背景：某项目中涉及Sentence-BERT 模型
Sentence Transformers (a.k.a. SBERT) is the go-to Python module for using and training state-of-the-art embedding and reranker models. It can be used to compute embeddings from text, images, audio, or video using Sentence Transformer models (quickstart), to calculate similarity scores using Cross-Encoder (a.k.a. reranker) models (quickstart), or to generate sparse embeddings using Sparse Encoder models (quickstart). This unlocks a wide range of applications, including semantic search, semantic textual similarity, and paraphrase mining.
Sentence Transformers was created by UKP Lab and is being maintained by 🤗 Hugging Face.
Installation
pip install -U sentence-transformers # Python 3.10+ and PyTorch 1.11.0+
Pretrained Models
via Sentence Transformers Hugging Face organization
from sentence_transformers import SentenceTransformer
# Load https://huggingface.co/sentence-transformers/all-mpnet-base-v2
model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
embeddings = model.encode([
"The weather is lovely today.",
"It's so sunny outside!",
"He drove to the stadium.",
])
similarities = model.similarity(embeddings, embeddings)
离线使用模型,例如:all-MiniLM-L6-v2
- 使用
huggingface-cli```shell #国内网络加速（镜像站） uv pip install huggingface_hub
手动下载时，把文件 URL 中的 huggingface.co 替换为 hf-mirror.com
export HF_ENDPOINT=https://hf-mirror.com
huggingface-cli download sentence-transformers/all-MiniLM-L6-v2 –local-dir ./all-MiniLM-L6-v2
uvx huggingface-cli download sentence-transformers/all-MiniLM-L6-v2 –local-dir ./all-MiniLM-L6-v2
- 手动从 Hugging Face 网页下载
- [**未验证**]用 sentence-transformers 自动下载（需联网一次）
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
# 下载后模型会自动存放在 ~/.cache/torch/sentence_transformers/ 下
# 在离线代码中加载时，直接传入本地路径即可：
model = SentenceTransformer('/your/path/all-MiniLM-L6-v2')