Instructions to use SNV/bert-ner-custom_custom_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SNV/bert-ner-custom_custom_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SNV/bert-ner-custom_custom_data")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("SNV/bert-ner-custom_custom_data") model = AutoModelForTokenClassification.from_pretrained("SNV/bert-ner-custom_custom_data") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b06e9cf827e60e28f2bc9b4dddc93554f2fde9579cff586d4487d507f2ee5ef0
- Size of remote file:
- 4.6 kB
- SHA256:
- 72ac50f8a78467ece9146356f09724393a4ec6a3a2829557d46807a63b171924
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