metadata
license: apache-2.0
datasets:
- Helsinki-NLP/tatoeba
- openlanguagedata/flores_plus
- facebook/bouquet
language:
- en
- nl
metrics:
- bleu
- comet
- chrf
pipeline_tag: translation
OPUS-MT-tiny-eng-nld
Distilled model from a Tatoeba-MT Teacher: Tatoeba-MT-models/deu+eng+fra+por+spa-gmw/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_eng-nld"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
tok = tokenizer("The area is also home to species of animals and birds with a wide variety.", 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+ | 27.1 | 58.3 | 0.8468 |
| Bouquet | 54.1 | 73.6 | 0.8828 |
Student
| testset | BLEU | chr-F | COMET |
|---|---|---|---|
| Flores+ | 24.9 | 56.7 | 0.8301 |
| Bouquet | 49.7 | 70.6 | 0.8714 |
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