Update README.md

README.md

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 license: mit
 short_description: It is to get the model to tell truth about real world news
 ---
+# Misinformation Detection Tool
+
+## Overview
+
+Misinformation has become a significant issue in today's digital age, influencing public opinion and spreading unreliable news. This project addresses the problem by building a robust **Misinformation Detecting Tool** using advanced **Hugging Face Transformers**. The system is capable of identifying whether a given news article or statement is genuine or fake.
+
+## Problem Statement
+
+The rapid spread of misinformation through online platforms and social media has created the need for reliable tools to combat fake news. Identifying fake news manually is time-consuming and prone to bias. This project automates the detection of fake news using natural language processing (NLP) techniques, ensuring scalability and accuracy.
+
+## Objective
+
+The objective of this project is to develop and deploy a machine learning model capable of analyzing textual data and accurately classifying it as either **real** or **fake** news. The solution is deployed using Hugging Face Transformers to make it accessible and scalable.
+
+## Features
+
+- **Deep Learning Model**: Built on Hugging Face Transformers for state-of-the-art text classification.
+- **Scalable Deployment**: Deployed on Hugging Face for seamless integration and access.
+- **Real-Time Prediction**: Provides instant results for news articles or headlines.
+
+## Methodology
+
+1. **Data Collection**:
+   - Collected datasets from Kaggle and other reliable sources containing labeled news articles.
+2. **Data Preprocessing**:
+
+   - Cleaned and tokenized text data.
+   - Removed stop words, special characters, and performed lemmatization.
+
+3. **Model Selection**:
+
+   - Used a pre-trained transformer model (e.g., BERT, RoBERTa) from Hugging Face.
+   - Fine-tuned the model on the fake news dataset.
+
+4. **Training**:
+
+   - Split the dataset into training and validation sets.
+   - Used PyTorch backend for training with optimization techniques.
+
+5. **Evaluation**:
+
+   - Measured performance using metrics like accuracy, precision, recall, and F1-score.
+   - Validated the model with a test dataset to ensure generalizability.
+
+6. **Deployment**:
+   - Deployed the model on Hugging Face for public access.
+   - API created for real-time predictions.
+
+## Scope
+
+- **Immediate Use**: Detects fake news effectively from textual inputs such as headline or article links.
+- **Future Enhancements**:
+  - Incorporating language detection and translation for multilingual support.
+  - Extending the dataset to include more diverse topics and sources.
+  - Integration with video and audio analysis for multimedia content.
+  - Expanded database for fact-checking and knowledge graphs.
+
+## Installation and Usage
+
+### Local Setup
+
+1. Clone the repository:
+
+   ```bash
+   git lfs install
+   git clone https://huggingface.co/spaces/malavika4089/misinformation-truthteller/tree/main
+   cd misinformation-truthteller
+   ```
+
+2. Install dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Run the script:
+   ```bash
+   streamlit run app.py
+   ```
+
+### Access Deployed Model
+
+The model is deployed on Hugging Face. You can access it [Live link](https://huggingface.co/spaces/malavika4089/misinformation-truthteller).
+
+## Dataset
+
+The dataset used for this project was sourced from:
+
+- [Kaggle Fake And Real News Dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
+
+## Technologies Used
+
+- **Programming Language**: Python
+- **Libraries**: Hugging Face Transformers, PyTorch, Scikit-learn, NumPy, Pandas, streamlit
+- **Deployment**: Hugging Face Spaces,
+- **Tools**: Colab
+
+## License
+
+This project is licensed under the [MIT License](LICENSE).
+
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference