---
license: mit
base_model:
- google-bert/bert-base-uncased
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
- **Training Framework**: PyTorch with Hugging Face Transformers
- **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")

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(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.

## License

This model is available under the MIT License.