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--- |
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license: apache-2.0 |
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datasets: |
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- Pulk17/Fake-News-Detection-dataset |
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language: |
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- en |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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base_model: |
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- google-bert/bert-base-uncased |
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pipeline_tag: text-classification |
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library_name: transformers |
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--- |
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# Fake News Detection Model |
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## Model Overview |
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This model is designed to classify news articles as real or fake based on their textual content. It uses a BERT-based transformer model (`bert-base-uncased`) fine-tuned on a custom dataset of news articles. The model predicts whether a given article is fake or real with high accuracy. |
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## Model License |
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This model is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). |
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## Datasets Used |
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The model was trained on a variety of datasets, including: |
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- **Fake News Dataset**: Contains labeled news articles with "fake" or "real" classifications. |
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- **News Articles Dataset**: A collection of news articles used for training and validation. |
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## Languages |
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The model primarily works with English-language news articles, but it could be extended to other languages with appropriate data. |
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## Metrics |
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The model's performance was evaluated on the following metrics: |
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- **Accuracy**: 99.58% |
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- **Precision**: 99.27% |
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- **Recall**: 99.88% |
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- **ROC-AUC**: 99.99% |
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- **F1-Score**: 99.57% |
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## Model Details |
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- **Base Model**: `bert-base-uncased` |
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- **Fine-Tuning**: The model was fine-tuned on a news dataset with labeled examples of real and fake news. |
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- **Training Epochs**: 3 |
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- **Batch Size**: 32 |
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- **Optimizer**: Adam with weight decay |
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- **Learning Rate**: 2e-5 |
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## Usage |
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To use this model, you can interact with it via the Hugging Face Inference API or integrate it into your Python-based applications. |
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Example code for inference: |
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```python |
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import requests |
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url = "https://api-inference.huggingface.co/models/your-username/fake-news-bert" |
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headers = {"Authorization": "Bearer YOUR_HUGGINGFACE_API_KEY"} |
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payload = {"inputs": "The news article content here"} |
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response = requests.post(url, headers=headers, json=payload) |
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prediction = response.json() |
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print(f"Prediction: {prediction}") |
