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layoutlmv3-finetuned-wildreceipt

This model is a fine-tuned version of microsoft/layoutlmv3-base on the wild_receipt dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3108
  • Precision: 0.8772
  • Recall: 0.8799
  • F1: 0.8785
  • Accuracy: 0.9249

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

The WildReceipt dataset consists of 1740 receipt images, and contains 25 key information categories, and a total of about 69000 text boxes. 1268 and 472 images are used for training and testing respectively to train the LayoutLMv3 model for Key Information Extraction.

Training procedure

The training code: https://github.com/Theivaprakasham/layoutlmv3/blob/main/training_codes/LayoutLMv3_training_WildReceipts_dataset.ipynb

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 4000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 0.32 100 1.3143 0.6709 0.2679 0.3829 0.6700
No log 0.63 200 0.8814 0.6478 0.5195 0.5766 0.7786
No log 0.95 300 0.6568 0.7205 0.6491 0.6829 0.8303
No log 1.26 400 0.5618 0.7544 0.7072 0.7300 0.8519
1.0284 1.58 500 0.5003 0.7802 0.7566 0.7682 0.8687
1.0284 1.89 600 0.4454 0.7941 0.7679 0.7807 0.8748
1.0284 2.21 700 0.4314 0.8142 0.7928 0.8033 0.8852
1.0284 2.52 800 0.3870 0.8172 0.8200 0.8186 0.8953
1.0284 2.84 900 0.3629 0.8288 0.8369 0.8329 0.9025
0.4167 3.15 1000 0.3537 0.8540 0.8200 0.8366 0.9052
0.4167 3.47 1100 0.3383 0.8438 0.8285 0.8361 0.9063
0.4167 3.79 1200 0.3403 0.8297 0.8493 0.8394 0.9062
0.4167 4.1 1300 0.3271 0.8428 0.8545 0.8487 0.9110
0.4167 4.42 1400 0.3182 0.8491 0.8518 0.8504 0.9131
0.2766 4.73 1500 0.3111 0.8491 0.8539 0.8515 0.9129
0.2766 5.05 1600 0.3177 0.8397 0.8620 0.8507 0.9124
0.2766 5.36 1700 0.3091 0.8676 0.8548 0.8612 0.9191
0.2766 5.68 1800 0.3080 0.8508 0.8645 0.8576 0.9162
0.2766 5.99 1900 0.3059 0.8492 0.8662 0.8576 0.9163
0.2114 6.31 2000 0.3184 0.8536 0.8657 0.8596 0.9147
0.2114 6.62 2100 0.3161 0.8583 0.8713 0.8648 0.9184
0.2114 6.94 2200 0.3055 0.8707 0.8682 0.8694 0.9220
0.2114 7.26 2300 0.3004 0.8689 0.8745 0.8717 0.9219
0.2114 7.57 2400 0.3111 0.8701 0.8720 0.8711 0.9211
0.174 7.89 2500 0.3130 0.8599 0.8741 0.8669 0.9198
0.174 8.2 2600 0.3034 0.8661 0.8748 0.8704 0.9219
0.174 8.52 2700 0.3005 0.8799 0.8673 0.8736 0.9225
0.174 8.83 2800 0.3043 0.8687 0.8804 0.8745 0.9240
0.174 9.15 2900 0.3121 0.8776 0.8704 0.8740 0.9242
0.1412 9.46 3000 0.3131 0.8631 0.8755 0.8692 0.9204
0.1412 9.78 3100 0.3067 0.8715 0.8773 0.8744 0.9233
0.1412 10.09 3200 0.3021 0.8751 0.8812 0.8782 0.9248
0.1412 10.41 3300 0.3092 0.8651 0.8808 0.8729 0.9228
0.1412 10.73 3400 0.3084 0.8776 0.8749 0.8762 0.9237
0.1254 11.04 3500 0.3156 0.8738 0.8785 0.8761 0.9237
0.1254 11.36 3600 0.3131 0.8723 0.8818 0.8770 0.9244
0.1254 11.67 3700 0.3108 0.8778 0.8781 0.8780 0.9250
0.1254 11.99 3800 0.3097 0.8778 0.8771 0.8775 0.9239
0.1254 12.3 3900 0.3115 0.8785 0.8801 0.8793 0.9251
0.111 12.62 4000 0.3108 0.8772 0.8799 0.8785 0.9249

Framework versions

  • Transformers 4.20.0.dev0
  • Pytorch 1.11.0+cu113
  • Datasets 2.2.2
  • Tokenizers 0.12.1
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