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---
language:
- ar
pipeline_tag: text2text-generation
library_name: keras-hub
tags:
- lstm
---
# 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. |