Feature Extraction
sentence-transformers
Safetensors
modernbert
multi-vector
colbert
late-interaction
Generated from Trainer
dataset_size:1000000
loss:CachedMultiVectorMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use multi-vector-encoder/mLateOn-medical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use multi-vector-encoder/mLateOn-medical with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("multi-vector-encoder/mLateOn-medical") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 144db9822aad4c0e6b608776395e3a6e612fff8eff534998567b0d68645ce37d
- Size of remote file:
- 34.4 MB
- SHA256:
- 6ba332f5e19ec3f9f386a581a83e92e46de70c77224df1a10939a95d5079edb1
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