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 flax_model.msgpack from ethanyt/guwenbert-base: direct link, hf CLI and curl.
- Browser
- Download file 416 MB
-
https://huggingface.co/ethanyt/guwenbert-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ethanyt/guwenbert-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ethanyt/guwenbert-base/resolve/main/flax_model.msgpack
416 MB
- Xet hash:
- 3be50b475e46bbfca221f1ca609b97cfa1d59d68f5f14fd9aeae32eb752fccdd
- Size of remote file:
- 416 MB
- SHA256:
- 023a2f18f12b9a795846649a8f6afeb46cdd3e3bcec925f0bfc3f4f883c29b0d
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