Instructions to use hfl/chinese-macbert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/chinese-macbert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/chinese-macbert-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-macbert-large") model = AutoModelForMaskedLM.from_pretrained("hfl/chinese-macbert-large", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from hfl/chinese-macbert-large: direct link, hf CLI and curl.
- Browser
- Download file 1.3 GB
-
https://huggingface.co/hfl/chinese-macbert-large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://hfl/chinese-macbert-large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/hfl/chinese-macbert-large/resolve/main/flax_model.msgpack
1.3 GB
- Xet hash:
- 90ccd77242d7729890fa0430a5ccd9c353ffb227b44be043029a12795fe04435
- Size of remote file:
- 1.3 GB
- SHA256:
- 70360130cc6a273607fe22cf665dc45f4c1100078694c5c5d92e109d15104798
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