Esmail-AGumaan/LlTRA-model

LlTRA stands for: Language to Language Transformer model from the paper "Attention is all you Need", building transformer model:Transformer model from scratch and using it for translation using pytorch.

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Problem Statement: In the rapidly evolving landscape of natural language processing (NLP) and machine translation, there exists a persistent challenge in achieving accurate and contextually rich language-to-language transformations. Existing models often struggle with capturing nuanced semantic meanings, context preservation, and maintaining grammatical coherence across different languages. Additionally, the demand for efficient cross-lingual communication and content generation has underscored the need for a versatile language transformer model that can seamlessly navigate the intricacies of diverse linguistic structures.

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Goal: Develop a specialized language-to-language transformer model that accurately translates from the Arabic language to the English language, ensuring semantic fidelity, contextual awareness, cross-lingual adaptability, and the retention of grammar and style. The model should provide efficient training and inference processes to make it practical and accessible for a wide range of applications, ultimately contributing to the advancement of Arabic-to-English language translation capabilities.

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Dataset used: from hugging Face huggingface/opus_infopankki

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Configuration: this is the settings of the model, You can customize the source and target languages, sequence lengths for each, the number of epochs, batch size, and more.

def Get_configuration():
    return {
        "batch_size": 8,
        "num_epochs": 30,
        "lr": 10**-4,
        "sequence_length": 100,
        "d_model": 512,
        "datasource": 'opus_infopankki',
        "source_language": "ar",
        "target_language": "en",
        "model_folder": "weights",
        "model_basename": "tmodel_",
        "preload": "latest",
        "tokenizer_file": "tokenizer_{0}.json",
        "experiment_name": "runs/tmodel"
    }

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Training: I used my drive to upload the project and then connected it to the Google Collab to train it:

• hours of training: 4 hours.
• epochs: 20.
• number of dataset rows: 2,934,399.
• size of the dataset: 95MB.
• size of the auto-converted parquet files: 153MB.
• Arabic tokens: 29999.
• English tokens: 15697.
• pre-trained model in collab.
• BLEU score from Arabic to English: 19.7

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