Update README.md

README.md

@@ -11,4 +11,70 @@ license: mit
 short_description: Real-Time Tweet Sentiment Analyzer
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🧠 Sentiment Analysis from Scratch (BiLSTM + Attention)
+
+Welcome to this live interactive demo of a sentiment analysis model trained completely from scratch using a **Deep Bidirectional LSTM** architecture enhanced with a **custom attention mechanism**. This project is designed to classify short texts or tweets into **Positive** or **Negative** sentiments with a confidence score.
+
+---
+
+## 📌 Project Highlights
+
+- ✅ **Trained from scratch**: The embedding layer is trained on the dataset itself (not using pretrained embeddings).
+- 🧠 **Model Architecture**: 
+  - Bidirectional LSTM layers
+  - Custom attention layer (`BetterAttention`)
+  - Final dense ANN for binary classification
+- 📊 **Output**: Label (Positive/Negative) and confidence score (0–1)
+- 🔠 **Tokenizer**: Also trained from scratch and saved as `tokenizer.joblib`
+- 📁 **Model Format**: Saved as `.keras` and loaded efficiently during inference
+
+---
+
+## 🚀 Try it Out
+
+Enter a tweet or short sentence below and see real-time prediction:
+
+👉 *Example*:  
+`"I absolutely loved the performance!"`  
+**Output**: Positive (0.91)
+
+---
+
+## 🛠 Model Files
+
+You can also explore/download the trained artifacts here:
+- [`sentiment_model.keras`](https://huggingface.co/MasterShomya/Sentiment_Analysis-Tweets/blob/main/sentiment_model.keras)
+- [`tokenizer.joblib`](https://huggingface.co/MasterShomya/Sentiment_Analysis-Tweets/blob/main/tokenizer.joblib)
+
+---
+
+## 🧪 How It Works
+
+1. The input text is tokenized using the trained tokenizer (`joblib`).
+2. The padded sequence is passed through:
+   - `Embedding → BiLSTM → BiLSTM → Attention → Dense Layers`
+3. The final sigmoid-activated output represents the **probability of positivity**.
+4. A confidence-aware label is returned using Gradio’s `Label` component.
+
+---
+
+## 📈 Model Performance
+
+Despite training from scratch without pretrained embeddings (like GloVe or FastText), the model performs comparably well. Experiments with `glove.27B.200d` embeddings yielded **similar accuracy**, and hence were excluded for clarity.
+
+Training plots and confusion matrix are available in the original [Kaggle Notebook](https://www.kaggle.com/code/mastershomya/sentiment-analysis-deep-bilstm).
+
+---
+
+## 🧑‍💻 Author
+
+**Shomya Soneji**  
+Machine Learning & Deep Learning Enthusiast  
+Connect on [Kaggle](https://www.kaggle.com/mastershomya)
+
+---
+
+## 🤝 Support
+
+If you find this project helpful, please consider giving it a 🌟 and sharing it!  
+Your feedback and suggestions are always welcome 💬