Text Classification
setfit
Safetensors
sentence-transformers
mpnet
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use amitprgx/setfit-categorization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use amitprgx/setfit-categorization with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("amitprgx/setfit-categorization") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use amitprgx/setfit-categorization with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("amitprgx/setfit-categorization") 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
Download model_head.pkl from amitprgx/setfit-categorization: direct link, hf CLI and curl.
- Browser
- Download file 215 kB
-
https://huggingface.co/amitprgx/setfit-categorization/resolve/main/model_head.pkl
- Command line
-
hf download hf://amitprgx/setfit-categorization/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/amitprgx/setfit-categorization/resolve/main/model_head.pkl
215 kB
- Xet hash:
- 873d74b5e6044ea9035e0489c684d4527b537bb664af595bc52f34894eb36b91
- Size of remote file:
- 215 kB
- SHA256:
- 3d602f4ba884361f2ed14170468d13e2a3cb8f67bbf0a3f0e6a39eb7381ab9c1
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