Instructions to use silsever/opus-mt-align-en-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use silsever/opus-mt-align-en-de with Transformers:
# 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="silsever/opus-mt-align-en-de")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("silsever/opus-mt-align-en-de") model = AutoModelForSeq2SeqLM.from_pretrained("silsever/opus-mt-align-en-de") - Notebooks
- Google Colab
- Kaggle
Opus Tatoeba English-German
*This model was obtained by running the script convert_marian_to_pytorch.py - Instruction available here. The original models were trained by Jörg Tiedemann using the MarianNMT library. See all available MarianMTModel models on the profile of the Helsinki NLP group.
This is the conversion of checkpoint opus+bt-2021-04-13.zip *
eng-deu
source language name: English
target language name: German
OPUS readme: README.md
model: transformer-align
source language code: en
target language code: de
dataset: opus+bt
release date: 2021-02-22
pre-processing: normalization + SentencePiece (spm32k,spm32k)
download original weights: opus+bt-2021-04-13.zip
Test set translations data: opus+bt-2021-04-13.test.txt
test set scores file: opus+bt-2021-04-13.eval.txt
Benchmarks
Test set BLEU chr-F newssyscomb2009.eng-deu 22.8 0.538 news-test2008.eng-deu 23.7 0.533 newstest2009.eng-deu 22.6 0.532 newstest2010.eng-deu 25.5 0.552 newstest2011.eng-deu 22.6 0.527 newstest2012.eng-deu 23.4 0.530 newstest2013.eng-deu 27.1 0.556 newstest2014-deen.eng-deu 29.6 0.599 newstest2015-ende.eng-deu 31.6 0.600 newstest2016-ende.eng-deu 37.2 0.644 newstest2017-ende.eng-deu 30.6 0.595 newstest2018-ende.eng-deu 45.6 0.696 newstest2019-ende.eng-deu 41.3 0.659 Tatoeba-test.eng-deu 45.7 0.654
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Evaluation results
- BLEU on Tatoeba-test.eng-deuself-reported45.700