opus mt fr en

ProviderHelsinki-NLP
Categorytranslation
Licenseapache-2.0
Downloads648
Stars0

Overview

The Opus MT FR-EN model is a specialized translation engine developed by Helsinki-NLP, designed specifically for high-quality English-to-French and French-to-English bidirectional mapping. Unlike general-purpose LLMs, this model is architected for efficiency and low-latency translation tasks, making it an ideal choice for developers building localized applications or automated documentation pipelines. It operates under the permissive Apache-2.0 license, allowing for seamless commercial integration and self-hosting without restrictive licensing overhead. For developers, this means a predictable, lightweight deployment that avoids the token-cost volatility of larger API-based models while maintaining strong linguistic accuracy for these specific language pairs.

Highlights

  • Dedicated bidirectional French-English translation capabilities
  • Permissive Apache-2.0 license for commercial use
  • Low-latency performance compared to general LLMs
  • Optimized for self-hosting and local deployment
  • Developed by trusted Helsinki-NLP research team

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

Git Download

Make sure git-lfs is installed first

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

To skip LFS large-file downloads, use:

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

Full Documentation

来源: HuggingFace

---
tags:

  • translation

license: apache-2.0
---

opus-mt-fr-en

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

Benchmarks

| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newsdiscussdev2015-enfr.fr.en | 33.1 | 0.580 |
| newsdiscusstest2015-enfr.fr.en | 38.7 | 0.614 |
| newssyscomb2009.fr.en | 30.3 | 0.569 |
| news-test2008.fr.en | 26.2 | 0.542 |
| newstest2009.fr.en | 30.2 | 0.570 |
| newstest2010.fr.en | 32.2 | 0.590 |
| newstest2011.fr.en | 33.0 | 0.597 |
| newstest2012.fr.en | 32.8 | 0.591 |
| newstest2013.fr.en | 33.9 | 0.591 |
| newstest2014-fren.fr.en | 37.8 | 0.633 |
| Tatoeba.fr.en | 57.5 | 0.720 |

Join our Telegram