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")# 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
- Xet hash:
- 8cd9faa49da11b55eb154d17ac4d66ff42b24da905c53d57d3cc2b0162dda740
- Size of remote file:
- 1.32 GB
- SHA256:
- 19a81df8f40bc559a449fc9abd891565591791532c93d4fc37705e04afca0eb8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.