Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use davidoneil/bge-full-tron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use davidoneil/bge-full-tron with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("davidoneil/bge-full-tron") 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 sparse_linear.pt from davidoneil/bge-full-tron: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/davidoneil/bge-full-tron/resolve/main/sparse_linear.pt
- Command line
-
hf download hf://davidoneil/bge-full-tron/sparse_linear.pt
-
curl -L -o sparse_linear.pt https://huggingface.co/davidoneil/bge-full-tron/resolve/main/sparse_linear.pt
3.52 kB
- Xet hash:
- 6171418c5ec09abf05fb7b020176207fe52d20e9242b72c51dcda27df1c90bbf
- Size of remote file:
- 3.52 kB
- SHA256:
- 2d94f2e81c64e2eddc2b3dff07dbec023f0c38b49ce1697593f2d340cb5788b7
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