Instructions to use google-bert/bert-large-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-large-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google-bert/bert-large-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-large-uncased") model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-large-uncased") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee2a0b350e6c8cb56a6acbe02afb190e67f0a7df9ade4c6dd9be04fd0eb25ecb
- Size of remote file:
- 1.25 GB
- SHA256:
- 9169b12daff1cc053a8b7ae4058c39eb3a2c15e5d8be160cfa8267aee332b6ff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.