Instructions to use Helsinki-NLP/opus-mt-en-roa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-en-roa 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-roa")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-roa") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-roa", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9cb76a3aae8ec04278ad689201d3eee90811960929816319a31516a8900e0f8
- Size of remote file:
- 578 MB
- SHA256:
- c4523c143f9d71cfb3428217ecba17644ecf8442d5bafa94b5eb2e7f92b7c777
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.