Fill-Mask
Transformers
PyTorch
JAX
Chinese
roberta
chinese
classical chinese
literary chinese
ancient chinese
bert
Instructions to use ethanyt/guwenbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ethanyt/guwenbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ethanyt/guwenbert-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ethanyt/guwenbert-base") model = AutoModelForMaskedLM.from_pretrained("ethanyt/guwenbert-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ethanyt/guwenbert-base: direct link, hf CLI and curl.
- Browser
- Download file 418 MB
-
https://huggingface.co/ethanyt/guwenbert-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ethanyt/guwenbert-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ethanyt/guwenbert-base/resolve/main/pytorch_model.bin
418 MB
- Xet hash:
- 877e5527fdc67e4bb8208a6b8ba0cbee5610791d0ecefff3d85acb584dae35e1
- Size of remote file:
- 418 MB
- SHA256:
- 6bcf18905048c2de864f21e57a248ba80326aa232965c16a7375ab0fb4d59c5e
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