Update README.md

README.md

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 metrics:
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 pipeline_tag: text-classification
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+---
+
+# 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**: 84.5%
+  - **Precision**: 1
+  - **Recall**: 0.845
+  - **F1 Score**: 0.916
+
+## 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)