Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use NLBSE/nlbse26_java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use NLBSE/nlbse26_java with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("NLBSE/nlbse26_java") - sentence-transformers
How to use NLBSE/nlbse26_java with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NLBSE/nlbse26_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:
- 5b8a56ab917914b3ad14bc85fd8396138e0f66a520ae60b1ce36254666594a16
- Size of remote file:
- 90.9 MB
- SHA256:
- 616f6e62759ee1b786d49f886680c582b3c47a60befe7786152a68f9befbcf90
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