# Datasets, Page 46 of 132 Source: https://savrn.com/datasets/page/46 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. 11,122Models 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 - [10K1T16](https://savrn.com/datasets?size=n%3E1T) - [1B1M1](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 ### [HF005](https://savrn.com/datasets/hf005) [Rofan Mouse](https://savrn.com/model-publishers/data005) Publicly accessible [View dataset](https://savrn.com/datasets/hf005) Dataset ### [16](https://savrn.com/datasets/16) [Miguel Braga Vargas](https://savrn.com/model-publishers/stor16) Publicly accessible [View dataset](https://savrn.com/datasets/16) Dataset ### [p2-etf-sparse-elasticnet-results](https://savrn.com/datasets/p2-etf-sparse-elasticnet-results) [Pranab Pattanaik](https://savrn.com/model-publishers/p2samapa) Publicly accessible [View dataset](https://savrn.com/datasets/p2-etf-sparse-elasticnet-results) Dataset · Tabular classification ### [nifty-session-logs](https://savrn.com/datasets/nifty-session-logs) [Himanshu nagar](https://savrn.com/model-publishers/nagarhimanshu37) This dataset repository contains structured session telemetry, real-time trade signals, risk gate evaluations, agent decisions, and accuracy improvement reports generated by the Nifty AI Brain Agent running autonomously on Kaggle dual T4 GPU environment. 1. Continuous Learning: Track which setups (CPR Breakout, VWAP Rejection, Trap Hunter) perform best. 2. Defect & Weakness Isolation: Automatically identify why signals fail (e.g. low VIX chop, opening 15-min noise, wrong delta). 3. Agent Tuning: Use improvementnotes.md to refine strategy thresholds, risk weights, and LLM reasoning prompts. Automatically synced from Kaggle dual T4 runners via HFSessionPusher. Publicly accessible mit n<1K [View dataset](https://savrn.com/datasets/nifty-session-logs) Dataset ### [Standard-Pipeline](https://savrn.com/datasets/standard-pipeline) [Latency Sensitive Bench](https://savrn.com/model-publishers/latency-sensitive-bench) Environment adaptation, training evidence, latency profiles, raw demonstrations and evaluation traces, organized by environment and experiment stage. - AirRaid: zero-latency and profile-latency experiments. Only raw demonstrations are distributed. Generate converted training datasets on the training server. Model weights remain in the dedicated model repository and are referenced from the experiment records. Pin a commit revision for reproducible downloads. All seven environments use condition/model-family/experiment/stage directories, with each environment’s shared zero-latency data stored once. Existing dataset configuration names and split names are retained and point to the reorganized… Publicly accessible [View dataset](https://savrn.com/datasets/standard-pipeline) Dataset ### [nexus-jobs](https://savrn.com/datasets/nexus-jobs) [Yash Mourya](https://savrn.com/model-publishers/yash514311) Publicly accessible mit [View dataset](https://savrn.com/datasets/nexus-jobs) Dataset ### [bmkg-peta-data](https://savrn.com/datasets/bmkg-peta-data) [Harun](https://savrn.com/model-publishers/harunjo) Publicly accessible [View dataset](https://savrn.com/datasets/bmkg-peta-data) Dataset ### [activations_and_barcodes_3108](https://savrn.com/datasets/activations-and-barcodes-3108) [Sara Veneziale](https://savrn.com/model-publishers/sveneziale) sveneziale/activationsandbarcodes3108 Compute artifacts pushed by tda-for-llms's Hugging-Face-backed pipeline (hf.enabled: true in experiment.yaml). Layout activations/{modelslug}/{corpus}/{revision}/{actname}/ Raw per-cloud activation matrices extracted from the model, one independent copy per checkpoint (revision). Independent of topology.metric — the same activations are reused across every metric or topology config Publicly accessible apache-2.0 [View dataset](https://savrn.com/datasets/activations-and-barcodes-3108) Dataset · Text generation ### [thai-semi-pretrain-qwen](https://savrn.com/datasets/thai-semi-pretrain-qwen) [AI](https://savrn.com/model-publishers/jommarn) ชุดข้อมูลภาษาไทยสำหรับ semi-pretraining: เอกสารเว็บภาษาไทยที่ถูกทำความสะอาดและเรียบเรียงใหม่ด้วย LLM โดยมีบล็อกการวิเคราะห์สั้น ๆ … ฝังอยู่หน้าข้อความ และมีคู่คำถาม-คำตอบ (SFT) พร้อม think full.jsonl (หนึ่งแถวต่อเอกสาร) pretrainwiththink.jsonl / pretrainclean.jsonl: id, type (rewrite), text sft.jsonl: id ( -q ), docid, question, think, answer, messages (รูปแบบแชท: user = คำถาม, assistant = … + คำตอบ) 1. แหล่งข้อมูลดิบ: สตรีมตัวอย่างจาก FineWeb-2 ภาษาไทย (HuggingFaceFW/fineweb-2, thaThai) และ Wikipedia ไทย (wikimedia/wikipedia, 20231101.th) รวม 80,000 เอกสาร (CulturaX ไม่ได้ใช้ เพราะยังไม่ได้รับสิทธิ์เข้าถึง) 2. กรองด้วยกฎ (ไม่ใช้ LLM): normalize Unicode (NFC), ลบอักขระล่องหน/ควบคุม, ลบ URL… Publicly accessible other n<1K [View dataset](https://savrn.com/datasets/thai-semi-pretrain-qwen) Dataset ### [global_sat_metar_v3](https://savrn.com/datasets/global-sat-metar-v3) [Meteolibre.dev](https://savrn.com/model-publishers/meteolibre-dev) Corrected republish of meteolibre-dev/globalsatmetarv2. Same shards, schema, patch grid and satellite/radar/elevation bytes; only the rasterized METAR/SYNOP channel was fixed. The v2 rasterizer used a closed 1-hour window [t-1h, t], so every observation stamped at exactly HH:00:00 (all SYNOP/FM-12 reports, and any station with