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-ruri 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-ruri with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("genshiai-daichi/med-ruri-v3-70m-v2-from-ruri") 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 4000
Browse files- metrics_progress.json +2 -1
metrics_progress.json
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"2000": 0.43330019996953195,
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"2500": 0.44659083423553114,
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"3000": 0.4496575061053132,
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"3500": 0.4608934483931739
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}
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"2000": 0.43330019996953195,
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"2500": 0.44659083423553114,
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"3000": 0.4496575061053132,
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"3500": 0.4608934483931739,
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"4000": 0.4708195055654097
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}
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