Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use NLBSE/nlbse26_python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use NLBSE/nlbse26_python with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("NLBSE/nlbse26_python") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - sentence-transformers
How to use NLBSE/nlbse26_python with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NLBSE/nlbse26_python") 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.safetensors from NLBSE/nlbse26_python: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/NLBSE/nlbse26_python/resolve/6add4e4c33d594b25cf244843d7f23efce88c8cd/model.safetensors
- Command line
-
hf download hf://NLBSE/nlbse26_python@6add4e4c33d594b25cf244843d7f23efce88c8cd/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/NLBSE/nlbse26_python/resolve/6add4e4c33d594b25cf244843d7f23efce88c8cd/model.safetensors
90.9 MB
- Xet hash:
- 8b7602ccffd86da0b9231365f1157683ca9cec8455213fd4d6b4403672b9913a
- Size of remote file:
- 90.9 MB
- SHA256:
- 5052f07e64a2b914783cb2722a93e76212e3eb45b8deb0967e7d01d067edbbca
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