Instructions to use Sameed1/layoutlmv3-receipts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sameed1/layoutlmv3-receipts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Sameed1/layoutlmv3-receipts")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("Sameed1/layoutlmv3-receipts") model = AutoModelForTokenClassification.from_pretrained("Sameed1/layoutlmv3-receipts") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05ca494b6332be46bab3734189e49de9f2bb24cbab20c02e8cfc5f1cdaef96a6
- Size of remote file:
- 5.43 kB
- SHA256:
- 8373894080e6a1d189514396c566e5949f30002bf6de05c8ee5ca626357e0488
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