Text Classification
setfit
Safetensors
sentence-transformers
English
bert
generated_from_setfit_trainer
text-embeddings-inference
Instructions to use fabiancpl/nlbse25_java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use fabiancpl/nlbse25_java with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("fabiancpl/nlbse25_java") - sentence-transformers
How to use fabiancpl/nlbse25_java with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("fabiancpl/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:
- b181fa2a8885b0120f0144bdffa54b7930ec8251160d6ddb3ae22582d1a609e2
- Size of remote file:
- 90.9 MB
- SHA256:
- d63b4676faf1f6bf9deb515a9e6d657068b84036384ca083a7b02e9c00778534
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.