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---
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
|