Image Classification
Transformers
PyTorch
Spanish
English
multilingual
layoutlmv3
feature-extraction
Instructions to use fedihch/InvoiceReceiptClassifier_LayoutLMv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fedihch/InvoiceReceiptClassifier_LayoutLMv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fedihch/InvoiceReceiptClassifier_LayoutLMv3") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("fedihch/InvoiceReceiptClassifier_LayoutLMv3") model = AutoModel.from_pretrained("fedihch/InvoiceReceiptClassifier_LayoutLMv3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0bea13b4126ec15521d02bd0cff0edd2b6c9b21f5024dc6209b68db3f49b1dd6
- Size of remote file:
- 501 MB
- SHA256:
- 41c0fc74549fa59dba336150292c585aee4f4a5f5470a71163230cda623e14a3
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