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
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## Model Description
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- **Model Type:** SetFit
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- **Classification head:**
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## Sources
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## Model Description
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- **Model Type:** SetFit
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- **Classification head:** [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)
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## Sources
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