Instructions to use KgModel/sbb_ticket_parser_LayoutLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KgModel/sbb_ticket_parser_LayoutLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KgModel/sbb_ticket_parser_LayoutLM")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KgModel/sbb_ticket_parser_LayoutLM") model = AutoModelForTokenClassification.from_pretrained("KgModel/sbb_ticket_parser_LayoutLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 22044a8af920623cf4055184b7304c924e2a096d9521926ae47eb3db58bcd3d6
- Size of remote file:
- 451 MB
- SHA256:
- ab7316fdee7114a6a04021ae11b7ceec494fef1f0f6f5f018de443979a3cb310
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