Instructions to use google-bert/bert-base-multilingual-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-base-multilingual-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google-bert/bert-base-multilingual-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-multilingual-uncased") model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-multilingual-uncased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google-bert/bert-base-multilingual-uncased: direct link, hf CLI and curl.
- Browser
- Download file 670 MB
-
https://huggingface.co/google-bert/bert-base-multilingual-uncased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google-bert/bert-base-multilingual-uncased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google-bert/bert-base-multilingual-uncased/resolve/main/flax_model.msgpack
670 MB
- Xet hash:
- 063b7da66d0a807702379f6501bc98d8545b5067eedd9475a50f5d56dbee9ab4
- Size of remote file:
- 670 MB
- SHA256:
- f710cb055884fa24672430f027b5375f0651dda1d9011f7d2407bc58e9682237
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