Sentence Similarity
sentence-transformers
Safetensors
Japanese
modernbert
medical
japanese
ruri
embedding
text-embeddings-inference
Instructions to use genshiai-daichi/med-ruri-v3-70m-v2-from-med with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use genshiai-daichi/med-ruri-v3-70m-v2-from-med with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("genshiai-daichi/med-ruri-v3-70m-v2-from-med") 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
metrics @ step 4500
Browse files- metrics_progress.json +2 -1
metrics_progress.json
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"2500": 0.4668654830792379,
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"3000": 0.47131302354355625,
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"3500": 0.48396495520080973,
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"4000": 0.49194027570043475
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}
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"2500": 0.4668654830792379,
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"3000": 0.47131302354355625,
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"3500": 0.48396495520080973,
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"4000": 0.49194027570043475,
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"4500": 0.49833703167334126
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}
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