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:
- 9fe2dae77f0dcae5722348e890ba7d318e57b70e651dc9d553fc11e786375b2e
- Size of remote file:
- 1.08 GB
- SHA256:
- 8c6fe40eebcaffac5051e6dddc93318faedfc74ed17b0de0c2512a3158f77ce5
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