Opus-mt-zh-en Open Source Machine Translation Model - Achieve Accurate Chinese-to-English Translation for Free

Opus Mt Zh En

Developed by Helsinki-NLP

A Chinese-to-English machine translation model developed by the University of Helsinki, based on the OPUS corpus

Machine Translation Supports Multiple Languages#Chinese-English Translation #Open-source Model #High-precision Translation

Downloads 441.24k

Release Time : 3/2/2022

Model Overview

This model is specifically designed for Chinese-to-English text translation tasks, utilizing the Transformer architecture and trained on large-scale multilingual parallel corpora

Model Features

High-quality Translation

Achieves a score of 36.1 BLEU on the Tatoeba test set, demonstrating excellent performance

Open-source License

Adopts the CC-BY-4.0 license, allowing for both commercial and research use

Standardized Preprocessing

Uses SentencePiece tokenization (spm32k) for text preprocessing

Model Capabilities

Chinese-to-English text translation

Batch text processing

Use Cases

Language Services

Document Translation

Automatically translates Chinese documents into English

High-quality translation output that preserves the original semantics

Content Localization

Assists multinational companies in localizing Chinese content into English

Education & Research

Language Learning Assistance

Provides translation references for language learners

🚀 zho-eng Translation Model

This is a translation model developed by the Language Technology Research Group at the University of Helsinki. It can be used for Chinese-to-English translation and text - to - text generation.

🚀 Quick Start

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")

✨ Features

This model can be used for translation and text - to - text generation, specifically for Chinese-to-English translation tasks.

📦 Model Details

Property	Details
Developed by	Language Technology Research Group at the University of Helsinki
Model Type	Translation
Source Language	Chinese
Target Language	English
License	CC - BY - 4.0
Resources for more information	GitHub Repo

🔧 Risks, Limitations and Biases

⚠️ Important Note

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}
 }

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