pumadic-en-fr

Pumatic English-French Translation Model

A neural machine translation model for English to French translation built with the MarianMT architecture.

Model Description

• Model type: Encoder-Decoder (MarianMT architecture)
• Language pair: English → French
• Parameters: ~74.7M
• GPU: H100
• Trained by: pumad

Training Details

Training Data

The model was trained on high-quality parallel corpora:

• OPUS-100 - Multilingual parallel corpus
• Europarl - European Parliament proceedings
• UN Parallel Corpus (UNPC) - United Nations documents

Training Procedure

• Hardware: NVIDIA H100 GPU
• Framework: Hugging Face Transformers
• Batch size: 128
• Learning rate: 2e-5
• Epochs: 3
• Max sequence length: 128 tokens

Data Preprocessing

• Quality filtering: Removed pairs with fewer than 5 words or more than 200 words
• Length ratio filtering: Excluded pairs with extreme length ratios (< 0.5 or > 2.0)
• Deduplication: Removed duplicate source sentences

Usage

Using the Transformers library

from transformers import MarianMTModel, MarianTokenizer

model_name = "pumad/pumadic-en-fr"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)

text = "Hello, how are you today?"
inputs = tokenizer(text, return_tensors="pt", padding=True)
translated = model.generate(**inputs)
output = tokenizer.decode(translated[0], skip_special_tokens=True)
print(output)

Using the Pipeline API

from transformers import pipeline

translator = pipeline("translation", model="pumad/pumadic-en-fr")
result = translator("The quick brown fox jumps over the lazy dog.")
print(result[0]['translation_text'])

Demo

Try this model live at pumatic.eu

API documentation available at pumatic.eu/docs

Limitations

• Optimized for general-purpose translation; domain-specific terminology may vary in quality
• Maximum input length of ~400 characters per chunk for optimal results
• Best performance on formal/written text; colloquial expressions may be less accurate

License

Apache 2.0

Citation

If you use this model, please cite:

@misc{pumatic-en-fr,
  author = {pumad},
  title = {Pumatic English-French Translation Model},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/pumad/pumadic-en-fr}
}