# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ko-en")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-ko-en", device_map="auto")kor-eng
source group: Korean
target group: English
OPUS readme: kor-eng
model: transformer-align
source language(s): kor kor_Hang kor_Latn
target language(s): eng
model: transformer-align
pre-processing: normalization + SentencePiece (spm32k,spm32k)
download original weights: opus-2020-06-17.zip
test set translations: opus-2020-06-17.test.txt
test set scores: opus-2020-06-17.eval.txt
Benchmarks
| testset | BLEU | chr-F |
|---|---|---|
| Tatoeba-test.kor.eng | 41.3 | 0.588 |
System Info:
hf_name: kor-eng
source_languages: kor
target_languages: eng
opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kor-eng/README.md
original_repo: Tatoeba-Challenge
tags: ['translation']
languages: ['ko', 'en']
src_constituents: {'kor_Hani', 'kor_Hang', 'kor_Latn', 'kor'}
tgt_constituents: {'eng'}
src_multilingual: False
tgt_multilingual: False
prepro: normalization + SentencePiece (spm32k,spm32k)
url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/kor-eng/opus-2020-06-17.zip
url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/kor-eng/opus-2020-06-17.test.txt
src_alpha3: kor
tgt_alpha3: eng
short_pair: ko-en
chrF2_score: 0.588
bleu: 41.3
brevity_penalty: 0.9590000000000001
ref_len: 17711.0
src_name: Korean
tgt_name: English
train_date: 2020-06-17
src_alpha2: ko
tgt_alpha2: en
prefer_old: False
long_pair: kor-eng
helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
port_machine: brutasse
port_time: 2020-08-21-14:41
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# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")