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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57adb905387ca40afe0bec0846b5d96293f8e38b3506af799addae6e965bba52
- Size of remote file:
- 559 MB
- SHA256:
- 90b017115ddd426da5656a6e29eb7b8f6d845f08abdf6dfb2a9ae22c549a6dbe
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