102
stars
33
commits
9
repos using this model
4
linked in READMEs
Aug 16, 2023
updated
Model Description:
This model can be used for translation and text-to-text generation.
CONTENT WARNING: Readers should be aware this section contains 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)).
Further details about the dataset for this model can be found in the OPUS readme: ru-en
Pre-processing: Normalization + SentencePiece
Dataset: opus
Download original weights: opus-2020-02-26.zip
Test set translations: opus-2020-02-26.test.txt
| testset | BLEU | chr-F |
|---|---|---|
| newstest2012.ru.en | 34.8 | 0.603 |
| newstest2013.ru.en | 27.9 | 0.545 |
| newstest2014-ruen.ru.en | 31.9 | 0.591 |
| newstest2015-enru.ru.en | 30.4 | 0.568 |
| newstest2016-enru.ru.en | 30.1 | 0.565 |
| newstest2017-enru.ru.en | 33.4 | 0.593 |
| newstest2018-enru.ru.en | 29.6 | 0.565 |
| newstest2019-ruen.ru.en | 31.4 | 0.576 |
| Tatoeba.ru.en | 61.1 | 0.736 |
@InProceedings{TiedemannThottingal:EAMT2020,
author = {J{\"o}rg Tiedemann and Santhosh Thottingal},
title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld},
booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)},
year = {2020},
address = {Lisbon, Portugal}
}
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ru-en")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-ru-en")
102
stars
33
commits
9
repos using this model
4
linked in READMEs
Aug 16, 2023
updated
Model Description:
This model can be used for translation and text-to-text generation.
CONTENT WARNING: Readers should be aware this section contains 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)).
Further details about the dataset for this model can be found in the OPUS readme: ru-en
Pre-processing: Normalization + SentencePiece
Dataset: opus
Download original weights: opus-2020-02-26.zip
Test set translations: opus-2020-02-26.test.txt
| testset | BLEU | chr-F |
|---|---|---|
| newstest2012.ru.en | 34.8 | 0.603 |
| newstest2013.ru.en | 27.9 | 0.545 |
| newstest2014-ruen.ru.en | 31.9 | 0.591 |
| newstest2015-enru.ru.en | 30.4 | 0.568 |
| newstest2016-enru.ru.en | 30.1 | 0.565 |
| newstest2017-enru.ru.en | 33.4 | 0.593 |
| newstest2018-enru.ru.en | 29.6 | 0.565 |
| newstest2019-ruen.ru.en | 31.4 | 0.576 |
| Tatoeba.ru.en | 61.1 | 0.736 |
@InProceedings{TiedemannThottingal:EAMT2020,
author = {J{\"o}rg Tiedemann and Santhosh Thottingal},
title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld},
booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)},
year = {2020},
address = {Lisbon, Portugal}
}
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ru-en")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-ru-en")