Text Classification
Transformers
Safetensors
English
roberta
code
solidity
smart-contracts
vulnerability-detection
graphcodebert
Eval Results (legacy)
Instructions to use tanaymitra01/graphcodebert-vulnerability-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tanaymitra01/graphcodebert-vulnerability-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tanaymitra01/graphcodebert-vulnerability-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tanaymitra01/graphcodebert-vulnerability-detector") model = AutoModelForSequenceClassification.from_pretrained("tanaymitra01/graphcodebert-vulnerability-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 881 Bytes
c61043c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | {
"labels": [
"safe",
"reentrancy",
"access_control",
"tx_origin_auth",
"integer_overflow",
"unsafe_delegatecall",
"weak_randomness",
"unbounded_loop",
"redundant_storage",
"gas_optimization",
"best_practice",
"other"
],
"id2label": {
"0": "safe",
"1": "reentrancy",
"2": "access_control",
"3": "tx_origin_auth",
"4": "integer_overflow",
"5": "unsafe_delegatecall",
"6": "weak_randomness",
"7": "unbounded_loop",
"8": "redundant_storage",
"9": "gas_optimization",
"10": "best_practice",
"11": "other"
},
"label2id": [
"safe",
"reentrancy",
"access_control",
"tx_origin_auth",
"integer_overflow",
"unsafe_delegatecall",
"weak_randomness",
"unbounded_loop",
"redundant_storage",
"gas_optimization",
"best_practice",
"other"
]
} |