Text Classification
Transformers
Safetensors
code
roberta
code-classification
vulnerability-detection
automatic-vulnerability-detection
secure-coding
text-embeddings-inference
Instructions to use jacpacd/vuln-detector-codebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jacpacd/vuln-detector-codebert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jacpacd/vuln-detector-codebert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jacpacd/vuln-detector-codebert") model = AutoModelForSequenceClassification.from_pretrained("jacpacd/vuln-detector-codebert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1f7af0e6165837c02ad35e3482b1d531dffd12e42d3dceb0a868af73fa28248
- Size of remote file:
- 499 MB
- SHA256:
- 97da04623f7ff120733adc435b80a52326f5f987dfbf376769cf8280ad3076a6
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