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--- |
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library_name: transformers |
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license: cc-by-nc-sa-4.0 |
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base_model: microsoft/layoutlmv2-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: layoutlmv2-document-classifier |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# layoutlmv2-document-classifier |
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This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0008 |
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- Accuracy: 1.0 |
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## Dataset Infomation |
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This model was fine-tuned to classify some company documents. |
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Dataset used: [Company Documents Dataset](https://www.kaggle.com/datasets/navodpeiris/company-documents-dataset) |
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## Dependencies |
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``` |
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pip install PyMuPDF |
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pip install transformers |
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pip install torch |
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pip install torchvision |
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pip install pytesseract |
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``` |
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- setup tesseract locally in your machine follow steps here: [install instructions](https://tesseract-ocr.github.io/tessdoc/Installation.html) |
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## Model Usage |
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use a file in this dataset to test: https://www.kaggle.com/datasets/navodpeiris/company-documents-dataset |
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``` |
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import os |
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from PIL import Image |
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from transformers import LayoutLMv2Processor, LayoutLMv2ForSequenceClassification |
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import fitz |
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import io |
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processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased") |
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model = LayoutLMv2ForSequenceClassification.from_pretrained("navodPeiris/layoutlmv2-document-classifier") |
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DATA_FOLDER = "data" |
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filename = "invoice.pdf" |
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file_location = os.path.join(DATA_FOLDER, filename) |
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doc = fitz.open(file_location) |
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page = doc.load_page(0) |
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pix = page.get_pixmap(dpi=200) |
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# Convert Pixmap to bytes |
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img_bytes = pix.tobytes("png") |
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# Load into PIL.Image |
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image = Image.open(io.BytesIO(img_bytes)).convert("RGB") |
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doc.close() |
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encoding = processor(image, return_tensors="pt", truncation=True, padding="max_length", max_length=512) |
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outputs = model(**encoding) |
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logits = outputs.logits |
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predicted_class_id = logits.argmax(dim=1).item() |
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classified_output = model.config.id2label[predicted_class_id] |
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print(f"Predicted class: {classified_output}") |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| 0.7722 | 0.0970 | 26 | 0.2249 | 0.9216 | |
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| 0.0828 | 0.1940 | 52 | 0.0452 | 0.9907 | |
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| 0.026 | 0.2910 | 78 | 0.0459 | 0.9907 | |
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| 0.0265 | 0.3881 | 104 | 0.0267 | 0.9907 | |
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| 0.0263 | 0.4851 | 130 | 0.0068 | 1.0 | |
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| 0.008 | 0.5821 | 156 | 0.0026 | 1.0 | |
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| 0.0023 | 0.6791 | 182 | 0.0014 | 1.0 | |
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| 0.0014 | 0.7761 | 208 | 0.0009 | 1.0 | |
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| 0.0011 | 0.8731 | 234 | 0.0008 | 1.0 | |
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| 0.0012 | 0.9701 | 260 | 0.0008 | 1.0 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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