Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use ashercn97/medicalcode-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use ashercn97/medicalcode-classifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("ashercn97/medicalcode-classifier") - sentence-transformers
How to use ashercn97/medicalcode-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ashercn97/medicalcode-classifier") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4b9ce12f4e4764b39902bd99d15159178b0ac77b5e475a1b926627d4195e869
- Size of remote file:
- 7.06 kB
- SHA256:
- e65285f19c0e24f39ed9ae40fa915b187adf9f8a7b7a0e72a8d094e13e880527
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