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---
title: News Classification
emoji: 📰
colorFrom: green
colorTo: indigo
sdk: docker
sdk_version: 0.95.2
app_file: app.py
pinned: false
language: en
license: mit
tags:
- text-classification
- news-classification
- LSTM
- tensorflow
pipeline_tag: text-classification
---
# News Source Classifier
This model classifies news headlines as either Fox News or NBC News using a deep learning LSTM (Long Short-Term Memory) neural network architecture.
## Model Description
### Architecture
- Input Layer: Embedding layer (vocab_size=74,934, embedding_dim=128)
- LSTM Layer 1: 128 units with return sequences
- Dropout Layer 1: For regularization
- LSTM Layer 2: 64 units
- Dropout Layer 2: For regularization
- Output Layer: Dense layer with 2 units (binary classification)
### Technical Details
- Total Parameters: 9,772,676 (37.28 MB)
- Training Parameters: 9,772,674 (37.28 MB)
- Input Shape: (41, ) - sequences of length 41
- Performance: Achieves binary classification of news sources
## Usage
You can use this model through our REST API:
```python
import requests
def predict_news_source(text):
response = requests.post(
"https://jiahuita-news-classification.hf.space/predict",
json={"text": text},
headers={"Content-Type": "application/json"}
)
return response.json()
# Example usage
headline = "Scientists discover breakthrough in renewable energy research"
result = predict_news_source(headline)
print(result)
```
Example response:
```json
{
"label": "nbc",
"score": 0.789
}
```
## Limitations and Bias
This model has been trained on news headlines from specific sources and time periods, which may introduce certain biases:
- Training data is limited to two news sources
- Headlines represent a specific time period
- Model may be sensitive to writing style rather than just content
## Training Details
The model was trained using:
- TensorFlow 2.10.0
- Binary cross-entropy loss
- Embedding layer for text representation
- Dual LSTM layers with dropout for robust feature extraction
- Dense layer with softmax activation for final classification
## License
This project is licensed under the MIT License. |