Instructions to use HUBioDataLab/SELFormer-Lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HUBioDataLab/SELFormer-Lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HUBioDataLab/SELFormer-Lite")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HUBioDataLab/SELFormer-Lite") model = AutoModelForMaskedLM.from_pretrained("HUBioDataLab/SELFormer-Lite") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85d86bb0e68905c05e79c51f442a532b9350cb940865654d0c7f13c5125c19b0
- Size of remote file:
- 233 MB
- SHA256:
- bec8f5b146d60ad6bff25b132fb4e66dbd56cb03b61bd09b6fb303ffdc0ec709
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.