Instructions to use uclanlp/plbart-c-cpp-defect-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uclanlp/plbart-c-cpp-defect-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="uclanlp/plbart-c-cpp-defect-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("uclanlp/plbart-c-cpp-defect-detection") model = AutoModelForSequenceClassification.from_pretrained("uclanlp/plbart-c-cpp-defect-detection") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 36488d3847967ae30e160b67180f8f214afde58b1d34ca9cfe813b0a6e522cc1
- Size of remote file:
- 559 MB
- SHA256:
- 11fbcfafd128243b46161c2ccccc7aefef3d21aa955ab7eae88da768d8bd48e0
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