license: cc-by-4.0
library_name: transformers
pipeline_tag: translation
tags:
- translation
- marian
- cameroon
- low-resource
- french-pivot
language:
- fr
français → Bulu — French-pivot MT (fp32)
Original fp32 MarianMT translation model for Cameroonian languages, part of the Lingo / NativeAI language-preservation project (first released September 2024).
Translation runs through French as a pivot (e.g. Ghomálá' → français → Ewondo),
so each language only needs an X→français and a français→X model. The models were
trained on a corpus we compiled by hand — scripture as the aligned backbone plus
gathered books, pamphlets and other written material — normalised under the AGLC
alphabet (Alphabet Général des Langues Camerounaises). Background: the
research log.
Compressed serving bundle
For deployment we serve int8 CTranslate2
conversions (~3.8× smaller, ~6× faster on CPU), bundled at
flagship-ai/cameroon-int8.
These fp32 weights are the originals / reference.
Usage
from transformers import MarianMTModel, MarianTokenizer
model = MarianMTModel.from_pretrained("flagship-ai/francais-bulu")
# Shared SentencePiece tokenizer ships in the int8 bundle subfolders:
tok = MarianTokenizer.from_pretrained("flagship-ai/cameroon-int8", subfolder="francais-bulu")
ids = tok("Bonjour, comment vas-tu ?", return_tensors="pt")
print(tok.decode(model.generate(**ids)[0], skip_special_tokens=True))
Limitations
Trained on a small, formal-leaning corpus, so it is strongest on everyday/simple sentences and weaker on modern or technical vocabulary. It is an early, open baseline — an ongoing voice-data collection project aims to broaden coverage with real spoken contributions.
— Open models for Cameroonian languages · lingo.cm