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
| { | |
| "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" | |
| ] | |
| } |