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README.md
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pipeline_tag: translation
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---
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metrics:
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pipeline_tag: translation
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---
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# Model info
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Distilled model from a Tatoeba-MT Teacher: [Tatoeba-MT-models/kor-eng/opusTCv20210807-sepvoc_transformer-big_2022-07-28](https://object.pouta.csc.fi/Tatoeba-MT-models/kor-eng/opusTCv20210807-sepvoc_transformer-big_2022-07-28.zip), which has been trained on the [Tatoeba](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/data) dataset.
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We used the [OpusDistillery](https://github.com/Helsinki-NLP/OpusDistillery) to train new a new student with the tiny architecture, with a regular transformer decoder.
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For training data, we used [Tatoeba](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/data).
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The configuration file fed into OpusDistillery can be found [here](https://github.com/Helsinki-NLP/OpusDistillery/blob/main/configs/hplt/config.hplt.kor-eng.yml).
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## How to run
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```
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>>> from transformers import pipeline
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>>> pipe = pipeline("translation", model="odegiber/ko-en", max_length=256)
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>>> pipe("2017๋
๋ง, ์๋ฏธ๋
ธํ๋ ์ผํ ํ
๋ ๋น์ ผ ์ฑ๋์ธ QVC์ ์ถ์ฐํ๋ค.")
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[{'translation_text': 'At the end of 2017, Siminof appeared on the shopping television channel QVC.'}]
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```
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## Benchmarks
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| testset | BLEU | chr-F |
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|-----------------------|-------|-------|
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| flores200 | 20.3 | 50.3 |
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