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:
- e7d106dc3465904ff52b365e6edecb463d17c3395a866d8f91fa66aac0a38867
- Size of remote file:
- 501 MB
- SHA256:
- 015a125660cab48f13c948ae0d6434cd9160da2c144e16fee5346a8483e8f35b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.