ShahzaibAli-1
/

News_Classifier-bert-base-uncased

@@ -1,199 +1,172 @@
 ---
-library_name: transformers
-tags: []
 ---
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 ---
+language: en
+license: apache-2.0
+datasets:
+- ag_news
+tags:
+- text-classification
+- bert
+- ag-news
 ---
+# BERT-base-uncased fine-tuned on AG News
+This model is a fine-tuned version of `bert-base-uncased` on the AG News dataset, achieving **94.36% accuracy** on the test set.
 ## Model Details
+- **Model Type:** Text Classification (BERT)
+- **Base Model:** [bert-base-uncased](https://huggingface.co/bert-base-uncased)
+- **Dataset:** [AG News](https://huggingface.co/datasets/ag_news)
+- **Fine-tuning Approach:** Sequence Classification
+## Training Results
+| Epoch | Training Loss | Validation Loss | Accuracy | F1 (Weighted) |
+|-------|---------------|-----------------|----------|---------------|
+| 1     | 0.231600      | 0.212338        | 0.9359   | 0.9359        |
+| 2     | 0.176300      | 0.213332        | 0.9439   | 0.9439        |
+| 3     | 0.119100      | 0.230517        | 0.9450   | 0.9450        |
+| 4     | 0.074500      | 0.286154        | 0.9447   | 0.9448        |
+| 5     | 0.031700      | 0.344374        | 0.9436   | 0.9435        |
+## Confusion Matrix
+![Confusion Matrix](image.png)
+### Confusion Matrix Values (True Label → Predicted Label)
+|             | World | Sports | Business | Sci/Tech |
+|-------------|-------|--------|----------|----------|
+| **World**   | 1812  | 13     | 43       | 32       |
+| **Sports**  | 7     | 1880   | 7        | 6        |
+| **Business**| 39    | 9      | 1728     | 124      |
+| **Sci/Tech**| 34    | 10     | 105      | 1751     |
+## How to Use
+```python
+from transformers import pipeline
+classifier = pipeline("text-classification", model="ShahzaibAli-1/News_Classifier-bert-base-uncased")
+result = classifier("Apple reported record profits last quarter.")
+print(result)
+```
+## Performance
+### Training Hyperparameters
+- Learning Rate: 5e-5
+- Batch Size: 8
+- Epochs: 5
+- Warmup Ratio: 0.1
+- Max Sequence Length: 128
+Final Test Accuracy: 94.36%
+Final Test F1-Score (Weighted): 94.35%
+### To watch a proper demo using Gradio
+```python
+from transformers import pipeline
+classifier = pipeline("text-classification", model="ShahzaibAli-1/News_Classifier-bert-base-uncased")
+result = classifier("Apple reported record profits last quarter.")
+print(result)
+import gradio as gr
+from transformers import pipeline
+# Load model
+classifier = pipeline("text-classification", model="ShahzaibAli-1/News_Classifier-bert-base-uncased")
+# Define label mapping (must match your training labels)
+label_map = {
+    0: "World",
+    1: "Sports",
+    2: "Business",
+    3: "Sci/Tech"
+}
+def predict(text):
+    result = classifier(text)[0]
+    # Extract numerical label (e.g., "LABEL_1" -> 1)
+    label_num = int(result['label'].split("_")[-1])
+    # Get corresponding text label
+    label_text = label_map[label_num]
+    return f"{label_text} (confidence: {result['score']:.2%})"
+# Create interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=2, placeholder="Enter news text here..."),
+    outputs="text",
+    title="AG News Classifier",
+    description="Classify news articles into World, Sports, Business, or Sci/Tech categories"
+)
+iface.launch()
+```
+## Example Outputs
+Here are some example outputs for various test cases:
+- **Sports News**:
+  Prompt: `"Newzealand Won the Test Championship today"`
+  Output: `Sports (confidence: 99.99%)`
+- **Business News**:
+  Prompt: `"The stock market saw a significant increase following the tech boom"`
+  Output: `Business (confidence: 98.50%)`
+- **World News**:
+  Prompt: `"The political unrest in Eastern Europe has escalated this week"`
+  Output: `World (confidence: 97.70%)`
+- **Sci/Tech News**:
+  Prompt: `"Scientists have developed a new battery that can last twice as long as current models"`
+  Output: `Sci/Tech (confidence: 96.30%)`
+## Evaluation Metrics
+The following evaluation metrics were used to assess the model's performance:
+- **Accuracy**: The percentage of correct predictions over the total number of predictions.
+- **Precision**: The proportion of positive predictions that were actually correct.
+- **Recall**: The proportion of actual positives that were correctly identified.
+- **F1-Score**: The harmonic mean of precision and recall.
+The model demonstrated strong performance across all metrics, particularly with an accuracy of 94.36%.
+---
+## Citation
+If you use this model in your research or projects, please cite it as follows:
+```
+@article{shahzaib2025news,
+  title={Fine-Tuning BERT for AG News Classification},
+  author={Shahzaib Ali},
+  journal={Hugging Face Model Hub},
+  year={2025},
+  url={https://huggingface.co/ShahzaibAli-1/News_Classifier-bert-base-uncased}
+}
+```
+## License
+The model is released under the [Apache-2.0 License](https://opensource.org/licenses/Apache-2.0). Feel free to use it in your applications and research.
+## Contact
+For any questions or suggestions, feel free to open an issue or contact the model creator at:
+- **Hugging Face**: [ShahzaibAli-1](https://huggingface.co/ShahzaibAli-1)