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:
- 238580160af07a847bdcc9b004d85087547a2fcd5f303bf2f3507032f97f572d
- Size of remote file:
- 3.9 kB
- SHA256:
- 1451c7f82064bd0b63f2426df129324e85a3150d653c4a61258acf0348cf3ef3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.