model-Resources](https://savrn.com/datasets/robosteer-model-resources)

[Buffay](https://savrn.com/model-publishers/phoebecc)

Documentation, resource manifests and planned supplementary implementations for models used in RoboSteer. This initial release contains model information forms and GMR usage documentation. It does not contain runnable model packages, weights, a training dataset, a finalized benchmark protocol or complete reproduction results. The collection has nine models/pipelines and two shared components: GMR and GR00T-WholeBodyControl. Shared components are not counted as models. This is a file-based resource collection. Download and browse the files; no loaddataset() table-loading interface or train/test split is claimed. Shared components: GMR — documentation only; GR00T-WholeBodyControl…

Publicly accessible

[View dataset](https://savrn.com/datasets/robosteer-model-resources)

Dataset

### [WSM_v1_Latent_PT_WSG_v1.8_for_Flux2](https://savrn.com/datasets/wsm-v1-latent-pt-wsg-v1-8-for-flux2)

[World Snapshot](https://savrn.com/model-publishers/world-snapshot)

This repository stores the original modality-scoped PyTorch latent shards used by WSM v1 training. PT shards are uploaded directly rather than wrapped in tar archives, so one modality can be replaced without republishing the full cache. Each modality update is committed with its changed PT shards and SHA-256 manifest. This is the continuously maintained raw-PT repository. When the WSG standard advances, this repository is renamed to the next standard version (for example, v1.8 to v1.9) so its commit history remains continuous. Historical tar-packaged repositories remain separate snapshots.

Publicly accessible mit

[View dataset](https://savrn.com/datasets/wsm-v1-latent-pt-wsg-v1-8-for-flux2)

Dataset

### [pi05-libero-taco-eval](https://savrn.com/datasets/pi05-libero-taco-eval)

[Aayushi Shrivastava](https://savrn.com/model-publishers/aayushis196)

Publicly accessible

[View dataset](https://savrn.com/datasets/pi05-libero-taco-eval)

Dataset · Text retrieval

### [chinese-ai-and-robotics-open-intelligence](https://savrn.com/datasets/chinese-ai-and-robotics-open-intelligence)

[Le](https://savrn.com/model-publishers/simpleg2023)

Curated open intelligence dataset tracking Chinese frontier developments in Large Language Models (LLMs), Humanoid Dynamic Locomotion, 3D Computer Vision, and Neuromorphic edge processors. If you utilize this open intelligence dataset in your academic research, industrial benchmarking, or LLM training pipelines, please cite our repository: Maintained automatically by Sino Open Intelligence Network.

Publicly accessible cc-by-nc-4.0 1K<n<10K

[View dataset](https://savrn.com/datasets/chinese-ai-and-robotics-open-intelligence)

Dataset · Other

### [CORDEX-ML-Bench](https://savrn.com/datasets/cordex-ml-bench)

[Neelesh](https://savrn.com/model-publishers/rampaln)

A benchmarking dataset for data-driven regional climate downscaling CORDEX-ML-Bench is a standardized benchmark for evaluating machine-learning approaches to regional climate downscaling. It is aligned with the World Climate Research Programme's CORDEX framework and constitutes the first phase of a community initiative to advance data-driven downscaling toward operational readiness, complementing future dynamical downscaling efforts under CMIP7. The benchmark targets downscaled daily maximum temperature (tasmax) and precipitation (pr) at ~10 km resolution (a 20x increase in resolution) across three accompanying paper, 40 independently developed ML configurations — spanning traditional ML…

Publicly accessible cc-by-4.0 100B<n<1T

[View dataset](https://savrn.com/datasets/cordex-ml-bench)

Dataset

### [gt](https://savrn.com/datasets/gt-7)

[Z Kong](https://savrn.com/model-publishers/hariizxd)

Publicly accessible

[View dataset](https://savrn.com/datasets/gt-7)

Dataset

### [Inference_OfflineRL](https://savrn.com/datasets/inference-offlinerl)

[Bui Tung](https://savrn.com/model-publishers/tungtom2004)

Publicly accessible

[View dataset](https://savrn.com/datasets/inference-offlinerl)

Dataset

### [data-backup-5](https://savrn.com/datasets/data-backup-5)

[Tom](https://savrn.com/model-publishers/dizh23)

Publicly accessible

[View dataset](https://savrn.com/datasets/data-backup-5)

Dataset

### [aquatic](https://savrn.com/datasets/aquatic)

[Do Thi Thanh](https://savrn.com/model-publishers/thanhdo54151)

Publicly accessible

[View dataset](https://savrn.com/datasets/aquatic)

## Model Collections

Hand-picked starting points, each with the reason it exists.

Collection · 4 entries

### [Embedding models for retrieval](https://savrn.com/collections/embedding-models-for-retrieval)

Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.

[View collection](https://savrn.com/collections/embedding-models-for-retrieval)

Collection · 4 entries

### [Models that fit on one accelerator](https://savrn.com/collections/models-that-run-on-one-accelerator)

Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.

[View collection](https://savrn.com/collections/models-that-run-on-one-accelerator)

Collection · 6 entries

### [Open-weight text models worth knowing](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.

[View collection](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Collection · 3 entries

### [Speech and audio models](https://savrn.com/collections/speech-and-audio)

Recognition and synthesis models, grouped so the two directions are easy to compare.

[View collection](https://savrn.com/collections/speech-and-audio)

## Related SAVRN Research

The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.

SAVRN Index

### [What open models cost to run](https://savrn.com/ai-index/pricing/open-models)

The same open-weight model priced by every host that serves it, per million tokens.

Research Hub

### [Data center trackers and maps](https://savrn.com/data-center-research)

Moratoriums, permits, power, water and capital behind the facilities that run these models.

Method

### [How the Model Hub is built](https://savrn.com/model-hub/methodology)

Sources, evidence labels, refresh behaviour, and the limits of every comparison here.