train-sentence-transformers

出处与授权

本站只收录项目元数据与简介，不对作者正文做转载；完整内容请到上方出处查看。商用前请自行核对原仓库的 LICENSE 与 NOTICE。

⚠ 上游仓库根目录未找到 LICENSE/COPYING 文件，以上许可证名来自第三方登记、未经核实。请到出处自行确认后再商用。

产品介绍

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), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

资源信息

许可证信息摘自官方源，可能过时或缺失。商用前请以原仓库的 LICENSE 文件为准。

功能说明

• •1. Identify the model type
• •2. Required reading
• •Per-type: always required
• •Cross-cutting: always required (regardless of task)
• •Cross-cutting: load when applicable
• •Variant scripts (open when the task matches)
• •3. Defaults
• •4. Constraints the produced script must satisfy

适用平台