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README.md

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-title: text
+title: 🐦 Twitter Sentiment Analysis using DistilBERT
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-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+
+This project demonstrates a sentiment analysis pipeline built with **DistilBERT**, a lightweight transformer model developed by Hugging Face. The model was fine-tuned on a dataset of 16,000 tweets to classify sentiment into categories such as **Positive**, **Negative**, and **Neutral**. The final model achieved an impressive **90% accuracy** on the validation set.
+
+---
+
+## 🚀 Features
+
+* Utilizes **DistilBERT** for high-performance NLP with lower resource consumption.
+* Cleaned and preprocessed Twitter data (16K rows).
+* Fine-tuned with PyTorch and Hugging Face Transformers.
+* Achieved **90%+ accuracy** on sentiment classification.
+* Includes training, validation, and evaluation pipelines.
+
+---
+
+## 📁 Dataset
+
+* 16,000 manually labeled tweets with three sentiment classes:
+
+  * `Positive`
+  * `Negative`
+  * `Neutral`
+* Dataset was preprocessed to remove mentions, hashtags, links, and special characters.
+
+---
+
+## 🧠 Model
+
+* **Base Model**: `distilbert-base-uncased`
+* **Fine-tuning**: Trained for several epochs using a cross-entropy loss function and AdamW optimizer.
+* **Tokenizer**: Hugging Face `DistilBertTokenizerFast`
+* **Training Framework**: PyTorch + Hugging Face `Trainer` API
+
+---
+
+## 📊 Performance
+
+| Metric    | Score |
+| --------- | ----- |
+| Accuracy  | 90%   |
+| Precision | High  |
+| Recall    | High  |
+| F1-score  | High  |
+
+> Note: Actual precision, recall, and F1-score values can be added if available.
+
+---
+
+## 📦 Dependencies
+
+```bash
+transformers==4.x.x
+torch==1.x
+scikit-learn
+pandas
+numpy
+matplotlib
+```
+
+Install with:
+
+```bash
+pip install -r requirements.txt
+```
+
+---
+
+## 🛠️ How to Run
+
+1. Clone the repository:
+
+   ```bash
+   git clone https://github.com/yourusername/twitter-sentiment-distilbert.git
+   cd twitter-sentiment-distilbert
+   ```
+
+2. Install dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Train the model:
+
+   ```bash
+   python train.py
+   ```
+
+4. Evaluate the model:
+
+   ```bash
+   python evaluate.py
+   ```
+
+5. Run prediction on new tweets:
+
+   ```bash
+   python predict.py --text "I love this app!"
+   ```
+
+---
+
+## 📈 Example Output
+
+```bash
+Input: "I love this app!"
+Predicted Sentiment: Positive
+```
+
+---
+
+## 📚 Future Improvements
+
+* Integrate with a live Twitter API for real-time sentiment tracking.
+* Add a web dashboard using Streamlit or Flask.
+* Extend to multilingual support using `xlm-roberta`.
+
+---
+
+## 📄 License
+
+This project is open-source and available under the [MIT License](LICENSE).
+
+---