|
--- |
|
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. |
