Instructions to use shibing624/bert4ner-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shibing624/bert4ner-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="shibing624/bert4ner-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("shibing624/bert4ner-base-uncased") model = AutoModelForTokenClassification.from_pretrained("shibing624/bert4ner-base-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 786d19da2bb910b43d07d823e8de06dac97ab11776042529cc72593528c1ba72
- Size of remote file:
- 436 MB
- SHA256:
- a09026a2fb9c444e2a58ac531a61c6979e3406646234930f4dc62cfcd890622b
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