BounharAbdelaziz/Transliteration-Moroccan-Darija

A Transformer translitertion model to convert Moroccan Darija text written in Arabizi (Latin script) to Arabic letters (Ary). # Transliteration-Moroccan-Darija This model is trained to convert Moroccan Darija text written in Arabizi (Latin script) to Arabic letters. 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. ## Model Overview Our model is built upon the powerful Transformer architecture, leveraging state-of-the-art natural language processing techniques. 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. Furthermore, we trained a BPE Tokenizer specifically for this task. The model checkpoint is available in Hugging Face. ## Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.02 - num_epochs: 120 ## Framework versions - Transformers 4.39.2 - Pytorch 2.2.2+cpu - Datasets 2.18.0 - Tokenizers 0.15.2 ## Usage Using our model for transliteration is simple and straightforward. You can integrate it into your projects or workflows via the Hugging Face Transformers library. Here's a basic example of how to use the model in Python: ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained("atlasia/Transliteration-Moroccan-Darija") model = AutoModelForSeq2SeqLM.from_pretrained("atlasia/Transliteration-Moroccan-Darija") # Define your Moroccan Darija Arabizi text input_text = "Your Moroccan Darija Arabizi text goes here." # Tokenize the input text input_tokens = tokenizer(input_text, return_tensors="pt", padding=True, truncati …