Create README.md

README.md

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+---
+license: mit
+language:
+- ar
+- en
+library_name: keras
+---
+# Arabic Semantic / Sentiment Classification using BiLSTM
+
+This repository contains a TensorFlow/Keras-based **Bidirectional LSTM (BiLSTM)** model for Arabic text classification.  
+The model is designed for **binary classification tasks** such as sentiment or semantic polarity detection.
+
+## Overview
+- **Language:** Arabic
+- **Task:** Binary text classification
+- **Model:** BiLSTM neural network
+- **Framework:** TensorFlow / Keras
+- **Focus:** Emoji-aware preprocessing and Arabic stemming
+
+This project combines classical NLP preprocessing with deep learning to handle informal Arabic text, including emojis.
+
+---
+
+## Model Architecture
+The neural network architecture consists of:
+
+- Embedding layer (vocabulary size = 10,000, embedding dim = 128)
+- Bidirectional LSTM (128 units, return sequences)
+- Dropout (0.5)
+- Bidirectional LSTM (64 units)
+- Dense layer (32 units, ReLU)
+- Output layer (1 unit, Sigmoid)
+
+Loss function: **Binary Crossentropy**  
+Optimizer: **Adam (lr = 0.001)**
+
+---
+
+## Preprocessing Pipeline
+The preprocessing steps are critical and must be applied **exactly as during training**:
+
+1. Emoji conversion using `demoji`
+2. Whitespace and regex normalization
+3. Tokenization using NLTK
+4. Arabic stemming using **ISRIStemmer**
+5. Keras tokenization and padding (max length = 100)
+
+This pipeline allows the model to better handle:
+- Informal Arabic
+- Social media text
+- Emoji-heavy content
+
+---
+
+## Files in This Repository
+
+| File | Description |
+|-----|------------|
+| `lstm_text_model.h5` | Trained BiLSTM model |
+| `tokenizer.pkl` | Keras tokenizer (must match training) |
+| `label_encoder.pkl` | Label encoder for output mapping |
+| `requirements.txt` | Python dependencies |
+
+---
+
+## How to Use the Model
+
+### Installation
+```bash
+pip install -r requirements.txt