Instructions to use junzai/bert_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junzai/bert_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="junzai/bert_test")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("junzai/bert_test") model = AutoModelForMaskedLM.from_pretrained("junzai/bert_test") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb84ca42768337038c4f94f32d0de1bd6ed312964a7feefec37b6faeafd80f85
- Size of remote file:
- 438 MB
- SHA256:
- b20bf918d4e3bb2a4d25284d24bdec80cd99c1665263e93d6b42d559946a3f91
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.