# Datasets, Page 81 of 132
Source: https://savrn.com/datasets/page/81
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

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

[Lilia](https://savrn.com/model-publishers/monikou123)

Publicly accessible

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

Dataset

### [Results_main_architecture_triple_video_tie_breakers](https://savrn.com/datasets/results-main-architecture-triple-video-tie-breakers)

[Tran Van Manh](https://savrn.com/model-publishers/manhmitcf)

Publicly accessible

[View dataset](https://savrn.com/datasets/results-main-architecture-triple-video-tie-breakers)

Dataset

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

[Haoliang Huang](https://savrn.com/model-publishers/virlight)

Access requested at publisher

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

Dataset

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

[Jessica Kent Ramos](https://savrn.com/model-publishers/jessicakramos9931)

Publicly accessible

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

Dataset · Text classification

### [extracao_estruturada](https://savrn.com/datasets/extracao-estruturada)

[Potelo](https://savrn.com/model-publishers/potelo)

Estados textuais e perguntas de decisão tipada para treinar um extrator no formato Jev. A fonte cobre timelines processuais, documentos de timeline, fragmentos OCR e questões de concurso. Os exemplos foram gerados por um único modelo professor, com filtros locais; este release não é uma avaliação humana. Validação: 0.9999% dos estados, no máximo 1%. Todas as linhas da mesma família documental ou processo ficam no mesmo split. Não há splits de calibração ou teste. O snapshot termina no ID 327039 do banco e está descrito em manifest.json. state é o texto de entrada. questionsjson é um array JSON de perguntas com id, type, instructions, criteria, target, labelsource="dataset" e weight=0.5.…

Publicly accessible 100K<n<1M

[View dataset](https://savrn.com/datasets/extracao-estruturada)

Dataset

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

[William Kendall Rivera](https://savrn.com/model-publishers/williamkrivera3234)

Publicly accessible

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

Dataset · Text generation

### [legal-ir-autoencoder-checkpoints](https://savrn.com/datasets/legal-ir-autoencoder-checkpoints)

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

This dataset stores checkpoint artifacts for the legal text -> formal logic IR autoencoder/Codex optimization loop. - state/legal-ir-autoencoder-canonical.state.json: canonical feature-level autoencoder warm-start state. - reports/: weight review, deprecation manifest, and consensus feature manifests. - scripts/reviewautoencoderweightruns.py: script used to classify runs and build the canonical state. - smoke/: summaries/logs from short local smoke tests proving default canonical warm-start loading. - manifest.json: checksums, source run IDs, merge weights, and reproducibility metadata. The state is built through ModalAutoencoderTrainingState.loadjson(path).generalizablecopy(), so it…

Publicly accessible mit

[View dataset](https://savrn.com/datasets/legal-ir-autoencoder-checkpoints)

Dataset · Multiple choice

### [nfcore-laya-decisions](https://savrn.com/datasets/nfcore-laya-decisions)

[PrimeOmicX LLC](https://savrn.com/model-publishers/primeomicx)

A standardized, multi-task decision-layer dataset for training Laya (System-1 Decision Engines) and fast autonomous agents across Nextflow DSL2 and the complete nf-core bioinformatics ecosystem. 1. QC Read Adaptation (1,500 records): Illumina NovaSeq/NextSeq, PacBio HiFi CCS, Oxford Nanopore Direct RNA/DNA quality control adaptation and tool swapping. 2. Resource Profiling & Directives (1,500 records): Process memory, CPU, time, and dynamic retry/error-strategy directive rules across genome-scale tools. 3. Subworkflow Packaging (622 records): Determining when to bundle multi-process tasks into local subworkflows or reuse official nf-core subworkflows. 4. Samplesheet Schema & Header…

Publicly accessible mit 10K<n<100K

[View dataset](https://savrn.com/datasets/nfcore-laya-decisions)

Dataset · Object detection

### [raccoon-corrected](https://savrn.com/datasets/raccoon-corrected)

[KeenForgeAI](https://savrn.com/model-publishers/keenforgeai)

A fully human-reviewed, re-annotated version of the classic Raccoon object detection dataset. Every one of the 193 images was manually reviewed; loose, missing, and incorrect bounding boxes were corrected. Every image was compared with the original VOC annotations using greedy IoU matching (matched ≥ 0.5, unchanged ≥ 0.95): Key observations - The original boxes were often loose (e.g., raccoon-1: box (81,88)-(522,408) → tightened to (80,105)-(529,405), IoU 0.91). The average IoU of matched boxes is 0.810, i.e. the corrected boxes bound the raccoon noticeably more tightly. - Occluded / partially visible raccoons were kept with tight boxes; several missed instances were added. - 7 exact/near…

Publicly accessible mit n<1K

[View dataset](https://savrn.com/datasets/raccoon-corrected)

Dataset

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

[Xz](https://savrn.com/model-publishers/zxc1bnm2)

Publicly accessible

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

Dataset

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

[Jason James Alvarez](https://savrn.com/model-publishers/alvarezjason9509)

Publicly accessible

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

Dataset

### [IWR-Bench-Output](https://savrn.com/datasets/iwr-bench-output)

[Dasen Dai](https://savrn.com/model-publishers/desondai)

Publicly accessible

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

Dataset

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

[ED](https://savrn.com/model-publishers/riot28)

Publicly accessible

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

Dataset

### [biemaapache1809](https://savrn.com/datasets/biemaapache1809)

[Saophaixoan1](https://savrn.com/model-publishers/saophaixoan1)

