Instructions to use voidful/albert_chinese_tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/albert_chinese_tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="voidful/albert_chinese_tiny")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("voidful/albert_chinese_tiny") model = AutoModelForMaskedLM.from_pretrained("voidful/albert_chinese_tiny") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d90192e29f5132d2a38fd4aeabb2466dc7f0ee7318e19d402bb13ad50e9859e
- Size of remote file:
- 16.6 MB
- SHA256:
- e0cc8db2ec94f43ffb94078291314465cef9330dfb131d3f8ba8d84849c7607c
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