Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
dense
Generated from Trainer
dataset_size:7684
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use shubhamggaur/MedVisionRouter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shubhamggaur/MedVisionRouter with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shubhamggaur/MedVisionRouter") sentences = [ "Are there mitotic figures visible?", "skin biopsy showing inflammatory or neoplastic process", "immunohistochemistry staining for cancer subtyping", "skin surface showing pigmented lesion characteristics" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "__version__": { | |
| "sentence_transformers": "5.1.2", | |
| "transformers": "4.57.6", | |
| "pytorch": "2.8.0+cu128" | |
| }, | |
| "model_type": "SentenceTransformer", | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |