Sentence Similarity
sentence-transformers
PyTorch
ONNX
xlm-roberta
feature-extraction
Eval Results
text-embeddings-inference
Instructions to use BAAI/bge-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-m3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-m3") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
- AMD Developer Cloud
Download config_sentence_transformers.json from BAAI/bge-m3: direct link, hf CLI and curl.
- Browser
- Download file 123 Bytes
-
https://huggingface.co/BAAI/bge-m3/resolve/refs%2Fpr%2F95/config_sentence_transformers.json
- Command line
-
hf download hf://BAAI/bge-m3@refs/pr/95/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/BAAI/bge-m3/resolve/refs%2Fpr%2F95/config_sentence_transformers.json
123 Bytes
| { | |
| "__version__": { | |
| "sentence_transformers": "2.2.2", | |
| "transformers": "4.33.0", | |
| "pytorch": "2.1.2+cu121" | |
| } | |
| } |