Instructions to use Helsinki-NLP/opus-mt-sv-sn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-sv-sn 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-sv-sn")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-sv-sn") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-sv-sn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 646daafe579402331dded7b08d8cef6610c0ff7e79a9eb2fa6c0cbf8ad452ec5
- Size of remote file:
- 305 MB
- SHA256:
- 4f786be80c7beca1b400cb166b74fcf83cda2ed7791eae73cf0027a4bcd83568
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.