Instructions to use Language-Media-Lab/mt5-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/mt5-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/mt5-small-jpn-ain-mt")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Language-Media-Lab/mt5-small-jpn-ain-mt") model = AutoModelForSeq2SeqLM.from_pretrained("Language-Media-Lab/mt5-small-jpn-ain-mt") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b914b70f3473febf081591941ab985374f61435923aebf428e30bf000bb709a
- Size of remote file:
- 1.2 GB
- SHA256:
- 349d3c71c35957727d16455b21ae117e30d6522bfa88ee5d56321806981436dd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.