Instructions to use Helsinki-NLP/opus-mt-en-cpf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-en-cpf 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-en-cpf")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-cpf") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-cpf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Helsinki-NLP/opus-mt-en-cpf: direct link, hf CLI and curl.
- Browser
- Download file 44 Bytes
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https://huggingface.co/Helsinki-NLP/opus-mt-en-cpf/resolve/main/tokenizer_config.json
- Command line
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hf download hf://Helsinki-NLP/opus-mt-en-cpf/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/Helsinki-NLP/opus-mt-en-cpf/resolve/main/tokenizer_config.json
44 Bytes
| {"target_lang": "cpf", "source_lang": "eng"} |