Instructions to use voidful/albert_chinese_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/albert_chinese_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="voidful/albert_chinese_base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("voidful/albert_chinese_base") model = AutoModelForMaskedLM.from_pretrained("voidful/albert_chinese_base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b56e83305d04f39563f779cb5bca6496b67497c3f99328c1aba6f2d0f646d880
- Size of remote file:
- 42.7 MB
- SHA256:
- 47d730bc123fb45b5516dd5e62799e58f89847260eaea2a5ba939011ab88d9bd
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