Instructions to use AISE-TUDelft/extended-java-summary-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AISE-TUDelft/extended-java-summary-classifier with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AISE-TUDelft/extended-java-summary-classifier") 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] - setfit
How to use AISE-TUDelft/extended-java-summary-classifier with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("AISE-TUDelft/extended-java-summary-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf0f045818dd88a9b76c88e16900aff7aa0cb0f203482bfdb6c33687334bcd60
- Size of remote file:
- 438 MB
- SHA256:
- c619c040af933f2746d7cc6d04950e7d57c9950e053e6ea7fcaf6581a702cc30
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