opus mt en ru
Overview
Highlights
- Optimized for high-speed English to Russian translation
- Permissive Apache-2.0 license for commercial use
- Low resource overhead for local edge deployment
- Easy integration via Hugging Face Transformers library
Usage
# Install Hugging Face transformers
pip install transformers torch
# Load model with transformers
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("Helsinki-NLP/opus-mt-en-ru")
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-ru")
Hugging Face Download
We recommend downloading the model via the Hugging Face CLI or Hub SDK.
Guidance:Before downloading, install huggingface_hub with:
pip install -U huggingface_hub
CLI Download
Download the full repository
huggingface-cli download Helsinki-NLP/opus-mt-en-ru
Download a single file to a local folder (e.g. config.json into ./dir)
huggingface-cli download Helsinki-NLP/opus-mt-en-ru config.json --local-dir ./dir
See the official docs for more CLI options
SDK Download
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Helsinki-NLP/opus-mt-en-ru')
Git Download
Make sure git-lfs is installed first
git lfs install
git clone https://huggingface.co/Helsinki-NLP/opus-mt-en-ru
To skip LFS large-file downloads, use:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Helsinki-NLP/opus-mt-en-ru
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
pip install -U transformers torch
Load the model and run inference
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('Helsinki-NLP/opus-mt-en-ru')
tokenizer = AutoTokenizer.from_pretrained('Helsinki-NLP/opus-mt-en-ru')
Model Download
We recommend downloading the model via the ModelScope CLI or SDK.
Guidance:Before downloading, install ModelScope with:
pip install modelscope
CLI Download
Download the full repository
modelscope download --model Helsinki-NLP/opus-mt-en-ru
Download a single file to a local folder (e.g. README.md into ./dir)
modelscope download --model Helsinki-NLP/opus-mt-en-ru README.md --local_dir ./dir
See the docs for more CLI options
SDK Download
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('Helsinki-NLP/opus-mt-en-ru')
Git Download
Make sure git-lfs is installed first
git lfs install
git clone https://www.modelscope.cn/Helsinki-NLP/opus-mt-en-ru.git
To skip LFS large-file downloads, use:
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/Helsinki-NLP/opus-mt-en-ru.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
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
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
p = pipeline('text-generation', 'Helsinki-NLP/opus-mt-en-ru')
Full Documentation
---
tags:
- translation
license: apache-2.0
---
opus-mt-en-ru
- source languages: en
- target languages: ru
- OPUS readme: en-ru
- dataset: opus
- model: transformer-align
- pre-processing: normalization + SentencePiece
- download original weights: opus-2020-02-11.zip
- test set translations: opus-2020-02-11.test.txt
- test set scores: opus-2020-02-11.eval.txt
Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newstest2012.en.ru | 31.1 | 0.581 |
| newstest2013.en.ru | 23.5 | 0.513 |
| newstest2015-enru.en.ru | 27.5 | 0.564 |
| newstest2016-enru.en.ru | 26.4 | 0.548 |
| newstest2017-enru.en.ru | 29.1 | 0.572 |
| newstest2018-enru.en.ru | 25.4 | 0.554 |
| newstest2019-enru.en.ru | 27.1 | 0.533 |
| Tatoeba.en.ru | 48.4 | 0.669 |