Update README.md

README.md

@@ -6,4 +6,56 @@ pipeline_tag: text-classification
 tags:
 - bbc-news-classification
 - bert
----
+---
+
+# BBC News Classification with BERT
+
+## Overview
+
+This model classifies BBC news articles into five categories: Business, Entertainment, Politics, Sport, and Tech. It is fine-tuned using a pre-trained BERT model to achieve high accuracy in text classification.
+
+## Dataset
+
+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.
+
+## 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
+
+## How to Use
+
+You can use this model to classify BBC news articles into one of the five categories. Below is an example using the Hugging Face Transformers library:
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="NotThareesh/BBC-News-Classifier-BERT")
+
+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}]
+```
+
+## Model Performance
+
+- Accuracy: Achieved over 99% accuracy on the validation dataset.
+
+- Evaluation: The model performs consistently on Precision, Recall, and F1-score.
+
+## License
+
+This model is available under the MIT License.