# Datasets, Page 77 of 132
Source: https://savrn.com/datasets/page/77
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

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

[Alaya Lab](https://savrn.com/model-publishers/alayalab)

PROWBench evaluates whether video models follow program-specified motion and interactions. It contains 170 episodes, 200 camera views, and 600 proxy videos, with prompts, recorded world states, and RGB first frames where available. Each view has three proxy representations: white (coarse 3D), mixed (semantic proxy), and colored-obb (3D boxes). Multi-view episodes have four synchronized cameras. The full Viewer split contains 600 rows, one per camera–representation pair; the other splits are subsets. Full prompts are available through promptfile. previewkind distinguishes RGB first-frame references from proxy previews. Long-video prompts use JSON files or a prompts/ directory of windowed…

Publicly accessible mit n<1K

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

Dataset · Text retrieval

### [ipfs_canada_municipal_laws_ir](https://savrn.com/datasets/ipfs-canada-municipal-laws-ir)

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

Research retrieval release of endomorphosis/ipfscanadamunicipallaws (revision 77d4b1c335c2524949801c1366ccf5e61cfd619a) packaged as country-laws-ir-graphrag/v1 (layout family skillcenter-huggingface-release/v3 / publicus-ir). Not legal advice. This is a research snapshot. The official gazette / authentic source of Canada Municipal prevails over this corpus. Retrieved documents and graph edges are retrieval evidence only. No legal text was invented. Primary key: entrycid (CIDv1 raw sha2-256 of a canonical identity record). Integer documentindex values are compact shard pointers, not identities. Target Hub id (packaging metadata only): justicedao/ipfscanadamunicipallawsir. entrycid, lawcid…

Publicly accessible other 10K<n<100K

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

Dataset

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

[Le Duc Kien](https://savrn.com/model-publishers/kienle25657)

Publicly accessible

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

Dataset

### [sorrel-T-gemma-3-27b-pt-seed0-documents](https://savrn.com/datasets/sorrel-t-gemma-3-27b-pt-seed0-documents)

[Joshua Fonseca](https://savrn.com/model-publishers/joshycodes)

Publicly accessible

[View dataset](https://savrn.com/datasets/sorrel-t-gemma-3-27b-pt-seed0-documents)

Dataset · Tabular classification

### [3w-causal-real-group-audit](https://savrn.com/datasets/3w-causal-real-group-audit)

[Sankalp Thakur](https://savrn.com/model-publishers/sankalpsthakur)

Decision-level and episode-level results from a well-event detection study on real Petrobras 3W 2.0 recordings, tested on well groups the detectors never saw in training. Use these tables to see how detectors transfer across wells. This is not a new sensor dataset; raw sensor values stay with the upstream source. - Scores, labels and provenance columns are for auditing. Never feed them to a predictor as inputs. - eventtimestampns is the dataset's nominal clock, not arrival or decision time. - Filename groups are anonymized public IDs, not authenticated well identities. The validation-selected detector covered 24 of 55 confirmation episodes and caught 3 of 12 observable onsets within 300…

Publicly accessible cc-by-4.0

[View dataset](https://savrn.com/datasets/3w-causal-real-group-audit)

Dataset

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

[Katherine Ray Wells](https://savrn.com/model-publishers/katherinewells87329)

Publicly accessible

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

Dataset

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

[Vu Duc Hieu](https://savrn.com/model-publishers/hieuvu21390)

Publicly accessible

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

Dataset

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

[Ly Minh Tuan](https://savrn.com/model-publishers/tuanly54071)

Publicly accessible

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

Dataset · Text generation

### [ship-design-decisions](https://savrn.com/datasets/ship-design-decisions)

[Wenhua Huo](https://savrn.com/model-publishers/wenhuahuo)

The Ship Design Decision Dataset (SDD Dataset) contains cleaned Chinese natural-language requests paired with typed decisions for constrained ship-form editing. SDDBench is the fixed 5,000-record evaluation subset used in the NL2Hull paper. - train.jsonl: 88,604 records - validation.jsonl: 22,758 records - test.jsonl: 23,196 records - test5k.jsonl: frozen 5,000-record benchmark subset - languagesamples.jsonl: source language snapshot The split policy separates hull families across training, validation, and test sets. The training split uses DTC, DTMB 5415, KCS, KVLCC2, containership, frigate, and NPL variants; validation uses S-175 and Series 60; test uses Wigley and workboat hulls. Each…

Publicly accessible 100K<n<1M

[View dataset](https://savrn.com/datasets/ship-design-decisions)

Dataset

### [table_recognition_eval](https://savrn.com/datasets/table-recognition-eval)

[Cheney Wong](https://savrn.com/model-publishers/cheneywong)

Publicly accessible

[View dataset](https://savrn.com/datasets/table-recognition-eval)

Dataset · Text generation

### [texto-clinico-brasileiro](https://savrn.com/datasets/texto-clinico-brasileiro)

[Laboratório de Big Data e Predições em Saúde](https://savrn.com/model-publishers/labdaps)

Dataset unificado de texto clinico e medico em portugues para NLP. Agrega datasets publicos de acesso aberto em um schema Parquet padronizado, com proveniencia (licenca, origem, lingua, sintetico ou nao) registrada em cada registro, para facilitar treino e avaliacao de modelos de linguagem no dominio clinico. Total: 530.099 registros, depois de remover duplicatas exatas de texto. - LusoClin foi gerado por um modelo pequeno (Llama 3.2 3B) e revisado por humanos so em amostra. As notas sao repetitivas e em portugues europeu. Filtre por source ou por metadata.sintetico se isso importar. - MedPT nao declara licenca no card de origem; os textos vem da Doctoralia. - MultiClinSum mistura portugues…

