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 .gitattributes from ethanyt/guwenbert-base: direct link, hf CLI and curl.
- Browser
- Download file 391 Bytes
-
https://huggingface.co/ethanyt/guwenbert-base/resolve/main/.gitattributes
- Command line
-
hf download hf://ethanyt/guwenbert-base/.gitattributes
-
curl -L -o .gitattributes https://huggingface.co/ethanyt/guwenbert-base/resolve/main/.gitattributes
391 Bytes
| *.bin.* filter=lfs diff=lfs merge=lfs -text | |
| *.lfs.* filter=lfs diff=lfs merge=lfs -text | |
| *.bin filter=lfs diff=lfs merge=lfs -text | |
| *.h5 filter=lfs diff=lfs merge=lfs -text | |
| *.tflite filter=lfs diff=lfs merge=lfs -text | |
| *.tar.gz filter=lfs diff=lfs merge=lfs -text | |
| *.ot filter=lfs diff=lfs merge=lfs -text | |
| *.onnx filter=lfs diff=lfs merge=lfs -text | |
| *.msgpack filter=lfs diff=lfs merge=lfs -text | |