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
| { | |
| "500": 0.42346580839400816, | |
| "1000": 0.4370587039801932, | |
| "1500": 0.4497772162074601, | |
| "2000": 0.45651138585926065, | |
| "2500": 0.4668654830792379, | |
| "3000": 0.47131302354355625, | |
| "3500": 0.48396495520080973, | |
| "4000": 0.49194027570043475, | |
| "4500": 0.49833703167334126, | |
| "5000": 0.5061113217420127, | |
| "5500": 0.5043877142438612, | |
| "6000": 0.5116565481291894, | |
| "6159": 0.5116675566402742 | |
| } |