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
File size: 384 Bytes
a943ace | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"__version__": {
"pytorch": "2.11.0+cu128",
"sentence_transformers": "5.5.1",
"transformers": "5.8.1"
},
"default_prompt_name": null,
"model_type": "SentenceTransformer",
"prompts": {
"document": "\u691c\u7d22\u6587\u66f8: ",
"query": "\u691c\u7d22\u30af\u30a8\u30ea: ",
"topic": "\u30c8\u30d4\u30c3\u30af: "
},
"similarity_fn_name": "cosine"
} |