opus mt en de

ProviderHelsinki-NLP
Categorytranslation
Licensecc-by-4.0
Downloads192
Stars0

Overview

The opus-mt-en-de model is a specialized neural machine translation (NMT) tool developed by Helsinki-NLP, designed specifically for high-accuracy translation from English to German. Unlike general-purpose LLMs, this model is purpose-built for translation tasks, offering lower latency and predictable output for production pipelines. It is particularly effective for developers building localization tools, automated documentation workflows, or real-time translation interfaces. Because it is released under the CC-BY-4.0 license, it provides significant flexibility for commercial integration without the heavy overhead of massive parameter counts, making it ideal for deployment on edge devices or lightweight server environments where efficiency is prioritized over general conversational ability.

Highlights

  • Dedicated English-to-German neural machine translation
  • Lightweight architecture for low-latency production deployments
  • Permissive CC-BY-4.0 license for commercial use
  • Optimized for structured text and localization workflows

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

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-en-de

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-en-de 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-en-de')

Git Download

Make sure git-lfs is installed first

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

To skip LFS large-file downloads, use:

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

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

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-en-de

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-en-de 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-en-de')

Git Download

Make sure git-lfs is installed first

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

Full Documentation

来源: HuggingFace

---
tags:

  • translation

license: cc-by-4.0
---

opus-mt-en-de

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: 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

Evaluation

#### Results

#### 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

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-en-de")


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

View official source
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