Update README.md

README.md

@@ -9,22 +9,110 @@ app_file: app.py
 pinned: false
 license: mit
 ---
+# TruthCheck: Fake News Detection with Fine-Tuned BERT
 
-# TrueCheck - Fake News Detection System
+TruthCheck is an advanced fake news detection system leveraging a hybrid deep learning architecture. It combines a pre-trained BERT-base-uncased model with a BiLSTM and attention mechanism, fully fine-tuned on a curated dataset of real and fake news. The project includes robust preprocessing, feature extraction, model training, evaluation, and a Streamlit web app for interactive predictions.
 
-This application uses a hybrid deep learning model (BERT + BiLSTM + Attention) to detect fake news articles.
+---
+
+## 🚀 Features
+- **Hybrid Model:** BERT-base-uncased + BiLSTM + Attention
+- **Full Fine-Tuning:** All layers of BERT and additional layers are trainable and optimized on the fake news dataset
+- **Comprehensive Preprocessing:** Cleaning, tokenization, lemmatization, and more
+- **Training & Evaluation:** Scripts for training, validation, and test evaluation
+- **Interactive App:** Streamlit web app for real-time news classification
+- **Ready for Deployment:** Easily extendable for research or production
+
+---
+
+## 🧠 Model Details
+- **Base Model:** [BERT-base-uncased](https://huggingface.co/bert-base-uncased)
+- **Architecture:**
+  - BERT encoder (pre-trained, all layers fine-tuned)
+  - BiLSTM layer for sequential context
+  - Attention mechanism for interpretability
+  - Fully connected classification head
+- **Fine-Tuning Technique:**
+  - All BERT layers are unfrozen and updated during training (full fine-tuning)
+  - Additional layers (BiLSTM, attention, classifier) are trained from scratch
+
+---
+
+## 📥 Download Data and Model
+
+**Raw and Processed Datasets:**  
+[Google Drive Link](https://drive.google.com/drive/folders/1tAhWhhhDes5uCdcnMLmJdFBSGWFFl55M?usp=sharing)
+
+**Trained Model(s):**  
+[Google Drive Link](https://drive.google.com/drive/folders/1VEFa0y_vW6AzT5x0fRwmX8shoBhUGd7K?usp=sharing)
+
+### **Instructions:**
+1. Download the datasets and place them in the `data/` directory:
+    - `data/raw/` for raw files
+    - `data/processed/` for processed files
+2. Download the trained model (e.g., `final_model.pt` or `best_model.pt`) and place it in `models/saved/`.
+
+---
+
+## ⚙️ Setup
+
+1. **Clone the repository:**
+    ```bash
+    git clone https://github.com/adnaan-tariq/fake-news-detection.git
+    cd fake-news-detection
+    ```
+2. **Create and activate a virtual environment:**
+    ```bash
+    python -m venv venv
+    .\venv\Scripts\activate
+    ```
+3. **Install dependencies:**
+    ```bash
+    pip install --upgrade pip
+    pip install -r requirements.txt
+    ```
+
+---
+
+## 🏃‍♂️ Usage
+
+### **Train the Model**
+If you want to train from scratch (after placing the data as described above):
+```bash
+python -m src.train
+```
+
+### **Run the Streamlit App**
+```bash
+streamlit run app.py
+```
+- Open [http://localhost:8501](http://localhost:8501) in your browser.
+
+### **Test the Model**
+- The app and scripts will use the model in `models/saved/final_model.pt` by default.
+- For custom inference, see the example in `src/app.py` or ask for a sample script.
+
+---
+
+## 📊 Results
+- **Validation Accuracy:** ~93%
+- **Validation F1 Score:** ~0.93
+- (See training logs and visualizations for more details.)
 
-## Features
-- Real-time fake news detection
-- Confidence scoring
-- Attention visualization
-- Interactive web interface
+---
+
+## 📦 Data & Model Policy
+- **Data and model files are NOT included in this repository.**
+- Please download them from the provided Google Drive links above.
+
+
+## 🤝 Contributing
+Pull requests and suggestions are welcome! For major changes, please open an issue first to discuss what you would like to change.
 
-## Model Architecture
-The model combines:
-- BERT for contextual embeddings
-- BiLSTM for sequence modeling  
-- Attention mechanism for interpretability
+---
+
+## 📄 License
+This project is licensed under the MIT License.
+
+---
 
-## Usage
-Simply paste a news article into the text area and click "Analyze" to get predictions with confidence scores and attention visualizations.