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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e12f8a202bb6ea2218590452c33a50c4378bf636b1c4328e86ced4572cfb271
- Size of remote file:
- 436 MB
- SHA256:
- c3a88698b53a311c061915c32dde9154ea0fc823bda686ca0a1a5d04181900a8
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