train-sentence-transformers
Skill1 starsChanged 4 months agoNo licenceView on GitHub
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder; dense or static embedding model; for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker; pair scoring for two-stage retrieval / pair classification), and `SparseEncoder` (SPLADE, sparse embedding model; for learned-sparse retrieval). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.
- Installs packages
We split this skill into the rules it gives an agent
2 rules. 0 are common, 0 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 5 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.
“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Other versions6 projects use a version of this.
Word for word · 1
- Adhamxon/claude-code-skills★ 2100% same
Edited · 4
- huggingface/sentence-transformers★ 19k84% same
- huggingface/skills★ 11k84% same
- waybarrios/opencode-power-pack★ 54797% same
- PracticalSwan/agent-skills★ 2267% same
Wrote their own version · 1
- tim-osterhus/millrace-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
Used alongsideProjects with it also use huggingface-llm-trainer.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
Hugging FaceSkillhuggingface-llm-trainer
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
Also in 3 projects that use this
In this repository36 other files it gives its agents.
- algorithmic-artSkill
- brainstormingSkill
- brand-guidelinesSkill
- canvas-designSkill
- claude-apiSkill
- dispatching-parallel-agentsSkill
- doc-coauthoringSkill
- docxSkill
- executing-plansSkill
- firecrawl-build-interactSkill
- firecrawl-build-scrapeSkill
- firecrawl-build-searchSkill
- firecrawl-research-indexSkill
- frontend-designSkill
- gurta-contextSkill
- huggingface-datasetsSkill
- internal-commsSkill
- mcp-builderSkill
- pdfSkill
- playwright-skillSkill
- pptxSkill
- puppeteer-skillSkill
- pytest-skillSkill
- requesting-code-reviewSkill
- skill-creatorSkill
- slack-gif-creatorSkill
- subagent-driven-developmentSkill
- systematic-debuggingSkill
- test-driven-developmentSkill
- theme-factorySkill
- verification-before-completionSkill
- vitest-skillSkill
- webapp-testingSkill
- web-artifacts-builderSkill
- writing-plansSkill
- xlsxSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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