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	---
	language:
	- en
	- ko

	tags:
	- translation
	- opus-mt-tc

	license: cc-by-4.0
	model-index:
	- name: opus-mt-tc-big-en-ko
	results:
	- task:
	name: Translation eng-kor
	type: translation
	args: eng-kor
	dataset:
	name: flores101-devtest
	type: flores_101
	args: eng kor devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 13.7
	- name: chr-F
	type: chrf
	value: 0.36399
	---
	# opus-mt-tc-big-en-ko

	## Table of Contents
	- [Model Details](#model-details)
	- [Uses](#uses)
	- [Risks, Limitations and Biases](#risks-limitations-and-biases)
	- [How to Get Started With the Model](#how-to-get-started-with-the-model)
	- [Training](#training)
	- [Evaluation](#evaluation)
	- [Citation Information](#citation-information)
	- [Acknowledgements](#acknowledgements)

	## Model Details

	Neural machine translation model for translating from English (en) to Korean (ko).

	This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).
	Model Description:
	- Developed by: Language Technology Research Group at the University of Helsinki
	- Model Type: Translation (transformer-big)
	- Release: 2022-07-28
	- License: CC-BY-4.0
	- Language(s):
	- Source Language(s):
	- Target Language(s):
	- Valid Target Language Labels:
	- Original Model: [opusTCv20210807-sepvoc_transformer-big_2022-07-28.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-kor/opusTCv20210807-sepvoc_transformer-big_2022-07-28.zip)
	- Resources for more information:
	- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
	- More information about released models for this language pair: [OPUS-MT eng-kor README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-kor/README.md)
	- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
	- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/

	This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>><<`

	## Uses

	This model can be used for translation and text-to-text generation.

	## Risks, Limitations and Biases

	CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.

	Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).

	## How to Get Started With the Model

	A short example code:

	```python
	from transformers import MarianMTModel, MarianTokenizer

	src_text = [
	"2, 4, 6 etc. are even numbers.",
	"Yes."
	]

	model_name = "pytorch-models/opus-mt-tc-big-en-ko"
	tokenizer = MarianTokenizer.from_pretrained(model_name)
	model = MarianMTModel.from_pretrained(model_name)
	translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))

	for t in translated:
	print( tokenizer.decode(t, skip_special_tokens=True) )

	# expected output:
	# 2, 4, 6 등은 짝수입니다.
	# 그래
	```

	You can also use OPUS-MT models with the transformers pipelines, for example:

	```python
	from transformers import pipeline
	pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-ko")
	print(pipe("2, 4, 6 etc. are even numbers."))

	# expected output: 2, 4, 6 등은 짝수입니다.
	```

	## Training

	- Data: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
	- Pre-processing: SentencePiece (spm32k,spm32k)
	- Model Type: transformer-big
	- Original MarianNMT Model: [opusTCv20210807-sepvoc_transformer-big_2022-07-28.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-kor/opusTCv20210807-sepvoc_transformer-big_2022-07-28.zip)
	- Training Scripts: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)

	## Evaluation

	* test set translations: [opusTCv20210807-sepvoc_transformer-big_2022-07-28.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-kor/opusTCv20210807-sepvoc_transformer-big_2022-07-28.test.txt)
	* test set scores: [opusTCv20210807-sepvoc_transformer-big_2022-07-28.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-kor/opusTCv20210807-sepvoc_transformer-big_2022-07-28.eval.txt)
	* benchmark results: [benchmark_results.txt](benchmark_results.txt)
	* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)

	\| langpair \| testset \| chr-F \| BLEU \| #sent \| #words \|
	\|----------\|---------\|-------\|-------\|-------\|--------\|

	## Citation Information

	* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)

	```
	@inproceedings{tiedemann-thottingal-2020-opus,
	title = "{OPUS}-{MT} {--} Building open translation services for the World",
	author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
	booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
	month = nov,
	year = "2020",
	address = "Lisboa, Portugal",
	publisher = "European Association for Machine Translation",
	url = "https://aclanthology.org/2020.eamt-1.61",
	pages = "479--480",
	}

	@inproceedings{tiedemann-2020-tatoeba,
	title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
	author = {Tiedemann, J{\"o}rg},
	booktitle = "Proceedings of the Fifth Conference on Machine Translation",
	month = nov,
	year = "2020",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2020.wmt-1.139",
	pages = "1174--1182",
	}
	```

	## Acknowledgements

	The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.

	## Model conversion info

	* transformers version: 4.16.2
	* OPUS-MT git hash: 8b9f0b0
	* port time: Fri Aug 12 11:02:03 EEST 2022
	* port machine: LM0-400-22516.local