Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:206874
loss:ContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use hyojuuun/gte_base_MIMICCXR_FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hyojuuun/gte_base_MIMICCXR_FT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hyojuuun/gte_base_MIMICCXR_FT") sentences = [ "Cardiac silhouette size is top normal. Aorta is tortuous and demonstrates mild atherosclerotic calcifications diffusely. Hilar contours are normal. Pulmonary vasculature is normal. Lungs are clear. No pleural effusion or pneumothorax is present. No acute osseous abnormality is detected. ", "No acute cardiopulmonary process. ", "No acute cardiopulmonary abnormality. ", "Normal chest radiographs. " ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle