Instructions to use andi611/bert-base-cased-ner-conll2003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andi611/bert-base-cased-ner-conll2003 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="andi611/bert-base-cased-ner-conll2003")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("andi611/bert-base-cased-ner-conll2003") model = AutoModelForTokenClassification.from_pretrained("andi611/bert-base-cased-ner-conll2003", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48be91ea314101e4b5c8222002a289be1d919fef3c3a1f2d5577db09477f368e
- Size of remote file:
- 431 MB
- SHA256:
- a135457e0380618ee9d4222e05d6857724dd6eae0f6cc9c15dcfa6ac7edd8564
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