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
File size: 359 Bytes
868ca9d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": "<s>",
"cls_token": "<s>",
"eos_token": "</s>",
"errors": "replace",
"is_local": false,
"mask_token": "<mask>",
"model_max_length": 512,
"pad_token": "<pad>",
"sep_token": "</s>",
"tokenizer_class": "RobertaTokenizer",
"trim_offsets": true,
"unk_token": "<unk>"
}
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