Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use NLBSE/nlbse25_java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use NLBSE/nlbse25_java with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("NLBSE/nlbse25_java") - sentence-transformers
How to use NLBSE/nlbse25_java with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NLBSE/nlbse25_java") 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:
- dcdc0dba5969c6f37d9c132d3cefef20c93a7d2e5c533f22135dc21549c97d19
- Size of remote file:
- 69.6 MB
- SHA256:
- 088ad4d3b2fc8ba2061ae6aa9ed800c4678e43163eda5824f9ea02996040dde4
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