| --- |
| library_name: transformers |
| tags: |
| - text-classification |
| - distilbert |
| - news-classification |
| - sri-lanka |
| base_model: |
| - distilbert/distilbert-base-uncased |
| --- |
| |
|
|
|
|
| ## Model Details |
|
|
| **Model Name:** `Ginidu2003/Distilbert-Base-News-classifier` |
| **Model Type:** Text Classification |
| **Base Model:** `distilbert/distilbert-base-uncased` |
| **Language(s):** English |
| **Finetuned from model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) |
|
|
| ### Model Description |
| This is a fine-tuned DistilBERT model designed to classify English news articles into **5 categories**: |
|
|
| - Business |
| - Opinion |
| - Political gossip |
| - Sports |
| - World news |
|
|
|
|
|
|
| ## Uses |
|
|
| ### Direct Use |
| - Classify news articles into one of the five predefined categories. |
| - Suitable for English news (Like Daily Mirror style). |
|
|
| ### Downstream Use |
| - Can be integrated into web applications (Streamlit/Gradio) for automated news categorization. |
| - Can be used for real-time news filtering and topic-based news recommendation systems. |
|
|
| ### Out-of-Scope Use |
| - Not intended for other languages. |
| - Not trained for sentiment analysis, fake news detection, or hate speech detection. |
| - Not suitable for very short texts. |
|
|
| ## Bias, Risks, and Limitations |
| - The model is trained only on **Daily Mirror** news data, so it may perform poorly on other news sources or different writing styles. |
| - Potential bias towards Sri Lankan context and English used in Sri Lankan media. |
| - Performance may degrade on very long or very short articles. |
|
|
| ## How to Get Started with the Model |
|
|
| ```python |
| from transformers import pipeline |
| |
| classifier = pipeline( |
| "text-classification", |
| model="Ginidu2003/Distilbert-Base-News-classifier" |
| ) |
| |
| result = classifier("Your news article text here...") |
| print(result) |
| ``` |
| ## Training Details |
|
|
| ### Training Data |
| - **Dataset**: Daily Mirror Sri Lankan English news (2024–2025) |
| - **Total samples**: ~1,018 articles (after preprocessing and deduplication) |
| - **Classes**: 5 balanced categories (Business, Opinion, Political gossip, Sports, World news) |
| - **Preprocessing**: Lowercasing, punctuation removal, lemmatization |
|
|
| ### Training Procedure |
| - **Framework**: Hugging Face Transformers + Trainer API |
| - **Base Model**: `distilbert/distilbert-base-uncased` |
| - **Epochs**: 4 |
| - **Batch Size**: 8 |
| - **Learning Rate**: 2e-5 |
| - **Validation Accuracy**: **90.19%** |
|
|
| ## Evaluation |
|
|
| **Validation Set Results (20% hold-out):** |
| - **Accuracy**: **91.18%** |
| - Model shows strong and consistent performance across all 5 classes. |
|
|
| ## Environmental Impact |
| - Training was done on a single GPU (T4 GPU on Colab) |
| - Estimated carbon emissions: Very low (small model + small dataset) |
