Instructions to use hfl/chinese-macbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/chinese-macbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/chinese-macbert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-macbert-base") model = AutoModelForMaskedLM.from_pretrained("hfl/chinese-macbert-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from hfl/chinese-macbert-base: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/hfl/chinese-macbert-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://hfl/chinese-macbert-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/hfl/chinese-macbert-base/resolve/main/flax_model.msgpack
409 MB
- Xet hash:
- 5bb6f6c0b7a14d39e630c220ed34d878e20bf5125c6e73c2ad2d73260b2cc7b6
- Size of remote file:
- 409 MB
- SHA256:
- f885a9d05b52893e65412b41633a7346abfe6c87bf7d946bb874ae8eacaf07e2
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