Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use ThomBors/NLBSE2026-java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use ThomBors/NLBSE2026-java with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("ThomBors/NLBSE2026-java") - sentence-transformers
How to use ThomBors/NLBSE2026-java with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ThomBors/NLBSE2026-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:
- d4d261109c6979d0250eb8c914eb64122c9ebefa1a537a672ccdd8d611d99ac3
- Size of remote file:
- 90.9 MB
- SHA256:
- 98ff1fe03f1dd35bf254095593c7094a6b546e08e200d54eeb581817141d7f1e
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