---
license: apache-2.0
datasets:
- yongchao/gptgen_text_detection
metrics:
- accuracy
pipeline_tag: text-classification
---

# BERT-based Classification Model for AI Generated Text Detection

## Model Overview
This BERT-based model is fine-tuned for the task of Ai generated text detection, especially in a TEXT-SQL senario. 
Please be noted that this model is still in testing phase, its validity has not been fully tested. 

## Model Details
- **Architecture**: BERT (bert-base-uncased)
- **Training Data**: The model was trained on a dataset of 2000 labeled human and ai created questions.
- **Training Procedure**:
  - **Epochs**: 10
  - **Batch Size**: 16
  - **Learning Rate**: 2e-5
  - **Warmup Steps**: 500
  - **Weight Decay**: 0.01
- **Model Performance**:
  - **Accuracy**: 85.7%
  - **Precision**: 82.4%
  - **Recall**: 91%
  - **F1 Score**: 86.5%

## Limitations and Ethical Considerations

### Limitations
The model may not perform well on text that are significantly different from the training data. 

### Ethical Considerations
Be aware of potential biases in the training data that could affect the model's predictions. Ensure that the model is used in a fair and unbiased manner.

## References
- **BERT Paper**: Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
- **Dataset**: [Link to the dataset](https://huggingface.co/datasets/yongchao/gptgen_text_detection)