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") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edfa297452a93e00eb62ee948a81511d8f6faf36b9a625ede23c9ef3854c46f1
- Size of remote file:
- 1.34 GB
- SHA256:
- 6191f9ee19831eabcf5376d1eb3d0d0d7aa1589e0f91e4975fd596a76c949a03
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