Instructions to use umanlp/mt5-mlm-16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use umanlp/mt5-mlm-16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="umanlp/mt5-mlm-16")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("umanlp/mt5-mlm-16") model = AutoModel.from_pretrained("umanlp/mt5-mlm-16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5abb6840804ade0beca66904df710a2e4ec313832646b1771c4fe840ed07b3da
- Size of remote file:
- 1.56 GB
- SHA256:
- 22f8a0a6e65d4deba692eadecb86138c35e5443e49b84984920feff55bd2ddb6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.