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Helsinki-NLP/
opus-mt_tiny_eng-rus

Name: opus-mt_tiny_eng-rus
Availability: InStock

openlanguagedata/flores_plus

facebook/bouquet

apache-2.0

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OPUS-MT-tiny-rus-eng

Distilled model from a Tatoeba-MT Teacher: OPUS-MT-models/en-ru/opus-2020-02-11, 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

python

from transformers import MarianMTModel, MarianTokenizer
model_name = "Helsinki-NLP/opus-mt_tiny_eng-rus"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
tok = tokenizer("Hello, how are you?", 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+	25.8	53.9	0.8459
Bouquet	31.8	55.3	0.8685

Student

testset	BLEU	chr-F	COMET
Flores+	24.4	52.0	0.8122
Bouquet	28.0	53.0	0.8243

Marian models

We also provide Marian-compatible versions of this model. To use them, compile Marian and run decoding with marian-decoder, for example:

bash

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