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") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - 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
Download model_head.pkl from fabiancpl/nlbse25_java: direct link, hf CLI and curl.
- Browser
- Download file 1.03 MB
-
https://huggingface.co/fabiancpl/nlbse25_java/resolve/main/model_head.pkl
- Command line
-
hf download hf://fabiancpl/nlbse25_java/model_head.pkl
-
curl -L -o model_head.pkl https://huggingface.co/fabiancpl/nlbse25_java/resolve/main/model_head.pkl
1.03 MB
- Xet hash:
- dd8e1c13cff4dac151c00b3094a88001b5536588fc73aac081dd79139f434e69
- Size of remote file:
- 1.03 MB
- SHA256:
- 4f296992c3b25cf49a4938791a0c00b0b8a0fe73173cce1a0b4c47864633cf26
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