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LayoutLM-v3 model fine-tuned on invoice dataset

This model is a fine-tuned version of microsoft/layoutlmv3-base on the invoice dataset.

We use Microsoft’s LayoutLMv3 trained on Invoice Dataset to predict the Biller Name, Biller Address, Biller post_code, Due_date, GST, Invoice_date, Invoice_number, Subtotal and Total. To use it, simply upload an image or use the example image below. Results will show up in a few seconds.

It achieves the following results on the evaluation set:

  • Loss: 0.0012
  • Precision: 1.0
  • Recall: 1.0
  • F1: 1.0
  • Accuracy: 1.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

All the training codes are available from the below GitHub link.

https://github.com/Theivaprakasham/layoutlmv3

The model can be evaluated at the HuggingFace Spaces link:

https://huggingface.co/spaces/Theivaprakasham/layoutlmv3_invoice

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 2000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 2.0 100 0.0878 0.968 0.9817 0.9748 0.9966
No log 4.0 200 0.0241 0.972 0.9858 0.9789 0.9971
No log 6.0 300 0.0186 0.972 0.9858 0.9789 0.9971
No log 8.0 400 0.0184 0.9854 0.9574 0.9712 0.9956
0.1308 10.0 500 0.0121 0.972 0.9858 0.9789 0.9971
0.1308 12.0 600 0.0076 0.9939 0.9878 0.9908 0.9987
0.1308 14.0 700 0.0047 1.0 0.9959 0.9980 0.9996
0.1308 16.0 800 0.0036 0.9960 0.9980 0.9970 0.9996
0.1308 18.0 900 0.0045 0.9960 0.9980 0.9970 0.9996
0.0069 20.0 1000 0.0043 0.9960 0.9980 0.9970 0.9996
0.0069 22.0 1100 0.0016 1.0 1.0 1.0 1.0
0.0069 24.0 1200 0.0015 1.0 1.0 1.0 1.0
0.0069 26.0 1300 0.0014 1.0 1.0 1.0 1.0
0.0069 28.0 1400 0.0013 1.0 1.0 1.0 1.0
0.0026 30.0 1500 0.0012 1.0 1.0 1.0 1.0
0.0026 32.0 1600 0.0012 1.0 1.0 1.0 1.0
0.0026 34.0 1700 0.0011 1.0 1.0 1.0 1.0
0.0026 36.0 1800 0.0011 1.0 1.0 1.0 1.0
0.0026 38.0 1900 0.0011 1.0 1.0 1.0 1.0
0.002 40.0 2000 0.0011 1.0 1.0 1.0 1.0

Framework versions

  • Transformers 4.20.0.dev0
  • Pytorch 1.11.0+cu113
  • Datasets 2.2.2
  • Tokenizers 0.12.1
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Spaces using Theivaprakasham/layoutlmv3-finetuned-invoice

Evaluation results