Instructions to use WindyTranslate/translate-ja-da with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-ja-da 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-ja-da")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-ja-da") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-ja-da", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download source.spm from WindyTranslate/translate-ja-da: direct link, hf CLI and curl.
- Browser
- Download file 854 kB
-
https://huggingface.co/WindyTranslate/translate-ja-da/resolve/main/source.spm
- Command line
-
hf download hf://WindyTranslate/translate-ja-da/source.spm
-
curl -L -o source.spm https://huggingface.co/WindyTranslate/translate-ja-da/resolve/main/source.spm
854 kB
- Xet hash:
- c33510523fed8af3b241c54d9f2683ccbd066734edc870fd275356f2cd258d0e
- Size of remote file:
- 854 kB
- SHA256:
- 72003f2d5fafbf93b47c40aa7706b7f77d5d7ee184568db987418e1fb4620377
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