Update README.md

README.md

@@ -16,25 +16,17 @@ This model classifies BBC news articles into five categories: Business, Entertai
 
 ## Dataset
 
-The model is trained on the BBC News dataset, which consists of categorized news articles. The dataset contains training and test splits.
+The model is trained on the **BBC News dataset**, which consists of categorized news articles. The dataset contains training and test splits.
 
-- Train Dataset: Contains labeled news articles.
-
-- Test Dataset: Contains unlabeled news articles for evaluation.
+- **Train Dataset**: Contains labeled news articles.
+- **Test Dataset**: Contains unlabeled news articles for evaluation.
 
 ## Model Details
 
-- Base Model: bert-base-uncased
-
-- Task: Text Classification
-
-- Training Framework: TensorFlow with Hugging Face Transformers
-
-- Fine-Tuning Epochs: 5
-
-- Batch Size: 16
-
-- Optimizer: Adam with Learning Rate Scheduling
+- **Base Model**: bert-base-uncased
+- **Training Framework**: PyTorch with Hugging Face Transformers
+- **Batch Size**: 16
+- **Optimizer**: Adam with Learning Rate Scheduling
 
 ## How to Use
 
@@ -45,16 +37,18 @@ from transformers import pipeline
 
 classifier = pipeline("text-classification", model="NotThareesh/BBC-News-Classifier-BERT")
 
+labels = {"LABEL_0": "Business", "LABEL_1": "Entertainment", "LABEL_2": "Politics", "LABEL_3": "Sport", "LABEL_4": "Tech"}
+
 text = "The stock market saw a significant rise today after tech companies reported high earnings."
 result = classifier(text)
-print(result)  # Output: [{'label': 'Business', 'score': 0.98}]
+
+print(f"Predicted Label: {labels[result[0]['label']]}, Accuracy Score: {result[0]['score']}")  # Predicted Label: Business, Accuracy: 0.996
 ```
 
 ## Model Performance
 
-- Accuracy: Achieved over 99% accuracy on the validation dataset.
-
-- Evaluation: The model performs consistently on Precision, Recall, and F1-score.
+- **Accuracy**: Achieved over 99% accuracy on the validation dataset.
+- **Evaluation**: The model performs consistently on Precision, Recall, and F1-score.
 
 ## License