Instructions to use hfl/chinese-lert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/chinese-lert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/chinese-lert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/chinese-lert-base") model = AutoModelForMaskedLM.from_pretrained("hfl/chinese-lert-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hfl/chinese-lert-base: direct link, hf CLI and curl.
- Browser
- Download file 412 MB
-
https://huggingface.co/hfl/chinese-lert-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hfl/chinese-lert-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hfl/chinese-lert-base/resolve/main/pytorch_model.bin
412 MB
- Xet hash:
- e71789ee6cb8fedf94abcadaa4b35b0200d3d12587f14ca4745d2ada2e8955c4
- Size of remote file:
- 412 MB
- SHA256:
- b4ef03ee5a6ea58f973a2fe8fce42ba5d1cb006e5646c5cb32d421e9934c242d
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