Helsinki-NLP_opus-mt-en-de

---

tags:

• translation license: cc-by-4.0

---

opus-mt-en-de

Table of Contents

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

Model Details

Model Description:

• Developed by: Language Technology Research Group at the University of Helsinki
• Model Type: Translation
• Language(s):
  • Source Language: English
  • Target Language: German
• License: CC-BY-4.0
• Resources for more information:
  • GitHub Repo

Uses

Direct Use

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

Risks, Limitations and Biases

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: en-de

Training Data

Preprocessing

• pre-processing: normalization + SentencePiece

• dataset: opus

• download original weights: opus-2020-02-26.zip

• test set translations: opus-2020-02-26.test.txt

Evaluation

Results

• test set scores: opus-2020-02-26.eval.txt

Benchmarks

testset	BLEU	chr-F
newssyscomb2009.en.de	23.5	0.540
news-test2008.en.de	23.5	0.529
newstest2009.en.de	22.3	0.530
newstest2010.en.de	24.9	0.544
newstest2011.en.de	22.5	0.524
newstest2012.en.de	23.0	0.525
newstest2013.en.de	26.9	0.553
newstest2015-ende.en.de	31.1	0.594
newstest2016-ende.en.de	37.0	0.636
newstest2017-ende.en.de	29.9	0.586
newstest2018-ende.en.de	45.2	0.690
newstest2019-ende.en.de	40.9	0.654
Tatoeba.en.de	47.3	0.664

Citation Information

@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}
 }

How to Get Started With the Model

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")

model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-de")