Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,30 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
**InvoiceReceiptClassifier** is a fine-tuned LayoutLMv2 model that classifies a document to an invoice or receipt.
|
2 |
+
|
3 |
+
## Quick start: using the raw model
|
4 |
+
|
5 |
+
```python
|
6 |
+
from transformers import (
|
7 |
+
AutoModelForSequenceClassification,
|
8 |
+
LayoutLMv2FeatureExtractor,
|
9 |
+
LayoutLMv2Tokenizer,
|
10 |
+
LayoutLMv2Processor,
|
11 |
+
)
|
12 |
+
model = AutoModelForSequenceClassification.from_pretrained("fedihch/InvoiceReceiptClassifier")
|
13 |
+
feature_extractor = LayoutLMv2FeatureExtractor()
|
14 |
+
tokenizer = LayoutLMv2Tokenizer.from_pretrained("microsoft/layoutlmv2-base-uncased")
|
15 |
+
processor = LayoutLMv2Processor(feature_extractor, tokenizer)
|
16 |
+
```
|
17 |
+
```python
|
18 |
+
from PIL import Image
|
19 |
+
input_img = Image.open("*****.jpg")
|
20 |
+
w, h = input_img.size
|
21 |
+
input_img = input_img.convert("RGB").resize((int(w * 600 / h), 600))
|
22 |
+
encoded_inputs = processor(input_img, return_tensors="pt")
|
23 |
+
for k, v in encoded_inputs.items():
|
24 |
+
encoded_inputs[k] = v.to(model.device)
|
25 |
+
outputs = model(**encoded_inputs)
|
26 |
+
logits = outputs.logits
|
27 |
+
predicted_class_idx = logits.argmax(-1).item()
|
28 |
+
id2label = {0: "invoice", 1: "receipt"}
|
29 |
+
print(id2label[predicted_class_idx])
|
30 |
+
```
|