Instructions to use Helsinki-NLP/opus-mt-sla-sla with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-sla-sla 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-sla-sla")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-sla-sla") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-sla-sla", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2fbfc3611e2d610cce6e038b64c4edd7609c41c393d17b82f5ada30bff5baacc
- Size of remote file:
- 257 MB
- SHA256:
- 5836f6c2782fa2bfbbfac82682718b58727a6d105bce54b3d0f65434aee58f2c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.