Create README.md

README.md

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+# Arabic Dotless to Dotted Text Conversion Model
+
+This model is designed to convert dotless Arabic text to dotted (vowelized) Arabic text using a **sequence-to-sequence (seq2seq)** architecture with an **attention mechanism**. It employs deep learning techniques, specifically **Long Short-Term Memory (LSTM)** units, to capture the dependencies within the input and output text sequences.
+
+## Key Features:
+
+### 1. Seq2Seq Architecture
+The model follows a typical encoder-decoder structure used in many sequence generation tasks. 
+- The **encoder** processes the dotless Arabic input text.
+- The **decoder** generates the vowelized (dotted) output text.
+
+### 2. Bidirectional LSTM Encoder
+- The encoder uses a **bidirectional LSTM**, allowing the model to capture both past and future context in the input text. This improves the model's understanding of the full sequence.
+
+### 3. Shared Embedding Layer
+- Both the encoder and decoder share the same **embedding layer**, which maps input tokens (characters or subwords) into dense vector representations.
+- This helps the model generalize better by learning shared patterns across the input and output sequences.
+
+### 4. Attention Mechanism
+- The **attention mechanism** allows the decoder to focus on relevant parts of the input sequence at each step, improving the output sequence's accuracy.
+- It calculates the context vector based on the weighted sum of encoder outputs, which guides the decoding process.
+
+### 5. LSTM Decoder
+- The **decoder LSTM** takes the encoder's final state and the context vector from the attention mechanism to generate the predicted vowelized output sequence.
+
+### 6. Dense Output Layer
+- The output layer is a **dense layer** that generates a probability distribution over the possible output tokens, including diacritics.
+- The model uses **softmax** activation to predict the next token in the sequence.
+
+### 7. Distributed Training
+- The model is optimized for **distributed training** using TensorFlow’s `MirroredStrategy`, which helps train the model across multiple GPUs, significantly speeding up the process on large datasets.
+
+### 8. Loss Function and Optimizer
+- The model uses **sparse categorical crossentropy** as the loss function, which is ideal for multi-class classification problems.
+- The **Adam optimizer** is employed for efficient training and convergence.
+
+## Model Usage:
+
+- **Training**: Train the model with pairs of dotless and vowelized (dotted) Arabic text.
+- **Inference**: After training, input a dotless Arabic sentence, and the model will output the vowelized version of the text.
+
+### Parameters:
+- **vocab_size**: Size of the vocabulary (total number of unique tokens in the input and output space).
+- **max_length**: Maximum length of input sequences.
+- **latent_dim**: Dimension of the embedding and LSTM layers (default is 64).
+
+## Example Workflow:
+
+1. **Training**: Train the model on a large corpus of paired dotless and vowelized Arabic text.
+2. **Inference**: Input a dotless Arabic sentence, and the model outputs the vowelized (dotted) version.
+
+## Applications:
+- **Automatic Diacritization**: Converts dotless Arabic text into vowelized form for better pronunciation and understanding.
+- **Speech Recognition**: Useful in improving accuracy in Arabic speech-to-text systems.
+- **Machine Translation**: Helps in generating accurate translations with proper vowelization for better meaning preservation.
+- **Educational Tools**: Aids in teaching Arabic reading and pronunciation.
+