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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ayshajavd/graphcodebert-vuln-classifier") model = AutoModelForSequenceClassification.from_pretrained("ayshajavd/graphcodebert-vuln-classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- af77e43795b316122d8da20168a3abbc5223b98a40d55b9adedd833495473edc
- Size of remote file:
- 5.33 kB
- SHA256:
- faf024c35995c1b5bd61eaa1ad4ddbbfca5175a19eb3f2e13c5cb7779bdbc19c
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