opus mt nl en

ProviderHelsinki-NLP
Categorytranslation
Licenseapache-2.0
Downloads117
Stars0

Overview

The opus-mt-nl-en model is a specialized neural machine translation (NMT) tool designed specifically for Dutch-to-English translation. Built on the Marian NMT framework by Helsinki-NLP, it offers a lightweight, efficient alternative to massive LLMs for dedicated translation pipelines. Developers can integrate it easily via the Hugging Face Transformers library, making it ideal for preprocessing multilingual datasets, automating localization workflows, or building real-time translation plugins. Unlike general-purpose models, it is optimized for translation accuracy and low latency, providing a predictable, deterministic output without the overhead of prompt engineering or high token costs.

Highlights

  • Dedicated Dutch to English neural machine translation
  • Lightweight architecture ensures low-latency inference
  • Seamless integration via Hugging Face Transformers
  • Apache-2.0 license for flexible commercial deployment
  • Efficient alternative to large general-purpose LLMs

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

Git Download

Make sure git-lfs is installed first

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

To skip LFS large-file downloads, use:

Skip LFS
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Helsinki-NLP/opus-mt-nl-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-nl-en')
tokenizer = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-nl-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-nl-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-nl-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-nl-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-nl-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-nl-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-nl-en')

Full Documentation

来源: HuggingFace

---
tags:

  • translation

license: apache-2.0
---

opus-mt-nl-en

  • source languages: nl
  • target languages: en
  • dataset: opus
  • model: transformer-align
  • pre-processing: normalization + SentencePiece

Benchmarks

| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.nl.en | 60.9 | 0.749 |

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