Model details

This machine translation model can convert single sentences from and to any combination of the following languages:

It was trained on the SALT dataset and a variety of additional external data resources, including back-translated news articles, FLORES-200, MT560 and LAFAND-MT. The base model was facebok/nllb-200-1.3B, with tokens adapted to add support for languages not originally included.

Usage example

tokenizer = transformers.NllbTokenizer.from_pretrained(
    'Sunbird/translate-nllb-1.3b-salt')
model = transformers.M2M100ForConditionalGeneration.from_pretrained(
    'Sunbird/translate-nllb-1.3b-salt')

text = 'Where is the hospital?'
source_language = 'eng'
target_language = 'lug'

language_tokens = {
    'eng': 256047,
    'ach': 256111,
    'lgg': 256008,
    'lug': 256110,
    'nyn': 256002,
    'teo': 256006,
}

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
inputs = tokenizer(text, return_tensors="pt").to(device)
inputs['input_ids'][0][0] = language_tokens[source_language]
translated_tokens = model.to(device).generate(
    **inputs,
    forced_bos_token_id=language_tokens[target_language],
    max_length=100,
    num_beams=5,
)

result = tokenizer.batch_decode(
    translated_tokens, skip_special_tokens=True)[0]
# Eddwaliro liri ludda wa?

Evaluation metrics

Results on salt-dev: