Instructions to use BayanDuygu/bert-base-cased-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BayanDuygu/bert-base-cased-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BayanDuygu/bert-base-cased-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("BayanDuygu/bert-base-cased-ner") model = AutoModelForTokenClassification.from_pretrained("BayanDuygu/bert-base-cased-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb32414fc381415526bcae962de4f5a49e842996e87962ac8ac3d58c51cd082b
- Size of remote file:
- 431 MB
- SHA256:
- a45158f9a1779ca64941b48c6c17aa9e1e7a4bfd8492dd677b9ef198f415e91b
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