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:
- 39d035835465ab2beda700958403f46dbde4b801ebcc42c68690da7e4b2ee02a
- Size of remote file:
- 18 kB
- SHA256:
- fad1256d65ecb9b2fe102f9e908ab41e9a02f86e17cc54b70701d1f1ec1d4f2e
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