# Datasets, Page 78 of 132
Source: https://savrn.com/datasets/page/78
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

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

[Ly Thu Phuong](https://savrn.com/model-publishers/phuongly76880)

Publicly accessible

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

Dataset

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

[Doris Hope Bell](https://savrn.com/model-publishers/belldoris8152)

Publicly accessible

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

Dataset

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

[Mansilla](https://savrn.com/model-publishers/rmansilla-imp)

Access requested at publisher

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

Dataset · Image classification

### [Mutilmodel_fake_news](https://savrn.com/datasets/mutilmodel-fake-news)

[Jiyao Yang](https://savrn.com/model-publishers/khat865)

Research resources for Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?, by Jiyao Yang, Yang Liu, Zhenyue Qin, Qingyu Chen, and Xiuzhen Zhang. 1. Sign in to your own Hugging Face account and complete the request form on this page. 2. Give your real name, university or research institution, institutional email, a public institutional profile or ORCID URL, and a short description of your research purpose. A detailed research-methodology or project-output proposal is not required. 3. Read and accept the Research Use License and Data Use Agreement, then submit the request. 4. Wait for manual review. The reviewer checks identity and affiliation against…

Access requested at publisher other 1K<n<10K

[View dataset](https://savrn.com/datasets/mutilmodel-fake-news)

Dataset

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

[Do Gia Ngan](https://savrn.com/model-publishers/ngando7095)

Publicly accessible

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

Dataset

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

[Ngo Hai Hieu](https://savrn.com/model-publishers/hieungo14582)

Publicly accessible

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

Dataset

### [1qna_algo](https://savrn.com/datasets/1qna-algo)

[Sybilhhhhh](https://savrn.com/model-publishers/sybilhhhhh)

Publicly accessible

[View dataset](https://savrn.com/datasets/1qna-algo)

Dataset

### [BVB-results](https://savrn.com/datasets/bvb-results)

[Yolo Y. Tang](https://savrn.com/model-publishers/yunlong10)

Access requested at publisher

[View dataset](https://savrn.com/datasets/bvb-results)

Dataset

### [DriveDNA-Controlled](https://savrn.com/datasets/drivedna-controlled)

[Henry](https://savrn.com/model-publishers/henryyhw)

DriveDNA-Controlled is a local naturalistic driving dataset recorded in Tampa, Florida, on the public roads around the University of South Florida (USF) campus, released as an additional, controlled supplement to DriveDNA. Fourteen participants drove the same Honda Civic (a single vehicle, platform HONDACIVIC) during their own everyday, commute-style driving in the area, and most drives pass through the same core road segments; all fourteen consented to public release and are included here, and none of them appears in the main release. Vehicle and road are therefore held approximately constant while the driver changes, which is exactly the comparison the main corpus — one car per driver…

Access requested at publisher other n<1K

[View dataset](https://savrn.com/datasets/drivedna-controlled)

Dataset

### [RV-labeling](https://savrn.com/datasets/rv-labeling)

[Yayuhuang](https://savrn.com/model-publishers/yayuhuang)

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/rv-labeling)

Dataset · Text generation

### [agent-kernel-sft-gfx942-triton-HIP](https://savrn.com/datasets/agent-kernel-sft-gfx942-triton-hip)

[Zhangdanyang-AMD](https://savrn.com/model-publishers/zhangdanyang)

Agent Kernel SFT Dataset (gfx942 Triton + HIP) SFT training data for GPU kernel optimization models targeting AMD MI300X (gfx942). All data in unified messages chat format. Dataset Composition File Samples Content train.jsonl 2000 patch optimization + 1164 complete implementation dev.jsonl Patch optimization (converted) samples.jsonl Full sample pool (converted) Format All files use unified messages chat format

Access requested at publisher mit 1K<n<10K

[View dataset](https://savrn.com/datasets/agent-kernel-sft-gfx942-triton-hip)

Dataset

### [data-generation-dataset](https://savrn.com/datasets/data-generation-dataset)

[Min Hao Data Gen](https://savrn.com/model-publishers/minhao-datagen)

Access requested at publisher

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

Dataset

### [khmer-ocr-selected-demo](https://savrn.com/datasets/khmer-ocr-selected-demo)

[Dorito](https://savrn.com/model-publishers/seoul12)

Screened 500 unique printed validation images using the three saved models. Selected exact matches: Transformer 380, BiLSTM 273, BiGRU 70. 49 images were correct for all three. These sets overlap; there are 426 selected unique images in total. These are selected demonstration examples, not overall accuracy or a fair benchmark. Open expanded live demo in Colab. Run all, choose a model and image, then click Run real prediction. CPU is sufficient. Download the gallery and image folders. Unzip it and open Gallery.html. Choose a model, or choose Correct for all three models. The allthree folder contains the 49 common exact-match images; individual model folders include more examples and…

Publicly accessible cc-by-4.0

[View dataset](https://savrn.com/datasets/khmer-ocr-selected-demo)

Dataset

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

[Khang](https://savrn.com/model-publishers/phuckhanghp29926)

Access requested at publisher

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

Dataset · Text generation

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

[Yuxuan Nie](https://savrn.com/model-publishers/yuxuanie)

