Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
text-embeddings-inference
Instructions to use champ7/LOINC_CROSS_ENCODER1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use champ7/LOINC_CROSS_ENCODER1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("champ7/LOINC_CROSS_ENCODER1") 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:
- 0017c82997bf5d2bc936ab0ccdf4c2c6e0d7a03ee1788dc7d1329e2cb96b98f8
- Size of remote file:
- 33.4 MB
- SHA256:
- 11acfd476d36d457f3354aef39e503155cbcd3dfbf8a6c335ec4ecaca87ee437
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