Translation
Transformers
PyTorch
Japanese
Ainu (Japan)
t5
text2text-generation
text-generation-inference
Instructions to use Language-Media-Lab/byt5-small-jpn-ain-mt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Language-Media-Lab/byt5-small-jpn-ain-mt 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="Language-Media-Lab/byt5-small-jpn-ain-mt")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Language-Media-Lab/byt5-small-jpn-ain-mt") model = AutoModelForSeq2SeqLM.from_pretrained("Language-Media-Lab/byt5-small-jpn-ain-mt") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c1b5bc727f39680198715808a305fd46ae6b26461b31b3ab11caf5f12a6aa68
- Size of remote file:
- 1.2 GB
- SHA256:
- 984b01e30529132688d5db41a69859820f56c8322429d1a14139ff44a52bfb33
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.