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")# pip install -U transformers accelerate # 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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google-bert/bert-base-multilingual-cased: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/google-bert/bert-base-multilingual-cased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google-bert/bert-base-multilingual-cased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google-bert/bert-base-multilingual-cased/resolve/main/flax_model.msgpack
712 MB
- Xet hash:
- bcbe6d46b2899c8f0970267ca42e7114e4742c6a635419b329b83ad6fb05a254
- Size of remote file:
- 712 MB
- SHA256:
- ebee9a143c50f975a7d5074642150011e112a65d8362dd6b6b84867ee689f2ad
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