AggressiveBag/VKR_Model

VKR Model

Binary classifier for detecting whether Python code is human-written or AI-generated.

The model was fine-tuned from microsoft/codebert-base on the AggressiveBag/VKR_Dataset dataset.

Labels

• 0: human-written Python code
• 1: AI-generated Python code

Training Setup

• Base model: microsoft/codebert-base
• Maximum sequence length: 512
• Epochs: 3
• Batch size: 8
• Learning rate: 2e-5
• Weight decay: 0.01
• Warmup ratio: 0.06
• Seed: 42
• Encoder frozen: yes

Validation Metrics

Metric	Value
Loss	0.4192
Accuracy	0.7836
Precision, AI class	0.7142
Recall, AI class	0.9456
F1, AI class	0.8138

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "AggressiveBag/VKR_Model"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

code = "print('hello world')"
inputs = tokenizer(code, return_tensors="pt", truncation=True, max_length=512)

with torch.no_grad():
    logits = model(**inputs).logits
    probs = torch.softmax(logits, dim=-1)[0]

print({"human": float(probs[0]), "ai": float(probs[1])})

Intended Use

This model is intended for research and educational experiments related to AI-generated Python code detection. It should not be used as the sole evidence for high-stakes decisions, because AI-code detection can produce false positives and false negatives.

Dataset

The training data is based on human solutions from APPS and locally generated AI solutions. See AggressiveBag/VKR_Dataset for dataset details.