Instructions to use google-bert/bert-large-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-large-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google-bert/bert-large-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-large-cased") model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-large-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b45d5af5636f5f24b1ea164bd0a90af14a983ae0486fdfc7303ed65fc391fa6
- Size of remote file:
- 1.34 GB
- SHA256:
- 137eefff71696316817af618eb19f72e277daab3225b56584848c5723a47cc59
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.