Update README.md

README.md

@@ -12,3 +12,84 @@ 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.
+
+---