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