Model Card for mt-fr-en-tatoeba

This is a fine-tuned version of Helsinki-NLP/opus-mt-fr-en, trained on the Tatoeba dataset for French-to-English translation.

Model Details

• Base Model: Helsinki-NLP/opus-mt-fr-en
• Dataset Used: opus_tatoeba (French-English)
• Fine-tuning Epochs: 3
• Optimizer: AdamW (learning rate: 2e-5)
• Evaluation Metric: BLEU Score
• Pretrained BLEU Score: 57.5 (on Tatoeba)
• Fine-Tuned BLEU Score: 64.43 (on Tatoeba test set, 10% random subset of tatoeba)

Model Description

• Developed by: Mahdi Ihdeme
• Model type: Language model for french to english translation
• Language(s) (NLP): English, French

Usage

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_name = "mihdeme/mt-fr-en-tatoeba"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def translate(sentence):
    inputs = tokenizer(sentence, return_tensors="pt", padding=True, truncation=True)
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

print(translate("Bonjour, comment ça va ?"))

Training Configuration

• Batch Size: 16
• Max Sequence Length: 512
• Hardware Used: Google Colab GPU (Tesla T4)

License

Apache 2.0

Acknowledgments

Trained using Hugging Face Transformers. Original dataset from Tatoeba.