Sentence Similarity
sentence-transformers
Safetensors
roberta
feature-extraction
dense
Generated from Trainer
dataset_size:39308
loss:CosineSimilarityLoss
text-embeddings-inference
Instructions to use srirangamuc/pathology-sbert-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use srirangamuc/pathology-sbert-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("srirangamuc/pathology-sbert-finetuned") sentences = [ "does this image show lymph node, mycobacterium avium-intracellulae mai?", "good example either chest", "yes", "cut surface both testicles on normal and one quite small typical probably due to mumps" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from srirangamuc/pathology-sbert-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/srirangamuc/pathology-sbert-finetuned/resolve/main/modules.json
- Command line
-
hf download hf://srirangamuc/pathology-sbert-finetuned/modules.json
-
curl -L -o modules.json https://huggingface.co/srirangamuc/pathology-sbert-finetuned/resolve/main/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |