metadata
datasets:
- Helsinki-NLP/tatoeba
language:
- ko
- en
metrics:
- bleu
- chrf
pipeline_tag: translation
library_name: transformers
Model info
Forked from odegiber/ko-en, with a .tflite version of the model weights added.
Distilled model from a Tatoeba-MT Teacher: Tatoeba-MT-models/kor-eng/opusTCv20210807-sepvoc_transformer-big_2022-07-28, 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 pipeline
>>> pipe = pipeline("translation", model="odegiber/ko-en", max_length=256)
>>> pipe("2017년 말, 시미노프는 쇼핑 텔레비젼 채널인 QVC에 출연했다.")
[{'translation_text': 'At the end of 2017, Siminof appeared on the shopping television channel QVC.'}]
Benchmarks
| testset | BLEU | chr-F |
|---|---|---|
| flores200 | 20.3 | 50.3 |