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 pytorch_model.bin from google-bert/bert-base-multilingual-uncased: direct link, hf CLI and curl.
- Browser
- Download file 672 MB
-
https://huggingface.co/google-bert/bert-base-multilingual-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://google-bert/bert-base-multilingual-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google-bert/bert-base-multilingual-uncased/resolve/main/pytorch_model.bin
672 MB
- Xet hash:
- fa37727ee43e77f09fa4cb629b178bc678c33caa31513a0b598f8c14fdc8f644
- Size of remote file:
- 672 MB
- SHA256:
- 2fec0e2a13cde5fa386fa00ba3e1bfea14b5d8fd8760f37f051799812a320e8d
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