Sentence Similarity
sentence-transformers
PyTorch
ONNX
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use Dioleti/bge-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Dioleti/bge-m3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Dioleti/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] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Dioleti/bge-m3: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Dioleti/bge-m3/resolve/main/tokenizer.json
- Command line
-
hf download hf://Dioleti/bge-m3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Dioleti/bge-m3/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 4702ee9935e9f7269c6ea843b54e61fb68515ac2b383365d9fdc2018ebaa34d9
- Size of remote file:
- 17.1 MB
- SHA256:
- 21106b6d7dab2952c1d496fb21d5dc9db75c28ed361a05f5020bbba27810dd08
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