---
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