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---
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title: News Source Classifier
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emoji: 📰
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colorFrom: blue
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colorTo: red
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sdk: fastapi
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sdk_version: 0.95.2
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app_file: app.py
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pinned: false
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language: en
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license: mit
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tags:
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- text-classification
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- news-classification
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- LSTM
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- tensorflow
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pipeline_tag: text-classification
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widget:
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- example_title: "Crime News Headline"
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text: "Wife of murdered Minnesota pastor hired 3 men to kill husband after affair: police"
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- example_title: "Science News Headline"
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text: "Scientists discover breakthrough in renewable energy research"
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- example_title: "Political News Headline"
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text: "Presidential candidates face off in heated debate over climate policies"
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model-index:
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- name: News Source Classifier
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results:
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- task:
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type: text-classification
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name: Text Classification
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dataset:
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name: Custom Dataset
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type: Custom
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.82
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---
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# News Source Classifier
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This model classifies news headlines as either Fox News or NBC News using an LSTM neural network.
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## Model Description
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- **Model Architecture**: LSTM Neural Network
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- **Input**: News headlines (text)
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- **Output**: Binary classification (Fox News vs NBC)
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- **Training Data**: Large collection of headlines from both news sources
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- **Performance**: Achieves approximately 82% accuracy on the test set
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## Usage
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You can use this model directly with a FastAPI endpoint:
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```python
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import requests
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response = requests.post(
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"https://huggingface.co/Jiahuita/NewsSourceClassification",
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json={"text": "Your news headline here"}
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)
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print(response.json())
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```
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Or use it locally:
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```python
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from transformers import pipeline
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classifier = pipeline("text-classification", model="Jiahuita/NewsSourceClassification")
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result = classifier("Your news headline here")
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print(result)
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```
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Example response:
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```json
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{
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"label": "foxnews",
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"score": 0.875
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}
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```
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## Limitations and Bias
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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.
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## Training
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The model was trained using:
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- TensorFlow 2.13.0
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- LSTM architecture
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- Binary cross-entropy loss
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- Adam optimizer
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## License
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This project is licensed under the MIT License. |
