Instructions to use ckiplab/bert-base-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ckiplab/bert-base-chinese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ckiplab/bert-base-chinese")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ckiplab/bert-base-chinese") model = AutoModelForMaskedLM.from_pretrained("ckiplab/bert-base-chinese", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from ckiplab/bert-base-chinese: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/ckiplab/bert-base-chinese/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ckiplab/bert-base-chinese/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ckiplab/bert-base-chinese/resolve/main/flax_model.msgpack
409 MB
- Xet hash:
- 330c9b7dc3596501bb46733a1196ce2c7f08b22d87a16964fabe4a33e26dda16
- Size of remote file:
- 409 MB
- SHA256:
- e3b3ed6f5c2a932452623a170ff9c6b3d419ea0f7d530eaf445a96984fa77d92
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