Instructions to use shibing624/bertspan4ner-base-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shibing624/bertspan4ner-base-chinese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="shibing624/bertspan4ner-base-chinese")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("shibing624/bertspan4ner-base-chinese") model = AutoModelForTokenClassification.from_pretrained("shibing624/bertspan4ner-base-chinese") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91ffde0dbbde6b1e6018595797ad0c52f41d8b4ba4745bb539013b92d38126ba
- Size of remote file:
- 412 MB
- SHA256:
- 07a7c3dc0b2345028c4a56371069bb4496e08dc63daba1a1ca2f37f02cba751b
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