Edit model card

LayoutLMv3-Fine-Tuning-Invoice Model

Model description

LayoutLMv3-Fine-Tuning-Invoice Model is a fine-tuned version of microsoft/layoutlmv3-base on the invoice dataset. For the fine-tuning, We used Invoice Dataset that includes 12 labels ('Other', 'ABN', 'BILLER', 'BILLER_ADDRESS', 'BILLER_POST_CODE', 'DUE_DATE', 'GST', 'INVOICE_DATE', 'INVOICE_NUMBER', 'SUBTOTAL', 'TOTAL', 'BILLER_ADDRESS').

It achieves the following results on the evaluation set:

  • Loss: 0.005334
  • Precision: 1.0
  • Recall: 1.0
  • F1: 1.0
  • Accuracy: 1.0

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1.5e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • optimizer: epsilon=1e-08
  • lr_scheduler_type: cosine
  • training_steps: 1000

Training results

Training Loss Step Validation Loss Precision Recall F1 Accuracy
No log 100 0.070030 0.972000 0.985801 0.978852 0.997051
No log 200 0.017637 0.972000 0.985801 0.978852 0.997051
No log 300 0.015573 0.972000 0.985801 0.978852 0.997051
No log 400 0.011000 0.973737 0.977688 0.978852 0.996419
0.110800 500 0.005334 1.0 1.0 1.0 1.0
0.110800 600 0.002994 1.0 1.0 1.0 1.0
0.110800 700 0.002330 1.0 1.0 1.0 1.0
0.110800 800 0.002188 1.0 1.0 1.0 1.0
0.110800 900 0.002105 1.0 1.0 1.0 1.0
0.004900 1000 0.002111 1.0 1.0 1.0 1.0

Framework versions

  • Transformers 4.20.1
Downloads last month
16
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Space using Ammar-alhaj-ali/LayoutLMv3-Fine-Tuning-Invoice 1

Evaluation results