# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-cus")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-cus", device_map="auto")eng-cus
source group: English
target group: Cushitic languages
OPUS readme: eng-cus
model: transformer
source language(s): eng
target language(s): som
model: transformer
pre-processing: normalization + SentencePiece (spm12k,spm12k)
download original weights: opus2m-2020-08-01.zip
test set translations: opus2m-2020-08-01.test.txt
test set scores: opus2m-2020-08-01.eval.txt
Benchmarks
| testset | BLEU | chr-F |
|---|---|---|
| Tatoeba-test.eng.multi | 16.0 | 0.173 |
| Tatoeba-test.eng-som.eng.som | 16.0 | 0.173 |
System Info:
hf_name: eng-cus
source_languages: eng
target_languages: cus
opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cus/README.md
original_repo: Tatoeba-Challenge
tags: ['translation']
languages: ['en', 'so', 'cus']
src_constituents: {'eng'}
tgt_constituents: {'som'}
src_multilingual: False
tgt_multilingual: True
prepro: normalization + SentencePiece (spm12k,spm12k)
url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cus/opus2m-2020-08-01.zip
url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cus/opus2m-2020-08-01.test.txt
src_alpha3: eng
tgt_alpha3: cus
short_pair: en-cus
chrF2_score: 0.17300000000000001
bleu: 16.0
brevity_penalty: 1.0
ref_len: 3.0
src_name: English
tgt_name: Cushitic languages
train_date: 2020-08-01
src_alpha2: en
tgt_alpha2: cus
prefer_old: False
long_pair: eng-cus
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-en-cus")