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") - 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
- Xet hash:
- 8b7602ccffd86da0b9231365f1157683ca9cec8455213fd4d6b4403672b9913a
- Size of remote file:
- 90.9 MB
- SHA256:
- 5052f07e64a2b914783cb2722a93e76212e3eb45b8deb0967e7d01d067edbbca
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