Instructions to use model-attribution-challenge/bert-base-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use model-attribution-challenge/bert-base-chinese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="model-attribution-challenge/bert-base-chinese")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("model-attribution-challenge/bert-base-chinese") model = AutoModelForMaskedLM.from_pretrained("model-attribution-challenge/bert-base-chinese") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d79a9a3fbd8f8898a03f5f3830b1401c89c19f11bcfb4da7888832b780d44aa
- Size of remote file:
- 412 MB
- SHA256:
- 8a693db616eaf647ed2bfe531e1fa446637358fc108a8bf04e8d4db17e837ee9
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