Instructions to use google-bert/bert-base-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google-bert/bert-base-chinese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="google-bert/bert-base-chinese")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-chinese") model = AutoModelForMaskedLM.from_pretrained("google-bert/bert-base-chinese", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google-bert/bert-base-chinese: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/google-bert/bert-base-chinese/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google-bert/bert-base-chinese/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google-bert/bert-base-chinese/resolve/main/flax_model.msgpack
409 MB
- Xet hash:
- 61251e6179fd3cf4dbab805e8ed4f1c2ffdb0fce18466d751fb6a21943ea7623
- Size of remote file:
- 409 MB
- SHA256:
- 76df8425215fb9ede22e0393e356f82a99d84e79f078cd141afbbf9277460c8e
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