## 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`](https://huggingface.co/datasets/FrancophonIA/english_french)
- **Framework**: Hugging Face Transformers & Datasets
- **Accelerator**: CUDA (GPU)

---

## 📦 Dependencies

Install all required Python packages:

```python
 pip install torch transformers datasets evaluate sentencepiece
```

# Load Dataset


```python
 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:

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

## 📏 Evaluation

```python
from evaluate import load

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