README.md

metadata

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:

import requests

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

Or use it locally:

from transformers import pipeline

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

Example response:

{
    "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.