Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:67290
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use Ganaraj/embeddinggemma-medical-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Ganaraj/embeddinggemma-medical-ft with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Ganaraj/embeddinggemma-medical-ft") sentences = [ "task: search result | query: Radiological devices associated with adverse incidents, needles used in radiologically-guided biopsies", "title: none | text: Radiological devices associated with injury or harm, diagnostic or monitoring devices", "title: none | text: Other specified duplications of chromosome 3", "title: none | text: Quintuplets, all stillborn" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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