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
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- **Repository:** [GitHub](https://github.com/fabiancpl/sbert-comment-classification/)
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- **Paper:** [Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification](https://ieeexplore.ieee.org/document/11029440)
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- **Dataset:** [HF Dataset](https://huggingface.co/NLBSE/
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## How to use it
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- **Repository:** [GitHub](https://github.com/fabiancpl/sbert-comment-classification/)
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- **Paper:** [Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification](https://ieeexplore.ieee.org/document/11029440)
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- **Dataset:** [HF Dataset](https://huggingface.co/datasets/NLBSE/nlbse25-code-comment-classification)
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## How to use it
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