---
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 through the FastAPI endpoint:

```python
import requests

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