Publicly accessible

[View dataset](https://savrn.com/datasets/biemaapache1809)

Dataset · Image segmentation

### [ocean-eddy-datasets](https://savrn.com/datasets/ocean-eddy-datasets)

[Bingzheng Yan](https://savrn.com/model-publishers/bingzheng27)

Figure 1. Visualization example from the SCSE-Eddy dataset. The left panel shows SSH intensity, and the right panel shows the corresponding ground-truth eddy segmentation label. This repository provides ocean eddy datasets for semantic segmentation and a curated PredCR candidate-level instance package for reliability reasoning and concept-based analysis. The datasets are designed for pixel-level identification of mesoscale ocean eddies using deep learning methods. They are mainly used for experiments involving EddyNet, DUNet-like segmentation baselines, and future neuro-symbolic verification and correction frameworks. The repository currently contains two ocean eddy segmentation datasets…

Publicly accessible other

[View dataset](https://savrn.com/datasets/ocean-eddy-datasets)

Dataset

### [sia-45.5-sod-11.5](https://savrn.com/datasets/sia-45-5-sod-11-5)

[Shufan Jiang](https://savrn.com/model-publishers/tsumugii)

Publicly accessible

[View dataset](https://savrn.com/datasets/sia-45-5-sod-11-5)

Dataset

### [sid-31.6-soa-21.9](https://savrn.com/datasets/sid-31-6-soa-21-9)

[Shufan Jiang](https://savrn.com/model-publishers/tsumugii)

Publicly accessible

[View dataset](https://savrn.com/datasets/sid-31-6-soa-21-9)

Dataset

### [APPL-assets](https://savrn.com/datasets/appl-assets)

[Oscar Jiang](https://savrn.com/model-publishers/oscattt)

Release assets for Agent Priors-guided Policy Learning (APPL). These archives contain the paper's Exp1 and final five-task Exp2 data, trained policies and detailed evaluation evidence. Agent+VLA and real-robot artifacts are outside this distribution. SHA256SUMS contains the SHA256 of all three archives. README.txt provides the matching source commit and installation commands. The two experiment archives contain 191,640 files and 222 trained checkpoints in total. Download the three archives, SHA256SUMS and README.txt into one directory. Use the specific release tag or commit from Files and versions for repeatable downloads. These are experiment archives installed by the APPL release tools…

Publicly accessible mit

[View dataset](https://savrn.com/datasets/appl-assets)

Dataset

### [deductive_reasoning_results](https://savrn.com/datasets/deductive-reasoning-results)

[Khusham Bansal](https://savrn.com/model-publishers/bansalkhusham)

Access requested at publisher

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

Dataset · Text retrieval

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

[Benjamin J Barber](https://savrn.com/model-publishers/endomorphosis)

Municipal by-laws of Canada, normalized to the shared 19-column worldmunicipal schema used by the world municipal-law program (one row per section/article; one parquet per municipality under data/ html.parquet). 15 consolidated bylaw documents, 1210 rows: Jurisdiction IDs are Statistics Canada SGC census subdivision codes (7 digits: province + census division + census subdivision). The denominator is 3489 municipal-government CSDs of 5161 total CSDs (SGC 2021, Statistics Canada table 17-10-0155); the full registry is at registry/sgcregistry2021.jsonl. See coveragereport.md for the route survey — including why the dominant CivicWeb platform is excluded (robots Disallow: / for the wildcard…

Publicly accessible other

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

Dataset

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

[Wilbert Pumacay](https://savrn.com/model-publishers/gregor-ws)

Publicly accessible apache-2.0

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

Dataset

### [nssort_s](https://savrn.com/datasets/nssort-s)

[Darealsven](https://savrn.com/model-publishers/darealsven)

Publicly accessible

[View dataset](https://savrn.com/datasets/nssort-s)

Dataset

### [anticloud-pombola](https://savrn.com/datasets/anticloud-pombola)

[The Anticloud by Lois-Kleinner](https://savrn.com/model-publishers/anticloud)

POMBOLA is an open-source project in the GOVERNMENT category. This project provides tools, libraries, and functionality for developers and end users working in the GOVERNMENT domain. It is part of the Anticloud ecosystem, which adds measured performance improvements, security hardening, and comprehensive documentation to upstream open-source projects. The project addresses key challenges in GOVERNMENT by providing: - Standard interfaces compatible with the broader ecosystem - Integration points for the Anticloud overlay improvements Before installing POMBOLA, ensure you have the following: - A compatible operating system (Linux, macOS, or Windows) - Required build tools as specified by the…

Publicly accessible

[View dataset](https://savrn.com/datasets/anticloud-pombola)

Dataset

### [ds-9f4c2e](https://savrn.com/datasets/ds-9f4c2e)

[Thejeffadrian Lab](https://savrn.com/model-publishers/thejeffadrian-lab)

Access-gated working data for an in-progress quantitative research project. Access is granted individually; request access if you have been directed here by one of the authors. Not intended for redistribution. Schema and layout are being migrated; pin a commit when running an analysis. Markets contains 9,607,393 outcome rows across all 7,845,001 inventoried native markets, including excluded and pending records. Previously reviewed metadata is preserved. This is not a count of eligible markets or a complete census of either exchange. Quotes and provider trade candles are being populated from the full archived Kalshi one-minute candle family, using the verified exact-copy removal map. These…

Access requested at publisher

[View dataset](https://savrn.com/datasets/ds-9f4c2e)

## 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.