Instructions to use WindyTranslate/translate-sv-et with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-sv-et 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="WindyTranslate/translate-sv-et")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-sv-et") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-sv-et", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Promote herm0 build from WindstormLabs/translate-sv-et, with attribution and change statement
830d3df verified - Xet hash:
- c23edc6e1953dd2c0661e6d21ceb957f7b7ed8fd2990a2db09eb71fb4397de3d
- Size of remote file:
- 818 kB
- SHA256:
- ae6f04e275a6a5a0735c3bb331b65088087f7028e2c385eaedce0d06c62d0c08
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