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Helsinki-NLP/
opus-mt_tiny_ell-eng

Name: opus-mt_tiny_ell-eng
Availability: InStock

openlanguagedata/flores_plus

facebook/bouquet

apache-2.0

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OPUS-MT-tiny-ell-eng

Distilled model from a Tatoeba-MT Teacher: Tatoeba-MT-models/ell-eng/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

python

from transformers import MarianMTModel, MarianTokenizer
model_name = "Helsinki-NLP/opus-mt_tiny_ell-eng"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
tok = tokenizer("Τα περσικά έχουν μια σχετικά εύκολη και ως επί το πλείστον ομαλή γραμματική.", 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+	33.8	61.2	0.8558
Bouquet	44.1	38.8	0.8809

Student

testset	BLEU	chr-F	COMET
Flores+	29.3	57.5	0.8372
Bouquet	38.8	60.3	0.8625

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