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