---
license: apache-2.0
datasets:
- Pulk17/Fake-News-Detection-dataset
language:
- en
metrics:
- accuracy
- precision
- recall
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
library_name: transformers
---

# 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](https://www.apache.org/licenses/LICENSE-2.0).

## 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:
```python
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}")