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:
- b102dbdd312be2493011faba62e682446b3fa31f6de7802a26aa9cc6b2b9ced2
- Size of remote file:
- 2.99 kB
- SHA256:
- 7426dceb7db538925a2098444fab5520bc9e5502f5b768b92185a3cd5c707821
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