opus-mt-tc-big-itc-tr

Model Details
Uses
Risks, Limitations and Biases
How to Get Started With the Model
Training
Evaluation
Citation Information
Acknowledgements

Model Details

Neural machine translation model for translating from Italic languages (itc) to Turkish (tr).

This model is part of the OPUS-MT project, 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, 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 and training pipelines use the procedures of 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): cat fra glg ita lad lad_Latn oci por ron spa
- Target Language(s): tur
- Language Pair(s): cat-tur fra-tur glg-tur ita-tur oci-tur por-tur ron-tur spa-tur
- Valid Target Language Labels:
Original Model: opusTCv20210807_transformer-big_2022-07-28.zip
Resources for more information:
- OPUS-MT-train GitHub Repo
- More information about released models for this language pair: OPUS-MT itc-tur README
- More information about MarianNMT models in the transformers library
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/

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) and Bender et al. (2021)).

How to Get Started With the Model

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ""Di che nazionalità sono le tue dottoresse?" "Malese."",
    ""Di che nazionalità sono i nostri amici?" "Maltese.""
]

model_name = "pytorch-models/opus-mt-tc-big-itc-tr"
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:
#     "Doktorların hangi milletten?" "Malezyalı."
#     "Arkadaşlarımız hangi milletten?" "Maltalı."

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

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-itc-tr")
print(pipe(""Di che nazionalità sono le tue dottoresse?" "Malese.""))

# expected output: "Doktorların hangi milletten?" "Malezyalı."

Training

Data: opusTCv20210807 (source)
Pre-processing: SentencePiece (spm32k,spm32k)
Model Type: transformer-big
Original MarianNMT Model: opusTCv20210807_transformer-big_2022-07-28.zip
Training Scripts: GitHub Repo

Evaluation

test set translations: opusTCv20210807_transformer-big_2022-07-28.test.txt
test set scores: opusTCv20210807_transformer-big_2022-07-28.eval.txt
benchmark results: benchmark_results.txt
benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
fra-tur	tatoeba-test-v2021-08-07	0.63006	34.8	2582	14307
ita-tur	tatoeba-test-v2021-08-07	0.59991	34.9	10000	75807
por-tur	tatoeba-test-v2021-08-07	0.67836	40.1	1794	9312
ron-tur	tatoeba-test-v2021-08-07	0.64031	35.5	2460	13788
spa-tur	tatoeba-test-v2021-08-07	0.71524	45.2	10615	56099
cat-tur	flores101-devtest	0.54892	21.7	1012	20253
fra-tur	flores101-devtest	0.55342	21.7	1012	20253
glg-tur	flores101-devtest	0.53936	20.6	1012	20253
ita-tur	flores101-devtest	0.52842	18.4	1012	20253
oci-tur	flores101-devtest	0.50618	17.6	1012	20253
por-tur	flores101-devtest	0.56396	23.5	1012	20253
ron-tur	flores101-devtest	0.55409	21.5	1012	20253
spa-tur	flores101-devtest	0.51066	16.5	1012	20253

Citation Information

Publications: OPUS-MT – Building open translation services for the World and The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT (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 as pilot project 2866, by the FoTran project, 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, 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, Finland.

Model conversion info

transformers version: 4.16.2
OPUS-MT git hash: 8b9f0b0
port time: Sat Aug 13 00:03:26 EEST 2022
port machine: LM0-400-22516.local

Helsinki-NLP
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opus-mt-tc-big-itc-tr