| ---
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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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| ---
|
|
|
| # 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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|
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| ## Model Description
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|
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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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|
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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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|
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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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|
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| ```python
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| from transformers import pipeline
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|
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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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|
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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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|
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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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|
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| ## Training
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|
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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
|
| This project is licensed under the MIT License. |