reporting gaps > 1h) was written into two consecutive hourly frames. ~20-40% of consecutive obs pairs in v2 were exact copies of the same report, which trains a copy-prior ("next frame = last frame") into sequence models. In v3, a frame t+1 whose tmpc, dwpc and mslp are all bit-exact equal to frame t (at least one finite) is treated as the same report leaked across… Publicly accessible [View dataset](https://savrn.com/datasets/global-sat-metar-v3) Dataset ### [MigrationTrace](https://savrn.com/datasets/migrationtrace) [Urcskk2mj](https://savrn.com/model-publishers/urcskk2mj) Publicly accessible mit [View dataset](https://savrn.com/datasets/migrationtrace) Dataset ### [p2-etf-temporal-gnn-results](https://savrn.com/datasets/p2-etf-temporal-gnn-results) [Pranab Pattanaik](https://savrn.com/model-publishers/p2samapa) Publicly accessible [View dataset](https://savrn.com/datasets/p2-etf-temporal-gnn-results) Dataset ### [Nexus_Ulaweng](https://savrn.com/datasets/nexus-ulaweng) [Gibrail](https://savrn.com/model-publishers/gibrail765) Publicly accessible [View dataset](https://savrn.com/datasets/nexus-ulaweng) Dataset · Text generation ### [style](https://savrn.com/datasets/style) [Hub](https://savrn.com/model-publishers/gijl) taskcategories: - text-generation - agent Publicly accessible [View dataset](https://savrn.com/datasets/style) Dataset ### [Agro_Ulaweng](https://savrn.com/datasets/agro-ulaweng) [Gibrail](https://savrn.com/model-publishers/gibrail765) Publicly accessible [View dataset](https://savrn.com/datasets/agro-ulaweng) Dataset ### [requests](https://savrn.com/datasets/requests-2) [Mushari Alothman](https://savrn.com/model-publishers/mushari440) Publicly accessible [View dataset](https://savrn.com/datasets/requests-2) Dataset ### [sweeperbench-traces](https://savrn.com/datasets/sweeperbench-traces) [Tony Chen](https://savrn.com/model-publishers/tonychenxyz) Sweeperbench run database runs.csv is the append-only raw table. Each row describes one prediction, core evaluation, or preservation evaluation. schema.json documents the fields. Primary key: runid (UUID). Each top-level UUID directory is exactly that run, with matching metadata.json, artifact manifest, prompts, traces, verdicts and any collected recordings. Relationships: experimentid groups a launch; evaluations reference the exact predictionrunid. Model, harness Publicly accessible [View dataset](https://savrn.com/datasets/sweeperbench-traces) Dataset ### [Arquivos](https://savrn.com/datasets/arquivos) [Square](https://savrn.com/model-publishers/riptide069) Publicly accessible [View dataset](https://savrn.com/datasets/arquivos) Dataset ### [15](https://savrn.com/datasets/15) [João Xavier Magalhães](https://savrn.com/model-publishers/15set) Publicly accessible [View dataset](https://savrn.com/datasets/15) Dataset ### [fec-dumps](https://savrn.com/datasets/fec-dumps) [Nick Crews](https://savrn.com/model-publishers/nickcrews) parquet versions of the Schedule A and Schedule B tables from the weekly postgres.dump backups from the Federal Election Commission's database. Published on a weekly cron job by https://github.com/NickCrews/fec-dumps, see that for more info. Publicly accessible mit [View dataset](https://savrn.com/datasets/fec-dumps) Dataset ### [c4d-scenes-previews](https://savrn.com/datasets/c4d-scenes-previews) [Davd](https://savrn.com/model-publishers/smerkd) Public browser assets for the C4D scene viewer, including the wider-family gallery. The full training exports are in smerkd/c4d-scenes. This repository contains browser previews and display-mapped supervision examples, not full training supervision. Browser videos are compressed previews; training RGB is stored losslessly in the scene archives. Gaussian displays use a bounded subset with experimental, uncertified covariance/opacity conventions. Preview assets are checksum-verified and gallery URLs refer to immutable revisions. Publicly accessible [View dataset](https://savrn.com/datasets/c4d-scenes-previews) Dataset · Text generation ### [nepali-agri-gov-instruct](https://savrn.com/datasets/nepali-agri-gov-instruct) [Aaraj Bhatar](https://savrn.com/model-publishers/aarajbhattarai) Synthetic Nepali instruction-tuning data generated with NVIDIA NeMo Data Designer from authoritative Nepali documents (agriculture manuals, legal texts). Answers are grounded strictly in the source; unanswerable questions get an explicit refusal. Records use chat messages format plus metadata and per-record qualityscores (grounding / correctness / naturalness, 1-5, LLM-as-judge). One data/train-.jsonl per source document; shards are overwritten idempotently on re-runs. (grounding>=4, correctness>=3, naturalness>=3). Companion repos hold quality-gate rejects (rejected-) and records whose judge call failed (unjudged-). Publicly accessible [View dataset](https://savrn.com/datasets/nepali-agri-gov-instruct) Dataset ### [wan22-5s](https://savrn.com/datasets/wan22-5s) [Nzc](https://savrn.com/model-publishers/nzc-415) Publicly accessible [View dataset](https://savrn.com/datasets/wan22-5s) Dataset ### [security](https://savrn.com/datasets/security) [Spencer bromberg](https://savrn.com/model-publishers/spencerbromberg) Publicly accessible [View dataset](https://savrn.com/datasets/security) ## 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.