README.md

metadata

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

PLS VISIT https://huggingface.co/fauxNeuz

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.

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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.

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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

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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

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🛠 Libraries Used

• transformers
• datasets
• torch
• scikit-learn
• pandas
• nltk (optional preprocessing)

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📦 Installation & Running

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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📄 License

This project is licensed under the MIT License.