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Helsinki-NLP/
opus-mt_tiny_ara-eng

Name: opus-mt_tiny_ara-eng
Availability: InStock

openlanguagedata/flores_plus

apache-2.0

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OPUS-MT-tiny-ara-eng

Distilled model from a Tatoeba-MT Teacher: OPUS-MT-models/ar-en/opus-2019-12-18, 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_ara-eng"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
tok = tokenizer('أضاف قائلاً، "لدينا الآن فئران تبلغ من العمر 4 أشهر التي كانت تعاني في السابق من مرض السكري، ولكنها لم تعد تعاني منه الآن.', 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+	34.0	61.0	0.8307

Student

testset	BLEU	chr-F	COMET
Flores+	31.1	59.1	0.8174

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