Update README.md

README.md

@@ -8,24 +8,42 @@ Training Data:  The model was trained and validated using a dataset of Twitter a
 
 ### Fine-Tuning Process:
 
-Data Preprocessing: Combined user descriptions, names, and screen names into a single text field for input.
-Data Splitting: Split the dataset into 80% for training and 20% for validation.
-Tokenization: Utilized the AutoTokenizer from Hugging Face to prepare text inputs for the BERT model.
-Hyperparameter Optimization: Employed Optuna to find the best combination of learning rate, batch size, and training epochs, resulting in optimal performance and minimizing validation loss.
+Data Preprocessing: 
+
+Combined user descriptions, names, and screen names into a single text field for input.
+
+Data Splitting: 
+
+Split the dataset into 80% for training and 20% for validation.
+
+Tokenization: 
+
+Utilized the AutoTokenizer from Hugging Face to prepare text inputs for the BERT model.
+
+Hyperparameter Optimization: 
+
+Employed Optuna to find the best combination of learning rate, batch size, and training epochs, resulting in optimal performance and minimizing validation loss.
 
 Optimal Hyperparameters:
+
 Learning Rate: 1.23e-5
+
 Batch Size: 32
+
 Epochs: 2
 
 ## Evaluation Results
 The fine-tuned model demonstrates excellent performance on the validation set, achieving the following metrics:
 Precision: 0.945
+
 Recall: 0.95
+
 F1-Score (Macro): 0.948
+
 Accuracy: 0.95
 
 
 Confusion Matrix:
 [[369  22]
+
  [ 19 375]]