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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("fedihch/InvoiceReceiptClassifier_LayoutLMv3") model = AutoModel.from_pretrained("fedihch/InvoiceReceiptClassifier_LayoutLMv3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from fedihch/InvoiceReceiptClassifier_LayoutLMv3: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/fedihch/InvoiceReceiptClassifier_LayoutLMv3/resolve/main/tokenizer.json
- Command line
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hf download hf://fedihch/InvoiceReceiptClassifier_LayoutLMv3/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/fedihch/InvoiceReceiptClassifier_LayoutLMv3/resolve/main/tokenizer.json
2.11 MB
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