README.md

---
language: en
license: mit
tags:
- fake-news-detection
- deberta-v3-large
- text-classification
- binary-classification
- news-classification
datasets:
- mrisdal/fake-news
- jainpooja/fake-news-detection
- clmentbisaillon/fake-and-real-news-dataset
metrics:
- accuracy
- f1
- precision
- recall
widget:
- text: "Scientists announce breakthrough discovery of alien life on Mars!"
  example_title: "Suspicious Claim"
- text: "The Federal Reserve announced a 0.25% interest rate increase following their monthly meeting."
  example_title: "Financial News"
model-index:
- name: Arko007/fact-check1-v1
  results:
  - task:
      type: text-classification
      name: Fake News Detection
    metrics:
    - type: accuracy
      value: 99.98
      name: Validation Accuracy
    - type: f1
      value: 99.98
      name: Validation F1-Score
---
# 🏆 Elite Fake News Detection Model

## Model Description
This is a **state-of-the-art** fake news detection model based on **DeBERTa-v3-large**, achieving **99.98% accuracy** on validation data. The model was fine-tuned on a carefully curated and deduplicated dataset combining multiple high-quality fake news datasets, totaling **51,319 samples** after preprocessing.

## 🚀 Performance Highlights
- **Validation Accuracy**: 99.98%
- **Test Accuracy**: 99.94%
- **F1-Score**: 99.98%
- **Precision**: 99.97%
- **Recall**: 100.00%

## Model Architecture
- **Base Model**: microsoft/deberta-v3-large
- **Task**: Binary Text Classification (Real vs Fake News)
- **Parameters**: ~400M parameters
- **Training Hardware**: NVIDIA A100-SXM4-80GB

## Training Details
- **Training Steps**: 640
- **Batch Size**: 64
- **Learning Rate**: 3e-05
- **Max Length**: 512 tokens
- **Training Time**: 0.43 hours
- **Gradient Checkpointing**: Non-reentrant (memory optimized)

## Dataset Information
**Total Samples**: 51,319
- **Training**: 41,055 samples
- **Validation**: 5,132 samples
- **Test**: 5,132 samples
- **Fake News**: 30,123 samples
- **Real News**: 21,196 samples
**Source Datasets**:
- `mrisdal/fake-news`
- `jainpooja/fake-news-detection`
- `clmentbisaillon/fake-and-real-news-dataset`

## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "Arko007/fact-check1-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example prediction function
def predict_fake_news(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
        probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
        prediction = torch.argmax(probabilities, dim=-1).item()

    labels = {0: "REAL", 1: "FAKE"}
    confidence = probabilities[0][prediction].item()

    return {
        "prediction": labels[prediction],
        "confidence": confidence,
        "probabilities": {
            "REAL": probabilities[0][0].item(),
            "FAKE": probabilities[0][1].item()
        }
    }

# Test the model
text = "Breaking: Scientists discover new planet in our solar system!"
result = predict_fake_news(text)
print(f"Prediction: {result['prediction']} ({result['confidence']:.2%} confidence)")
```
## Model Performance

This model achieves **research-grade performance** on fake news detection, with near-perfect accuracy across all metrics. The high precision and recall indicate excellent balance between catching fake news while avoiding false positives on real news.

## Limitations and Bias

- Trained primarily on English news articles
- Performance may vary on news domains not represented in training data
- May reflect biases present in the source datasets
- Designed for binary classification (fake vs real) only

## Citation
```bibtex
@misc{fake-news-deberta-2025,
author = {Arko007},
title = {Elite Fake News Detection with DeBERTa-v3-Large},
year = {2025},
publisher = {Hugging Face},
url = {[https://huggingface.co/](https://huggingface.co/)Arko007/fact-check1-v1}
}
```
## License
MIT License - Feel free to use this model for research and applications.
---
**Built with ❤️ using A100 80GB + DeBERTa-v3-Large**