A repository-level software engineering dataset for deep learning frameworks. FrameSWE contains 102 real-world software engineering tasks drawn from the PaddlePaddle deep learning framework. The dataset covers bug fixes, enhancements, implementations, and API changes, with an emphasis on framework-specific code understanding and repository-level reasoning. Each task provides a problem description and supporting artifacts, including a reference patch and task-specific tests. FrameSWE is intended for research on coding agents, automated program repair, and software engineering benchmarks. The enhancement category includes one task previously labeled Unspecified in an earlier summary. Affected…

Publicly accessible apache-2.0 n<1K

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

Dataset · Text classification

### [Sev-behavioral-research-v1](https://savrn.com/datasets/sev-behavioral-research-v1)

[Rr](https://savrn.com/model-publishers/macmacmacmac)

The v0.3.0 response-policy checkpoint improves authored policy decisions from 127/175 to 147/175 across 35 held-out source programs. At its calibration-selected alert threshold, it flags 14/140 permitted decisions, a 10% false-alert rate on this panel. 26/28 registered checks pass. The DNS diagnostic remains 21/32, with one repaired answer and one newly incorrect answer. These are bounded research results, not field validation or proof of human-versus-agent classification. The current 40-entry collection inventory and catalog preserve all previous entries. The new response-policy configuration contains 139 training, 28 calibration, and 35 development programs with explicit authored…

Publicly accessible other

[View dataset](https://savrn.com/datasets/sev-behavioral-research-v1)

Dataset

### [benthic](https://savrn.com/datasets/benthic-2)

[Jerry Alan Castillo](https://savrn.com/model-publishers/jerrycastillo96103)

Publicly accessible

[View dataset](https://savrn.com/datasets/benthic-2)

Dataset · Text classification

### [diversa-biomedical-v1](https://savrn.com/datasets/diversa-biomedical-v1)

[Hisky](https://savrn.com/model-publishers/skyleee)

A curated, normalized, enriched and packaged biomedical research intelligence dataset derived from OpenAlex metadata, provided in AI-ready formats (Parquet + JSONL) with full provenance and reproducible build tooling. This is a derivative dataset: it is not a re-upload of OpenAlex. The value added here is biomedical filtering by taxonomy, cleaning, normalization, relational restructuring, quality control and derived analytical features. taxonomy IDs (fields 13, 24, 27, 28, 29, 30, 35, 36), not keyword search. fixed random seed, so every year and every biomedical field is represented proportionally and the result is reproducible. OpenAlex IDs, 862,633 unique DOIs. Machine-learning and…

Publicly accessible cc0-1.0

[View dataset](https://savrn.com/datasets/diversa-biomedical-v1)

Dataset · Image to text

### [german-newspaper-pages](https://savrn.com/datasets/german-newspaper-pages)

[AI4History](https://savrn.com/model-publishers/ai-historian)

7,000,060 newspaper page scans from 650 German newspapers, each shipped with the layout boxes and OCR transcription produced for this corpus, and — for 38 % of pages — the digitising library's own ALTO XML. Every page here comes from an issue whose own rights statement is CC Public Domain Mark 1.0 or CC0 1.0. Sources were harvested from the Deutsche Digitale Bibliothek newspaper portal. Shards are data/pages-NNNNN.tar, ordered chronologically; an issue is never split across two shards. Each page is one WebDataset sample, key: -.json boxes are in the coordinates of the.jpg in the same sample. They were produced on the full-resolution scan and rescaled by the same factor the image was, so…

Publicly accessible cc0-1.0 1M<n<10M

[View dataset](https://savrn.com/datasets/german-newspaper-pages)

Dataset

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

[Vo Quang An](https://savrn.com/model-publishers/anvo69415)

Publicly accessible

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

Dataset · Question answering

### [topographrag-bench](https://savrn.com/datasets/topographrag-bench)

[RrrealLL](https://savrn.com/model-publishers/diandianone123)

pretty\name: TopoGraphRAG-Bench task\categories: - question-answering - benchmark - multimodal - document-question-answering - retrieval-augmented-generation - graph-rag - text - github.io/TopoGraphRAG-Bench/) | Code | Case Study TopoGraphRAG-Bench is a benchmark for layout-grounded multimodal evidence reasoning in GraphRAG. It contains 2,024 question–answer pairs over 201 long documents sampled from MMDocIR (https://huggingface.co/datasets/MMDocIR/MMDocIR\Eval\Dataset), spanning nine domains, including academic papers, legal documents, research reports, and financial reports. Each document averages approximately 45 pages and 13,000 words, with evidence grounded in text, figures, and tables…

Publicly accessible 1K<n<10K

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

Dataset

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

[Linda C. Jimenez](https://savrn.com/model-publishers/lindacjimenez5983)

Publicly accessible

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

Dataset

### [sg-jepa-btm](https://savrn.com/datasets/sg-jepa-btm)

[Arnon Kahani](https://savrn.com/model-publishers/desertmouse)

Publicly accessible

[View dataset](https://savrn.com/datasets/sg-jepa-btm)

Dataset

### [stellar](https://savrn.com/datasets/stellar-2)

[Ngo Bao Phuong](https://savrn.com/model-publishers/phuongngo19910)

Publicly accessible

[View dataset](https://savrn.com/datasets/stellar-2)

## 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.