AventIQ-AI/English-To-French

English to French Translation AI Model

This demonstrates training, quantization, and inference of a text translation model from English to French using Hugging Face Transformers on CUDA-enabled devices.

🧠 Model Overview

• Base Model: Helsinki-NLP/opus-mt-tc-big-en-fr
• Task: English to French text translation
• Dataset: FrancophonIA/english_french
• Framework: Hugging Face Transformers & Datasets
• Accelerator: CUDA (GPU)

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📦 Dependencies

Install all required Python packages:

 pip install torch transformers datasets evaluate sentencepiece

Load Dataset

 from datasets import load_dataset
 dataset = load_dataset("FrancophonIA/english_french")
 dataset["train"] = dataset["train"].shuffle(seed=42).select(range(60000))

⚙️ Training Configuration

Training is done using Seq2SeqTrainer with the following configuration:

• batch_size: 8

• epochs: 3

• fp16: Mixed precision enabled

• save_strategy: Disabled to reduce I/O

• report_to: Disabled (no Weights & Biases)

🧊 Model Quantization

We apply dynamic quantization on the trained model to reduce size and enable CPU inference:

quantized_model = torch.quantization.quantize_dynamic(
    model.cpu(), {torch.nn.Linear}, dtype=torch.qint8
)

📏 Evaluation

from evaluate import load

metric = load("sacrebleu")
score = metric.compute(predictions=predictions, references=references)
print(f"BLEU Score: {score['score']}")