Instructions to use speydach/layoutlmv2-finetuned-cord with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use speydach/layoutlmv2-finetuned-cord with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="speydach/layoutlmv2-finetuned-cord")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("speydach/layoutlmv2-finetuned-cord") model = AutoModelForTokenClassification.from_pretrained("speydach/layoutlmv2-finetuned-cord", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66fd19d4511a09b09acaa4abd6e7d18c019a173ff6ee1aa2eddc533572bd1b59
- Size of remote file:
- 802 MB
- SHA256:
- 21f592115d353293389434a788bf623b7179ed0dffa416b73b9270dcaad1e5f2
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