Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
legal
legal-nlp
labse
law
text-embeddings-inference
Instructions to use ahbond/lebse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ahbond/lebse with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ahbond/lebse") 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
- Xet hash:
- ff7b6e9479ff15e415b9f359e4cbb40f55a631245c896ba3318a8bc9f4cefe17
- Size of remote file:
- 13.6 MB
- SHA256:
- edba4e57ec22a2a74bbdb601d3f908e4699c34f8386d52ed055e6fe6bd2b51ac
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