metadata
license: apache-2.0
datasets:
- Helsinki-NLP/tatoeba
- openlanguagedata/flores_plus
language:
- es
- gl
metrics:
- bleu
- comet
- chrf
pipeline_tag: translation
OPUS-MT-tiny-spa-glg
Distilled model from a Tatoeba-MT Teacher: Tatoeba-MT-models/deu+eng+fra+por+spa-roa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30, which has been trained on the Tatoeba dataset.
We used the OpusDistillery to train new a new student with the tiny architecture, with a regular transformer decoder. For training data, we used Tatoeba. The configuration file fed into OpusDistillery can be found here.
How to run
from transformers import MarianMTModel, MarianTokenizer
model_name = "Helsinki-NLP/opus-mt_tiny_spa-glg"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
tok = tokenizer("La gastronomía de Mayorca, como la de otras regiones similares del Mediterráneo, se basa en el pan, los vegetales y la carne (especialmente la porcina), y utiliza aceite de oliva en todas sus recetas.", return_tensors="pt").input_ids
output = model.generate(tok)[0]
tokenizer.decode(output, skip_special_tokens=True)
Benchmarks
Teacher
| testset | BLEU | chr-F | COMET |
|---|---|---|---|
| Flores+ | 22.3 | 52.9 | 0.8386 |
Student
| testset | BLEU | chr-F | COMET |
|---|---|---|---|
| Flores+ | 21.6 | 51.9 | 0.8572 |
Marian models
We also provide Marian-compatible versions of this model. To use them, compile Marian and run decoding with marian-decoder, for example:
marian-decoder \
-i input.txt \
-c final.model.npz.best-perplexity.npz.decoder.yml \
-m final.model.npz.best-perplexity.npz \
-v vocab.spm vocab.spm