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Carlonli/
opus-mt-zh-en

Name: opus-mt-zh-en
Availability: InStock

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zho-eng

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

bibtex

@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

python

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")

Carlonli/opus-mt-zh-en