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:
- 79bccd3b0c0bb4aacef6aad05b89deeb14c8dd2162fe0abec422a877d37f29de
- Size of remote file:
- 7.01 kB
- SHA256:
- e817609b4aabc4f7891b4b00b27e960638cb69cf8de3cb90af45d935ba128b77
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