Instructions to use voidful/albert_chinese_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/albert_chinese_small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="voidful/albert_chinese_small")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("voidful/albert_chinese_small") model = AutoModelForMaskedLM.from_pretrained("voidful/albert_chinese_small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ef8e1433c93c134c81db42075a6abdc9653b0ff125506336c4dcb11a34d2272a
- Size of remote file:
- 19.3 MB
- SHA256:
- a480185be5bf98c486b2156fe9b5b5b221b1014b8c29e0cbf2fb1f3cb09e12ee
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