Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:1322
loss:MultipleNegativesRankingLoss
medical
clinical
traditional-medicine
ASU
NAMASTE
text-embeddings-inference
Instructions to use 0xsoh/namaste-asu-matcher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use 0xsoh/namaste-asu-matcher with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("0xsoh/namaste-asu-matcher") sentences = [ "febricity s, unspecified", "Increase of bone tissues pattern Physical constituent derangement patterns (ICD-11: SR49)", "Dry chapped lip disorder Gastro-intestinal disorders -> Oral cavity disorders (ICD-11: SM1M)", "Febricity disorders, unspecified Disorders affecting the whole body -> Febricity disorders (ICD-11: SP5Z)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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