Instructions to use google-bert/bert-base-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-base-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google-bert/bert-base-cased")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased") model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-cased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google-bert/bert-base-cased: direct link, hf CLI and curl.
- Browser
- Download file 525 MB
-
https://huggingface.co/google-bert/bert-base-cased/resolve/ae1d3b2cce5ef798cab884c0e7e61e34f46bc412/flax_model.msgpack
- Command line
-
hf download hf://google-bert/bert-base-cased@ae1d3b2cce5ef798cab884c0e7e61e34f46bc412/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google-bert/bert-base-cased/resolve/ae1d3b2cce5ef798cab884c0e7e61e34f46bc412/flax_model.msgpack
525 MB
- Xet hash:
- 927cbb2656dbd2af8726dd6d212670c2c21ffe92f5399f252fd223bc4b816fba
- Size of remote file:
- 525 MB
- SHA256:
- a24546c218a84d83427f7c5a68fcbe80add3a752e64715394aa8de77e7874e1f
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