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

97b1878 about 2 years ago

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5.48 kB

	---
	language:
	- fi
	- uk
	tags:
	- translation
	- opus-mt-tc
	license: cc-by-4.0
	---
	# opus-mt-tc-base-fi-uk

	Neural machine translation model for translating from Finnish (fi) to Ukrainian (uk).

	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): fin
	* target language(s): ukr
	* model: transformer-align
	* data: opusTCv20210807+pbt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
	* tokenization: SentencePiece (spm32k,spm32k)
	* original model: [opusTCv20210807+pbt_transformer-align_2022-03-07.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-ukr/opusTCv20210807+pbt_transformer-align_2022-03-07.zip)
	* more information released models: [OPUS-MT fin-ukr README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-ukr/README.md)

	## Usage

	A short example code:

	```python
	from transformers import MarianMTModel, MarianTokenizer

	src_text = [
	"Afrikka on ihmiskunnan kehto.",
	"Yksi, kaksi, kolme, neljä, viisi, kuusi, seitsemän, kahdeksan, yhdeksän, kymmenen."
	]

	model_name = "pytorch-models/opus-mt-tc-base-fi-uk"
	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:
	# Африка є колискою людства.
	# Один, два, три, чотири, п'ять, шість, сім, вісім, дев'ять, десять.
	```

	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-base-fi-uk")
	print(pipe("Afrikka on ihmiskunnan kehto."))

	# expected output: Африка є колискою людства.
	```

	## Benchmarks

	* test set translations: [opusTCv20210807+pbt_transformer-align_2022-03-07.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-ukr/opusTCv20210807+pbt_transformer-align_2022-03-07.test.txt)
	* test set scores: [opusTCv20210807+pbt_transformer-align_2022-03-07.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-ukr/opusTCv20210807+pbt_transformer-align_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 \|
	\|----------\|---------\|-------\|-------\|-------\|--------\|
	\| fin-ukr \| flores101-devtest \| 0.49562 \| 19.7 \| 1012 \| 22810 \|

	## 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 02:00:05 EET 2022
	* port machine: LM0-400-22516.local