Add `opus-mt-tc` tag (#1)

bf1e5da almost 2 years ago

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	---
	language:
	- be
	- ru
	- uk
	- zle
	tags:
	- translation
	- opus-mt-tc
	license: cc-by-4.0
	model-index:
	- name: opus-mt-tc-big-zle-zle
	results:
	- task:
	name: Translation rus-ukr
	type: translation
	args: rus-ukr
	dataset:
	name: flores101-devtest
	type: flores_101
	args: rus ukr devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 25.5
	- task:
	name: Translation ukr-rus
	type: translation
	args: ukr-rus
	dataset:
	name: flores101-devtest
	type: flores_101
	args: ukr rus devtest
	metrics:
	- name: BLEU
	type: bleu
	value: 28.3
	- task:
	name: Translation bel-rus
	type: translation
	args: bel-rus
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: bel-rus
	metrics:
	- name: BLEU
	type: bleu
	value: 68.6
	- task:
	name: Translation bel-ukr
	type: translation
	args: bel-ukr
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: bel-ukr
	metrics:
	- name: BLEU
	type: bleu
	value: 65.5
	- task:
	name: Translation rus-bel
	type: translation
	args: rus-bel
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: rus-bel
	metrics:
	- name: BLEU
	type: bleu
	value: 50.3
	- task:
	name: Translation rus-ukr
	type: translation
	args: rus-ukr
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: rus-ukr
	metrics:
	- name: BLEU
	type: bleu
	value: 70.1
	- task:
	name: Translation ukr-bel
	type: translation
	args: ukr-bel
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: ukr-bel
	metrics:
	- name: BLEU
	type: bleu
	value: 58.9
	- task:
	name: Translation ukr-rus
	type: translation
	args: ukr-rus
	dataset:
	name: tatoeba-test-v2021-08-07
	type: tatoeba_mt
	args: ukr-rus
	metrics:
	- name: BLEU
	type: bleu
	value: 75.7
	---
	# opus-mt-tc-big-zle-zle

	Neural machine translation model for translating from East Slavic languages (zle) to East Slavic languages (zle).

	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).

	* 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",
	}
	```

	## Model info

	* Release: 2022-03-07
	* source language(s): bel rus ukr
	* target language(s): bel rus ukr
	* valid target language labels: >>bel<< >>rus<< >>ukr<<
	* model: transformer-big
	* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
	* tokenization: SentencePiece (spm32k,spm32k)
	* original model: [opusTCv20210807+bt_transformer-big_2022-03-07.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opusTCv20210807+bt_transformer-big_2022-03-07.zip)
	* more information released models: [OPUS-MT zle-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-zle/README.md)
	* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)

	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. `>>bel<<`

	## Usage

	A short example code:

	```python
	from transformers import MarianMTModel, MarianTokenizer

	src_text = [
	">>ukr<< Кот мёртвый.",
	">>bel<< Джон живе в Нью-Йорку."
	]

	model_name = "pytorch-models/opus-mt-tc-big-zle-zle"
	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:
	# Кіт мертвий.
	# Джон жыве ў Нью-Йорку.
	```

	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-zle-zle")
	print(pipe(">>ukr<< Кот мёртвый."))

	# expected output: Кіт мертвий.
	```

	## Benchmarks

	* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-07.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opusTCv20210807+bt_transformer-big_2022-03-07.test.txt)
	* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-07.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-zle/opusTCv20210807+bt_transformer-big_2022-03-07.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 \|
	\|----------\|---------\|-------\|-------\|-------\|--------\|
	\| bel-rus \| tatoeba-test-v2021-08-07 \| 0.82526 \| 68.6 \| 2500 \| 18895 \|
	\| bel-ukr \| tatoeba-test-v2021-08-07 \| 0.81036 \| 65.5 \| 2355 \| 15179 \|
	\| rus-bel \| tatoeba-test-v2021-08-07 \| 0.66943 \| 50.3 \| 2500 \| 18756 \|
	\| rus-ukr \| tatoeba-test-v2021-08-07 \| 0.83639 \| 70.1 \| 10000 \| 60212 \|
	\| ukr-bel \| tatoeba-test-v2021-08-07 \| 0.75368 \| 58.9 \| 2355 \| 15175 \|
	\| ukr-rus \| tatoeba-test-v2021-08-07 \| 0.86806 \| 75.7 \| 10000 \| 60387 \|
	\| bel-rus \| flores101-devtest \| 0.47960 \| 14.5 \| 1012 \| 23295 \|
	\| bel-ukr \| flores101-devtest \| 0.47335 \| 12.8 \| 1012 \| 22810 \|
	\| rus-ukr \| flores101-devtest \| 0.55287 \| 25.5 \| 1012 \| 22810 \|
	\| ukr-rus \| flores101-devtest \| 0.56224 \| 28.3 \| 1012 \| 23295 \|

	## 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: 1bdabf7
	* port time: Thu Mar 24 00:15:39 EET 2022
	* port machine: LM0-400-22516.local