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.
Updated 2026-10-18 · How the library is built
3,164 datasets, sorted by most downloaded.
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference A web-based interface for preparing audio datasets to fine-tune OpenAI's Whisper model. This tool helps in recording, managing, and organizing voice recordings with their corresponding transcriptions, with support for cloud storage and authentication. - ⌨ Keyboard shortcuts for efficiency 1. Create a transcript CSV file with your content: 2. Start the Flask application: 3. Access the interface: 1. Authentication 2. Session Setup - Click "Start Session" 3. Recording - Use on-screen controls or keyboard shortcuts: - R: Start recording / Stop recording - Space: Play recording - Enter: Save…
v0.0.1, revised 29 September 2026. Research release, complete text. This revision is built from internal release v1.3. The first upload of v0.0.1, built from internal release v1.2, stays at Hub revision 3e3ed7f3bbed83b6d12316d2b5b396acdfc40873 (tag v0.0.1), so results computed on it remain traceable; CHANGELOG.md lists every change. Code: https://github.com/raxITlabs/goldrails at c468c82204cf3acb20821cf41df17e0d4ead9e78. Every row of release v1.3 (release sha 818c9e00f9f8, 8807 rows) ships with its text, context, labels and span annotations. RIGHTS.md records the basis for publishing the sources that earlier shipped without text. [gold]rails measures guardrails on six capabilities: harmful…
This repository contains the complete rlbenchunseentasks512aug dataset. The original directory tree is stored losslessly as one uncompressed tar shard per RLBench task under archives/. Extract all shards into a dataset directory: The generation-only texturepool and the separate mixtureroot datasets are not required to read or train on this dataset and are intentionally excluded.
This dataset contains clean, structured public data exported directly from production runs of Apify actors. It serves as a benchmark and sample for lead generation, labor market intelligence, and compliance verification. Clean structured extract of active software engineering, full stack, and AI developer job listings in the Austin, Texas metro area. Includes canonical job URLs, company names, employer ratings, location tags, salary estimates, raw posting age (e.g. 24h, 3d), and normalized estimated posting dates. Generated via Apify Actor captainhandsome/glassdoor-jobs-scraper. - jobtitle: (e.g. Entry-level Software Developer) - joburl: (e.g.…
TRACE-OU-Bench is a sequential benchmark for ontology-update decisions under evolving ontology states. It contains 620 human-reviewed ontology-update proposals generated from public U.S. SEC 10-Q filings covering 45 U.S. companies, five sectors, and 2016–2026. The public release does not redistribute SEC filing text. Instead, each record retains SEC provenance, deterministic chunk identifiers, offsets, and SHA-256 hashes. reconstruction/traceoureconstructv11.py downloads the original filing from SEC EDGAR and deterministically reconstructs the source chunk. - Train (2016–2023): 350 proposals generated against O0; reviewed Train updates build O1. - Test (2025–2026): 150 proposals generated…
MADBench-Full contains 5,200 fully labeled execution traces from five LLMs solving synthetic escape-room tasks. The updated release adds paired tool-enabled and no-tool runs over two clue domains, making it possible to study tool selection, argument construction, tool execution failures, error recovery, and downstream error propagation in a controlled multi-agent system. Four models have 1,200 traces each: 2 clue domains × 2 tool modes × 3 temperatures × 100 rooms. claude-opus-4-8 has 400 traces at temperature 0.0 only. Each room embeds one or more reasoning problems inside escape-room clues: - gsm-hard uses challenging arithmetic word problems. - livecodebench uses chained Python programs.…
Audit tables for a small gas-imaging study on baselab's Controlled Diet dataset, which is laboratory methane from rumen fermentation. Its 137 rows describe 42 source images from nine recordings. They are not images, chemical measurements or gas-free sessions. The spatial prior's comparison IoU is 0.138078, against 0.026416 for the tiny MLP and 0.022555 for logistic regression: both learned models lose. Empty cells are undefined metrics, not zeros. backgroundpixeloverpredictionratio measures pixel overprediction against weak masks. It is never a gas false-alarm rate. Models, weights and the full report are in by Mohamed G. Embaby, Toqi Tahamid Sarker, Amer AbuGhazaleh and Khaled R. Ahmed…
justicedao/ipfsuklawsir UK laws IR release.
This repository is a convenience collection of the NVIDIA Nemotron Pretraining Specialized releases. It preserves each original subset as a separate Hugging Face configuration, so consumers can select a single domain-focused subset without visiting multiple source repositories. Every configuration has a train split. Configuration names begin with the source release (v1, v1.1, or v1.2) to keep similarly named subsets unambiguous. Replace the configuration name with any name listed in the repository’s configuration selector. Schemas can differ by source subset; inspect dataset.features after loading rather than assuming a common schema. The records are redistributed from the source releases…
PriorGen 使用的数据独立存放于此目录，代码项目位于 /mnt/datadisk0/PriorGen。数据按用途组织： ~text PriorGenData/ ├── features/uni/ # UNI WSI patch features (.pt) ├── features/plip/ # PLIP WSI patch features (.pt) ├── annotations/organs/ # TCGA organ report annotations and images ├── splits/ # train/val/test CSV and label mappings ├── knowledgebanks/ # organ knowledge banks (.pt/.json) ├── anchors/ # PIB anchor features (.pt) ├── DATASETCARD.md # 数据集说明与统计 └── datasetmanifest.json 这些目录是从原有位置移动而来，原位置保留了兼容软链接。上传或迁移时，优先完整打包 PriorGenData，再单独打包 PriorGen 代码目录。 代码默认通过 PRIORGENDATAROOT=/mnt/datadisk0/PriorGenData 读取这里的数据，也可以在 PriorGen/.env.example 中修改路径。
Predicted monomer structures for 292,804 unique protein sequences: 38,934 from a Gene Ontology molecular-function coupling set and 254,182 audited ancestral sequence reconstructions (312 sequences occur in both). The predictions serve as teacher labels for 1,421,494 training pairs; one structure is reused by every pair referencing that exact sequence. Sequence IDs are seq, so a prediction can always be matched back to its exact sequence. Stock open-source AlphaFold 3 with default settings — no protocol Both MSAs AlphaFold 3 builds are retained: the unpaired MSA and the paired (UniProt) MSA. The paired MSA is kept even though every input is a single chain, because AlphaFold 3 featurises it…
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
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.
Collection · 4 entries
Models that fit 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.
Collection · 6 entries
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.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
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
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.