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
Update README.md
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README.md
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from sentence_transformers import MultiVectorEncoder
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# Download from the 🤗 Hub
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model = MultiVectorEncoder("
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# Run inference: each input becomes a sequence of per-token vectors (variable length).
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queries = [
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'How does the polymorphism in the IL28B gene region 8099917 affect the response to treatment in patients with AHC?\n',
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from sentence_transformers import MultiVectorEncoder
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# Download from the 🤗 Hub
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model = MultiVectorEncoder("multi-vector-encoder/mLateOn-medical")
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# Run inference: each input becomes a sequence of per-token vectors (variable length).
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queries = [
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'How does the polymorphism in the IL28B gene region 8099917 affect the response to treatment in patients with AHC?\n',
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