Instructions to use browndw/docusco-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use browndw/docusco-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="browndw/docusco-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("browndw/docusco-bert") model = AutoModelForTokenClassification.from_pretrained("browndw/docusco-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb481ac58cf4b287d8d68fd2cba6d243dae0d2463aaf3a0749bf0a5b6cda3bff
- Size of remote file:
- 431 MB
- SHA256:
- 2e9f4569902c7c1277b7c435cefa0d5684c55229c971e57d4731dc3ddf040305
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