Instructions to use WindyWord/translate-lua-sv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyWord/translate-lua-sv 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="WindyWord/translate-lua-sv")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("WindyWord/translate-lua-sv", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4254ae58bd6fb444223b8d86160e19e7245271978b2f61fbe75ba3f2975fe30
- Size of remote file:
- 854 kB
- SHA256:
- 30beda1431fe8eb72779d21c47117e7456e03aaa1c6e1c0fa034c02888ce8b31
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.