Refactor code structure for improved readability and maintainability

README.md

@@ -36,6 +36,25 @@ The model was trained on a custom dataset compiled from human-written financial
 - **Data Generation:** Actual human texts from corporate annual reports were compiled. State-of-the-art Large Language Models (LLMs), including OpenAI's ChatGPT, Google's Gemini, and Anthropic's Claude, were then prompted to rewrite these sections or generate similar artificial financial texts.
 - **Training Method:** The base `finbert-pretrain` model—already pre-trained on a large corpus of financial text—was fine-tuned on this mixed dataset to classify whether a given segment of text is human-written or generated by an AI.
 
+## Performance
+
+Total cases (AI & Human): 6000
+Total cases (AI): 3000
+
+Estimation cases: 4200
+Test cases: 1800
+
+| Metric    | Value   |
+|-----------|---------|
+| accuracy  | 89.16%  |
+| f1        | 88.57%  |
+| precision | 92.64%  |
+| recall    | 84.84%  |
+
+### Confusion Matrix
+
+![Confusion Matrix](figs/confusion_matrix.png)
+
 ## Uses
 
 This model is intended for researchers, financial analysts, and auditors who want to verify the authenticity of corporate disclosures and determine if a financial text (like an annual report or an earnings call transcript) was written by an AI or a human.