Instructions to use Helsinki-NLP/opus-mt-de-ca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-de-ca 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-ca")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-de-ca") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-de-ca", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a86fbb8b1cf82da4bc8e9076e5f1cd4fd03f2dc146ead80bec7f43af0287f782
- Size of remote file:
- 224 MB
- SHA256:
- ad9f6af17a9aab69318926b5db9aac500c2db0f34c7e58322c47d1701f377527
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.