Instructions to use google-bert/bert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google-bert/bert-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased") model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 317d5cee2afa4179f3874fe340bda3ce0f94cd4c705873b67956b5b6acdf2113
- Size of remote file:
- 436 MB
- SHA256:
- d6992b8cd27d7a132eafce6a8210272329a371b1c762d453588795dd3835593e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.