|
|
--- |
|
|
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. |
