---
language: en
license: mit
tags:
- text-classification
- news-classification
pipeline_tag: text-classification
inference: true
widget:
- example_title: "Write a news headline to classify"
  text: "Wife of murdered Minnesota pastor hired 3 men to kill husband after affair: police"
- example_title: "Another example"
  text: "Scientists discover breakthrough in renewable energy research"
---

# News Source Classifier

This model classifies news headlines as either Fox News or NBC News using an LSTM neural network.

## Model Description

- **Model Architecture**: LSTM Neural Network
- **Input**: News headlines (text)
- **Output**: Binary classification (Fox News vs NBC)
- **Training Data**: Large collection of headlines from both news sources
- **Performance**: Achieves approximately 82% accuracy on the test set

## Usage

You can use this model directly with a FastAPI endpoint:

```python
import requests

# Make a prediction
response = requests.post(
    "https://huggingface.co/Jiahuita/NewsSourceClassification/predict",
    json={"text": "Your news headline here"}
)
print(response.json())
```

Or use it locally:

```python
from transformers import pipeline

classifier = pipeline("text-classification", model="Jiahuita/NewsSourceClassification")
result = classifier("Your news headline here")
print(result)
```

## Limitations and Bias

This model has been trained on news headlines from specific sources and time periods, which may introduce certain biases. Users should be aware of these limitations when using the model.

## Training

The model was trained using:
- TensorFlow 2.13.0
- LSTM architecture
- Binary cross-entropy loss
- Adam optimizer

## License
This project is licensed under the MIT License.