Fill-Mask
Transformers
PyTorch
JAX
Chinese
roberta
chinese
classical chinese
literary chinese
ancient chinese
bert
Instructions to use ethanyt/guwenbert-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ethanyt/guwenbert-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ethanyt/guwenbert-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ethanyt/guwenbert-large") model = AutoModelForMaskedLM.from_pretrained("ethanyt/guwenbert-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from ethanyt/guwenbert-large: direct link, hf CLI and curl.
- Browser
- Download file 1.31 GB
-
https://huggingface.co/ethanyt/guwenbert-large/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ethanyt/guwenbert-large/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ethanyt/guwenbert-large/resolve/main/flax_model.msgpack
1.31 GB
- Xet hash:
- 8b955385b70e1b0b1a203c796a12117c2974798b60f4b5853189fb25cf8d4eb5
- Size of remote file:
- 1.31 GB
- SHA256:
- 10c877b231ad82e4ab0e614ebea935057ecf0b79ff90b83294820e5100c4cdd4
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