Helsinki-NLP_opus-mt-zh-en

---

language:

• zh
• en

tags:

• translation

license: cc-by-4.0

zho-eng

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: Chinese
  • Target Language: English
• 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: zho-eng

Training

System Information

• helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
• transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
• port_machine: brutasse
• port_time: 2020-08-21-14:41
• src_multilingual: False
• tgt_multilingual: False

Training Data

Preprocessing

• pre-processing: normalization + SentencePiece (spm32k,spm32k)

• ref_len: 82826.0

• dataset: opus

• download original weights: opus-2020-07-17.zip

• test set translations: opus-2020-07-17.test.txt

Evaluation

Results

• test set scores: opus-2020-07-17.eval.txt

• brevity_penalty: 0.948

Benchmarks

testset	BLEU	chr-F
Tatoeba-test.zho.eng	36.1	0.548

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-zh-en")

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