metadata

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:

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:

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.