Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use sudhir2016/setfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use sudhir2016/setfit with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("sudhir2016/setfit") - sentence-transformers
How to use sudhir2016/setfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sudhir2016/setfit") 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:
- 881e356a3d8be72e57d6cb7e996627126777f4270423be0473faf1ebf3affdc2
- Size of remote file:
- 6.99 kB
- SHA256:
- d31733e2d97d618f0a5695ddc8ff894fc4369f67cb58f89919ab44938f5a1c1b
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