Instructions to use google-bert/bert-base-multilingual-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-base-multilingual-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google-bert/bert-base-multilingual-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-multilingual-cased") model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-multilingual-cased") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c12cc0d57b1d68420141a68be8ef7983c71556750e9d6098b15387d9354b4d20
- Size of remote file:
- 714 MB
- SHA256:
- 3496a508a9a3511c8a55e4d0e6f471c70c68c2a8c4784b3b2b5dc16ffb87d238
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.