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--- |
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license: apache-2.0 |
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datasets: |
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- yongchao/gptgen_text_detection |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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--- |
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# BERT-based Classification Model for AI Generated Text Detection |
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## Model Overview |
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This BERT-based model is fine-tuned for the task of Ai generated text detection, especially in a TEXT-SQL senario. |
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Please be noted that this model is still in testing phase, its validity has not been fully tested. |
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## Model Details |
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- **Architecture**: BERT (bert-base-uncased) |
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- **Training Data**: The model was trained on a dataset of 2000 labeled human and ai created questions. |
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- **Training Procedure**: |
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- **Epochs**: 10 |
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- **Batch Size**: 16 |
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- **Learning Rate**: 2e-5 |
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- **Warmup Steps**: 500 |
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- **Weight Decay**: 0.01 |
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- **Model Performance**: |
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- **Accuracy**: 85.7% |
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- **Precision**: 82.4% |
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- **Recall**: 91% |
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- **F1 Score**: 86.5% |
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## Limitations and Ethical Considerations |
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### Limitations |
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The model may not perform well on text that are significantly different from the training data. |
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### Ethical Considerations |
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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. |
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## References |
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- **BERT Paper**: Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. |
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- **Dataset**: [Link to the dataset](https://huggingface.co/datasets/yongchao/gptgen_text_detection) |
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