Fake News Detection Model

Model Overview

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.

Model License

This model is licensed under the Apache 2.0 License.

Datasets Used

The model was trained on a variety of datasets, including:

Fake News Dataset: Contains labeled news articles with "fake" or "real" classifications.
News Articles Dataset: A collection of news articles used for training and validation.

Languages

The model primarily works with English-language news articles, but it could be extended to other languages with appropriate data.

Metrics

The model's performance was evaluated on the following metrics:

Accuracy: 99.58%
Precision: 99.27%
Recall: 99.88%
ROC-AUC: 99.99%
F1-Score: 99.57%

Model Details

Base Model: bert-base-uncased
Fine-Tuning: The model was fine-tuned on a news dataset with labeled examples of real and fake news.
Training Epochs: 3
Batch Size: 32
Optimizer: Adam with weight decay
Learning Rate: 2e-5

Usage

To use this model, you can interact with it via the Hugging Face Inference API or integrate it into your Python-based applications.

Example code for inference:

import requests

url = "https://api-inference.huggingface.co/models/your-username/fake-news-bert"
headers = {"Authorization": "Bearer YOUR_HUGGINGFACE_API_KEY"}
payload = {"inputs": "The news article content here"}

response = requests.post(url, headers=headers, json=payload)
prediction = response.json()

print(f"Prediction: {prediction}")

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Fake-News-Detection