Instructions to use Helsinki-NLP/opus-mt-de-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-de-en 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="Helsinki-NLP/opus-mt-de-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-de-en") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-de-en") - Inference
- Notebooks
- Google Colab
- Kaggle
Adding '\n' to this model (using CTranslate2)
#7
by Geremia23 - opened
How do I add special tokens (like \n) to this model?
tokenizer.add_tokens('\n') seems to work, but CTranslate2 drops the \n when translating:
import ctranslate2
import transformers
translator = ctranslate2.Translator("opus-mt-de-en", device='cuda')
tokenizer = transformers.AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-de-en")
# add special token
tokenizer.add_tokens('\n') # output: 1
tokenizer.added_tokens_decoder # output: {58101: '\n'}
tokenizer.added_tokens_encoder # output: {'\n': 58101}
source = tokenizer.convert_ids_to_tokens(tokenizer.encode("Guten\ntag!")) # == ['▁Guten', '\n', '▁', 'tag', '!', '</s>']
results = translator.translate_batch([source], beam_size=5) # == [TranslationResult(hypotheses=[['▁Good', '▁day', '!']], scores=[], attention=[])] ← NOTICE THE `\n` IS DROPPED!
How do I get CTranslate2 to map token ID #58101 to \n?
Geremia23 changed discussion title from Add '\n' to this model? to Adding '\n' to this model (using CTranslate2)