Publicly accessible cc-by-4.0 100K<n<1M

[View dataset](https://savrn.com/datasets/texto-clinico-brasileiro)

Dataset

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

[Pham Xuan Phat](https://savrn.com/model-publishers/phatpham56692)

Publicly accessible

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

Dataset · Speech recognition

### [Burmese-Daily-Dialogue-Corpus](https://savrn.com/datasets/burmese-daily-dialogue-corpus-2)

[Thant Zin Phyo](https://savrn.com/model-publishers/thantzinphyo)

The Burmese Daily Dialogue Corpus ( BDDC ) is a synthetic speech dataset based on everyday conversational speech in Myanmar (Burmese). All 13 speakers in the dataset are synthetic Burmese speakers, and no human individual's voice recordings are used. Each speaker was independently designed to have different Speaking Styles and Voice Characteristics. The two test speakers are unseen speakers who are not included in the training or validation sets. To support speaker-independent evaluation, two speakers who are not included in the training or validation sets are reserved exclusively for the test set. This setup allows models to be evaluated on unseen-speaker generalization. In the…

Publicly accessible cc-by-4.0

[View dataset](https://savrn.com/datasets/burmese-daily-dialogue-corpus-2)

Dataset

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

[Dang Ha Thu Phuong](https://savrn.com/model-publishers/dhthufuong)

configname: crawledmerged datafiles: crawledmerged.jsonl

Publicly accessible

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

Dataset

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

[Noah Mae Castro](https://savrn.com/model-publishers/noahmcastro983)

Publicly accessible

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

Dataset

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

[Benjamin Jane Jimenez](https://savrn.com/model-publishers/jimenezbenjamin9755)

Publicly accessible

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

Dataset

### [eurospeech_da_precomputed_17b](https://savrn.com/datasets/eurospeech-da-precomputed-17b)

[Daniel Biørrith](https://savrn.com/model-publishers/biorrith)

Publicly accessible

[View dataset](https://savrn.com/datasets/eurospeech-da-precomputed-17b)

Dataset

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

[Heather B. Freeman](https://savrn.com/model-publishers/freemanheather1315)

Publicly accessible

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

Dataset · Question answering

### [mmlu-prox-lite-evals](https://savrn.com/datasets/mmlu-prox-lite-evals)

[3ae3ae](https://savrn.com/model-publishers/3ae3ae3ae)

Raw outputs from evaluating recent LLMs on li-lab/MMLU-ProX-Lite (revision e82aafb) with lm-evaluation-harness 0.4.13, in 10 languages (en, zh, ja, ko, pt, de, id, ru, ar, sw). The purpose is within-model diagnosis: for each model, which languages (relative to its own English accuracy on the same parallel questions) and which subjects it is weak in. It is not a leaderboard, and models are not meant to be compared with each other: reasoning effort differs by model (the developer's default for most of them; see the GitHub README). Code, protocol, model settings and summary tables: One folder per model, in the layout lm-evaluation-harness writes: - results.json: accuracy per language and…

Publicly accessible mit 10K<n<100K

[View dataset](https://savrn.com/datasets/mmlu-prox-lite-evals)

Dataset

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

[Bui Thanh Thao](https://savrn.com/model-publishers/thaobui88359)

Publicly accessible

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

Dataset

### [thinkdial_spin](https://savrn.com/datasets/thinkdial-spin)

[Nehemiah](https://savrn.com/model-publishers/fungamer2)

Access requested at publisher

[View dataset](https://savrn.com/datasets/thinkdial-spin)

Dataset

### [federal-ja-documents](https://savrn.com/datasets/federal-ja-documents)

[Abigail Haddad](https://savrn.com/model-publishers/abigailhaddad)

Federal Justification & Approval (J&A) Documents Justification & Approval documents (and similar justifications for other than full and open competition) that federal agencies posted to SAM.gov Contract Opportunities, with the original files, extracted text, and notice metadata. Notices posted from 2025-01-20 through 2026-10-15

Publicly accessible other

[View dataset](https://savrn.com/datasets/federal-ja-documents)

Dataset

### [granite-decisions-synthetic](https://savrn.com/datasets/granite-decisions-synthetic)

[Adam Pippert](https://savrn.com/model-publishers/adampippert)

Original, deterministic English fixtures for Adam Pippert's personal Granite Decisions project. The original default config has 162 examples: 54 train, 54 calibration, and 54 test. These exercise the pipeline; they are not a representative quality benchmark. The source is the project's original template generator, published here as makesmokedata.py, from commit 543345ea033370484ca226424afd73164d48ca35. All dataset content and the generator are MIT licensed; see LICENSE. No third-party dataset or model output was used to generate labels. Jev was used separately for evaluation, never as a source of training labels. Each JSONL record has an id, state.request containing an English software-work…

Publicly accessible mit 10K<n<100K

[View dataset](https://savrn.com/datasets/granite-decisions-synthetic)

Dataset

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

[Pham Hai Thanh](https://savrn.com/model-publishers/thanhpham42136)

Publicly accessible

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

## 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.
