Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:148295
loss:SymmetricLoss
text-embeddings-inference
Instructions to use WeihaoLi/icd9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use WeihaoLi/icd9 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("WeihaoLi/icd9") sentences = [ "Complications of pregnancy; childbirth; and the puerperium → Complications during labor → Forceps delivery", "Complications of pregnancy; childbirth; and the puerperium → Complications during labor", "Complications of pregnancy; childbirth; and the puerperium → Other complications of birth; puerperium affecting management of mother", "Complications of pregnancy; childbirth; and the puerperium → Normal pregnancy and/or delivery → Other pregnancy and delivery including normal" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 309b55a641efd070173c39611b3722217ef7298382e9755e99d9902d40ad7c2a
- Size of remote file:
- 132 MB
- SHA256:
- 3267591bee712e2691655d4cba5c11e29e7e632573f0511bd0a47cface95cf71
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