Text Classification
Transformers
Safetensors
English
code
roberta
security
vulnerability-detection
code-analysis
multi-label-classification
graphcodebert
owasp
cwe
static-analysis
Eval Results (legacy)
text-embeddings-inference
Instructions to use ayshajavd/graphcodebert-vuln-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ayshajavd/graphcodebert-vuln-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ayshajavd/graphcodebert-vuln-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ayshajavd/graphcodebert-vuln-classifier") model = AutoModelForSequenceClassification.from_pretrained("ayshajavd/graphcodebert-vuln-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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v2: GraphCodeBERT + ASL + two-phase + per-class thresholds + calibration
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Update model card with full test set evaluation metrics (5K samples)
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Add comprehensive model card with metrics, examples, and limitations
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Fix id2label mapping with actual CWE names
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Upload label_config.json with huggingface_hub
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GraphCodeBERT vulnerability classifier (CPU-trained, 20K balanced subset)
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initial commit
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