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title: FakeNewsDetector
emoji: 🐠
colorFrom: pink
colorTo: red
sdk: gradio
sdk_version: 5.35.0
app_file: app.py
pinned: false
license: mit
short_description: BERT1+BERT2
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
---
title: NewFakeNewsModel
emoji: ⚡
colorFrom: purple
colorTo: gray
sdk: gradio
sdk_version: 5.34.2
app_file: app.py
pinned: false
license: mit
short_description: wrk on prgress
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
# Fake News Classifier (BERT-based)
This project detects whether a news article is real or fake using a fine-tuned BERT model for binary text classification.
---
## Disclaimer
- This project is for **educational and experimental purposes only**.
- It is **not suitable for real-world fact-checking** or serious decision-making.
- The model uses a simple binary classifier and does not verify factual correctness.
---
## Project Overview
This fake news classifier was built as part of a research internship to:
- Learn how to fine-tune transformer models on classification tasks
- Practice handling class imbalance using weighted loss
- Deploy models using Hugging Face-compatible APIs
---
## How It Works
- A BERT-based model (`bert-base-uncased`) was fine-tuned on a labeled dataset of news articles.
- Input text is tokenized using `BertTokenizer`.
- A custom Trainer with class-weighted loss was used to handle class imbalance.
- Outputs are binary: **0 = FAKE**, **1 = REAL**.
### Training Details
- Model: `BertForSequenceClassification`
- Epochs: 4
- Batch size: 8
- Learning rate: 2e-5
- Optimizer: AdamW (via Hugging Face Trainer)
- Evaluation Metrics: Accuracy, F1-score, Precision, Recall
---
## 🛠 Libraries Used
- `transformers`
- `datasets`
- `torch`
- `scikit-learn`
- `pandas`
- `nltk` (optional preprocessing)
---
## 📦 Installation & Running
```bash
pip install -r requirements.txt
python app.py
```
Or run the training script in a notebook or script environment if you're using Google Colab or Jupyter.
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