Update README.md

README.md

@@ -43,10 +43,10 @@ Out[7]: [{'label': 'Positive', 'score': 0.9996090531349182}]
 - Based on the text the outcomes can be "Positive, Negative, Neutral" along with their confidence score.
 - 
 ### Recommendations
-- Continuous Monitoring: Regularly monitor the model's performance on new data to ensure its effectiveness and reliability over time.
-- Error Analysis: Conduct thorough error analysis to identify common patterns of misclassifications and areas for improvement.
-- Fine-Tuning: Consider fine-tuning the model further based on feedback and insights from users, especially from LDS, to enhance its domain-specific performance.
-- Model Interpretability: Explore techniques for explaining the model's predictions, such as attention mechanisms or feature importance analysis, to increase trust and understanding of its decisions.
+- **Continuous Monitoring:** Regularly monitor the model's performance on new data to ensure its effectiveness and reliability over time.
+- **Error Analysis:** Conduct thorough error analysis to identify common patterns of misclassifications and areas for improvement.
+- **Fine-Tuning:** Consider fine-tuning the model further based on feedback and insights from users, especially from LDS, to enhance its domain-specific performance.
+- **Model Interpretability:** Explore techniques for explaining the model's predictions, such as attention mechanisms or feature importance analysis, to increase trust and understanding of its decisions.
 
 
 ## Training Details
@@ -62,11 +62,11 @@ X_train, X_val, y_train, y_val = train_test_split(X,y, test_size = 0.2, stratify
 
 ### Training Procedure
 
-**Dataset Split:** Data divided into 80% training and 20% validation sets.
-**Preprocessing:** Input data tokenized into 'input_ids' and 'attention_mask' tensors.
-**Training Hyperparameters:** Set for training, evaluation, and optimization, including batch size, epochs, and logging strategies.
-**Training Execution:** Model trained with specified hyperparameters, monitored with metrics, and logged for evaluation.
-**Evaluation Metrics:** Model evaluated on loss, accuracy, F1 score, precision, and recall for both training and validation sets.
+- **Dataset Split:** Data divided into 80% training and 20% validation sets.
+- **Preprocessing:** Input data tokenized into 'input_ids' and 'attention_mask' tensors.
+- **Training Hyperparameters:** Set for training, evaluation, and optimization, including batch size, epochs, and logging strategies.
+- **Training Execution:** Model trained with specified hyperparameters, monitored with metrics, and logged for evaluation.
+- **Evaluation Metrics:** Model evaluated on loss, accuracy, F1 score, precision, and recall for both training and validation sets.
 
 #### Preprocessing [optional]
 ```