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")# pip install -U transformers accelerate # 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 pytorch_model.bin from google-bert/bert-base-cased: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/google-bert/bert-base-cased/resolve/refs%2Fpr%2F4/pytorch_model.bin
- Command line
-
hf download hf://google-bert/bert-base-cased@refs/pr/4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/google-bert/bert-base-cased/resolve/refs%2Fpr%2F4/pytorch_model.bin
433 MB
- Xet hash:
- 605008cccd9c910b4a812aabce9758a2842e8b6c6381fcb727d90d2b4fd58825
- Size of remote file:
- 433 MB
- SHA256:
- 8b69d70fbbc5a67cc3db614c0a02c4c3fd9d85c7e0af080590d9ab0bfe331348
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