README copy.md

---
title: News Source Classifier
emoji: 📰
colorFrom: blue
colorTo: red
sdk: fastapi
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
widget:
- example_title: "Crime News Headline"
  text: "Wife of murdered Minnesota pastor hired 3 men to kill husband after affair: police"
- example_title: "Science News Headline"
  text: "Scientists discover breakthrough in renewable energy research"
- example_title: "Political News Headline"
  text: "Presidential candidates face off in heated debate over climate policies"
model-index:
- name: News Source Classifier
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Custom Dataset
      type: Custom
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.82
---

# 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

response = requests.post(
    "https://huggingface.co/Jiahuita/NewsSourceClassification",
    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)
```

Example response:
```json
{
    "label": "foxnews",
    "score": 0.875
}
```

## 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.