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--- |
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language: |
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- es |
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- en |
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pipeline_tag: image-classification |
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tags: |
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- image-classification |
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widget: |
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- src: https://upserve.com/media/sites/2/Bill-from-Mezcalero-in-Washington-D.C.-photo-by-Alfredo-Solis-1-e1507226752437.jpg |
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example_title: receipt |
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- src: https://templates.invoicehome.com/invoice-template-us-neat-750px.png |
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example_title: invoice |
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--- |
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**InvoiceReceiptClassifier** is a fine-tuned LayoutLMv2 model that classifies a document to an invoice or receipt. |
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## Quick start: using the raw model |
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```python |
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from transformers import ( |
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AutoModelForSequenceClassification, |
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AutoProcessor, |
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) |
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from PIL import Image |
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model = AutoModelForSequenceClassification.from_pretrained("fedihch/InvoiceReceiptClassifier") |
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processor = AutoProcessor.from_pretrained("fedihch/InvoiceReceiptClassifier") |
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input_img = Image.open("https://upload.wikimedia.org/wikipedia/commons/thumb/0/0b/ReceiptSwiss.jpg/1024px-ReceiptSwiss.jpg") |
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encoded_inputs = processor(input_img, padding="max_length", return_tensors="pt") |
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for k, v in encoded_inputs.items(): |
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encoded_inputs[k] = v.to(model.device) |
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outputs = model(**encoded_inputs) |
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logits = outputs.logits |
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predicted_class_idx = logits.argmax(-1).item() |
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id2label = {0: "invoice", 1: "receipt"} |
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print(id2label[predicted_class_idx]) |
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``` |
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