opus mt zh en

ProviderHelsinki-NLP
Categorytranslation
Licensecc-by-4.0
Downloads3.3K
Stars5

Overview

The opus-mt-zh-en model is a specialized neural machine translation (NMT) tool designed specifically for Chinese-to-English translation. Unlike general-purpose LLMs, this model is optimized for translation efficiency and accuracy, making it an ideal choice for developers who need a lightweight, dedicated translation layer without the latency or cost of a massive generative model. It is particularly effective for integrating automated translation into pipelines, processing large datasets, or building real-time translation features into applications. Because it operates under the CC-BY-4.0 license, it offers significant flexibility for commercial deployment and modification. For developers, this means a predictable, focused performance profile that excels at structural linguistic mapping between these two specific languages.

Highlights

  • Specialized neural machine translation for Chinese to English
  • Lightweight architecture ensures low latency and high throughput
  • Permissive CC-BY-4.0 license for flexible commercial integration
  • Ideal for dedicated translation pipelines and data preprocessing

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download Helsinki-NLP/opus-mt-zh-en

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download Helsinki-NLP/opus-mt-zh-en config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Helsinki-NLP/opus-mt-zh-en')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/Helsinki-NLP/opus-mt-zh-en

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Helsinki-NLP/opus-mt-zh-en

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('Helsinki-NLP/opus-mt-zh-en')
tokenizer = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-zh-en')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model Helsinki-NLP/opus-mt-zh-en

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model Helsinki-NLP/opus-mt-zh-en README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('Helsinki-NLP/opus-mt-zh-en')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/Helsinki-NLP/opus-mt-zh-en.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/Helsinki-NLP/opus-mt-zh-en.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'Helsinki-NLP/opus-mt-zh-en')

Full Documentation

来源: HuggingFace

---
language:

  • zh

  • en

tags:

  • translation

license: cc-by-4.0
---

zho-eng

Table of Contents

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


Evaluation

#### Results

  • 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")

View official source
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