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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Transliteration-Moroccan-Darija |
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results: [] |
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datasets: |
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- atlasia/ATAM |
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language: |
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- ar |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Transliteration-Moroccan-Darija |
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This model is trained to convert Moroccan Darija text written in Arabizi (Latin script) to Arabic letters. |
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Whether you're dealing with informal texts, social media posts, or any other content in Moroccan Arabizi, the model is here to help you accurately transliterate it into Arabic script. |
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## Model Overview |
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Our model is built upon the powerful Transformer architecture, leveraging state-of-the-art natural language processing techniques. |
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It has been trained from scratch on the "atlasia/ATAM" dataset, specifically for the task of transliterating Moroccan Darija Arabizi into Arabic letters, ensuring high-quality and accurate transliterations. |
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Furthermore, we trained a BPE Tokenizer specifically for this task. |
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## Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.02 |
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- num_epochs: 120 |
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## Framework versions |
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- Transformers 4.39.2 |
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- Pytorch 2.2.2+cpu |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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## Usage |
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Using our model for transliteration is simple and straightforward. |
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You can integrate it into your projects or workflows via the Hugging Face Transformers library. |
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Here's a basic example of how to use the model in Python: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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# Load the tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("BounharAbdelaziz/Transliteration-Moroccan-Darija") |
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model = AutoModelForSeq2SeqLM.from_pretrained("BounharAbdelaziz/Transliteration-Moroccan-Darija") |
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# Define your Moroccan Darija Arabizi text |
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input_text = "Your Moroccan Darija Arabizi text goes here." |
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# Tokenize the input text |
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input_tokens = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True) |
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# Perform transliteration |
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output_tokens = model.generate(**input_tokens) |
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# Decode the output tokens |
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output_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True) |
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print("Transliteration:", output_text) |
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``` |
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## Example |
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Let's see an example of transliterating Moroccan Darija Arabizi to Arabic: |
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**Input**: "kayn chi" |
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**Output**: "كاين شي" |
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## Limiations |
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This version has some limitations mainly due to the Tokenizer. |
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We're currently collecting more data with the aim of continous improvements. |
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## Feedback |
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We're continuously striving to improve our model's performance and usability and we will be improving it incrementaly. |
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If you have any feedback, suggestions, or encounter any issues, please don't hesitate to reach out to us. |
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