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layoutlmv3-finetuned-wildreceipt

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

  • Loss: 0.3154
  • Precision: 0.8693
  • Recall: 0.8753
  • F1: 0.8723
  • Accuracy: 0.9240

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

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 1.3618 0.6375 0.3049 0.4125 0.6708
No log 0.63 200 0.9129 0.6662 0.4897 0.5645 0.7631
No log 0.95 300 0.6800 0.7273 0.6375 0.6795 0.8274
No log 1.26 400 0.5733 0.7579 0.6926 0.7238 0.8501
1.0638 1.58 500 0.5015 0.7854 0.7383 0.7611 0.8667
1.0638 1.89 600 0.4501 0.7916 0.7680 0.7796 0.8770
1.0638 2.21 700 0.4145 0.8177 0.8053 0.8114 0.8917
1.0638 2.52 800 0.3835 0.8214 0.8210 0.8212 0.8961
1.0638 2.84 900 0.3666 0.8251 0.8338 0.8294 0.9009
0.423 3.15 1000 0.3524 0.8485 0.8217 0.8349 0.9026
0.423 3.47 1100 0.3451 0.8430 0.8327 0.8378 0.9060
0.423 3.79 1200 0.3348 0.8347 0.8504 0.8425 0.9092
0.423 4.1 1300 0.3331 0.8368 0.8448 0.8408 0.9079
0.423 4.42 1400 0.3163 0.8503 0.8519 0.8511 0.9138
0.2822 4.73 1500 0.3168 0.8531 0.8504 0.8518 0.9133
0.2822 5.05 1600 0.3013 0.8629 0.8577 0.8603 0.9183
0.2822 5.36 1700 0.3146 0.8619 0.8528 0.8573 0.9160
0.2822 5.68 1800 0.3121 0.8474 0.8638 0.8555 0.9159
0.2822 5.99 1900 0.3054 0.8537 0.8667 0.8601 0.9166
0.2176 6.31 2000 0.3127 0.8556 0.8592 0.8574 0.9167
0.2176 6.62 2100 0.3072 0.8568 0.8667 0.8617 0.9194
0.2176 6.94 2200 0.2989 0.8617 0.8660 0.8638 0.9209
0.2176 7.26 2300 0.2997 0.8616 0.8682 0.8649 0.9199
0.2176 7.57 2400 0.3100 0.8538 0.8689 0.8613 0.9191
0.1777 7.89 2500 0.3022 0.8649 0.8695 0.8672 0.9228
0.1777 8.2 2600 0.2990 0.8631 0.8740 0.8685 0.9224
0.1777 8.52 2700 0.3072 0.8669 0.8697 0.8683 0.9228
0.1777 8.83 2800 0.3038 0.8689 0.8720 0.8705 0.9238
0.1777 9.15 2900 0.3138 0.8726 0.8673 0.8700 0.9216
0.1434 9.46 3000 0.3150 0.8610 0.8740 0.8674 0.9221
0.1434 9.78 3100 0.3055 0.8674 0.8742 0.8708 0.9239
0.1434 10.09 3200 0.3182 0.8618 0.8724 0.8671 0.9215
0.1434 10.41 3300 0.3175 0.8633 0.8727 0.8680 0.9223
0.1434 10.73 3400 0.3146 0.8685 0.8717 0.8701 0.9234
0.1282 11.04 3500 0.3136 0.8671 0.8757 0.8714 0.9233
0.1282 11.36 3600 0.3186 0.8679 0.8734 0.8706 0.9225
0.1282 11.67 3700 0.3147 0.8701 0.8745 0.8723 0.9238
0.1282 11.99 3800 0.3159 0.8705 0.8759 0.8732 0.9244
0.1282 12.3 3900 0.3147 0.8699 0.8748 0.8723 0.9246
0.1121 12.62 4000 0.3154 0.8693 0.8753 0.8723 0.9240

Framework versions

  • Transformers 4.22.0.dev0
  • Pytorch 1.12.0+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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Evaluation results