Helsinki-NLP_opus-mt_tiny_eng-tur

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license: apache-2.0 datasets:

• Helsinki-NLP/tatoeba
• openlanguagedata/flores_plus
• facebook/bouquet language:
• en
• tr metrics:
• bleu
• comet
• chrf pipeline_tag: translation

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OPUS-MT-tiny-eng-tur

Distilled model from a Tatoeba-MT Teacher: Tatoeba-MT-models/eng-tur/opusTCv20210807+bt_transformer-big_2022-02-25), 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-tur"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
tok = tokenizer("Persians have a relatively easy and mostly smooth grammar.", 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+	31.4	62.8	0.8854
Bouquet	36.6	66.5	0.9058

Student

testset	BLEU	chr-F	COMET
Flores+	28.5	62.8	0.8633
Bouquet	33.0	64.6	0.8899

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