Instructions to use ugiugi/distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ugiugi/distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ugiugi/distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ugiugi/distilbert") model = AutoModelForMaskedLM.from_pretrained("ugiugi/distilbert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24b6bf37696aec1a39a2194676c923dc8fe8f697fce5c91f83bd221f752c8eb0
- Size of remote file:
- 268 MB
- SHA256:
- 5f0da028517179ea035b0feaaeac953a141cfabc09f30ced03cc06b462f33c5f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.