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layoutlmv3-for-complete-receipt-understanding

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metadata
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: NLPmonster/layoutlmv3-for-receipt-understanding
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-for-complete-receipt-understanding
    results: []

layoutlmv3-for-complete-receipt-understanding

This model is a fine-tuned version of NLPmonster/layoutlmv3-for-receipt-understanding on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4673
  • Precision: 0.8401
  • Recall: 0.8399
  • F1: 0.8400
  • Accuracy: 0.8784

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: 5e-05
  • train_batch_size: 5
  • eval_batch_size: 5
  • 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
1.0756 0.4425 50 0.5379 0.7401 0.7577 0.7488 0.8092
0.5502 0.8850 100 0.4509 0.7628 0.8035 0.7827 0.8354
0.4459 1.3274 150 0.4267 0.7667 0.8307 0.7974 0.8461
0.4209 1.7699 200 0.4030 0.7837 0.8130 0.7981 0.8476
0.3973 2.2124 250 0.3828 0.7930 0.8222 0.8073 0.8545
0.3421 2.6549 300 0.3754 0.8199 0.8060 0.8129 0.8618
0.3529 3.0973 350 0.3780 0.7888 0.8464 0.8166 0.8585
0.2961 3.5398 400 0.4031 0.7724 0.8512 0.8099 0.8493
0.3119 3.9823 450 0.3564 0.8111 0.8424 0.8265 0.8676
0.2629 4.4248 500 0.3746 0.7991 0.8427 0.8203 0.8649
0.2684 4.8673 550 0.3764 0.8198 0.8028 0.8112 0.8611
0.2433 5.3097 600 0.3752 0.8225 0.8330 0.8277 0.8684
0.2289 5.7522 650 0.3966 0.7908 0.8377 0.8136 0.8561
0.2141 6.1947 700 0.3870 0.8251 0.8175 0.8213 0.8645
0.2072 6.6372 750 0.3782 0.8129 0.8427 0.8275 0.8694
0.2101 7.0796 800 0.3758 0.8311 0.8379 0.8345 0.8743
0.1848 7.5221 850 0.3959 0.8063 0.8342 0.8200 0.8638
0.1787 7.9646 900 0.4088 0.8127 0.8360 0.8241 0.8634
0.1563 8.4071 950 0.4146 0.8068 0.8222 0.8144 0.8598
0.1617 8.8496 1000 0.3919 0.8220 0.8360 0.8289 0.8714
0.1498 9.2920 1050 0.4222 0.8149 0.8222 0.8186 0.8625
0.1422 9.7345 1100 0.4104 0.8188 0.8402 0.8293 0.8699
0.1341 10.1770 1150 0.4207 0.8370 0.8115 0.8241 0.8701
0.1311 10.6195 1200 0.4277 0.8401 0.8135 0.8266 0.8710
0.1239 11.0619 1250 0.4153 0.8368 0.8222 0.8295 0.8729
0.1139 11.5044 1300 0.4330 0.8272 0.8379 0.8325 0.8721
0.1126 11.9469 1350 0.4389 0.8393 0.8295 0.8344 0.8739
0.0983 12.3894 1400 0.4601 0.8362 0.8148 0.8254 0.8679
0.1027 12.8319 1450 0.4431 0.8369 0.8280 0.8324 0.8732
0.0944 13.2743 1500 0.4557 0.8253 0.8422 0.8337 0.8717
0.0866 13.7168 1550 0.4566 0.8333 0.8312 0.8323 0.8734
0.0872 14.1593 1600 0.4609 0.8390 0.8312 0.8351 0.8760
0.079 14.6018 1650 0.4522 0.8349 0.8357 0.8353 0.8765
0.0793 15.0442 1700 0.4590 0.8263 0.8447 0.8354 0.8740
0.0738 15.4867 1750 0.4606 0.8373 0.8275 0.8324 0.8751
0.0704 15.9292 1800 0.4553 0.8454 0.8369 0.8411 0.8812
0.0642 16.3717 1850 0.4724 0.8339 0.8424 0.8381 0.8766
0.0647 16.8142 1900 0.4670 0.8429 0.8417 0.8423 0.8812
0.0624 17.2566 1950 0.4647 0.8410 0.8402 0.8406 0.8792
0.0593 17.6991 2000 0.4673 0.8401 0.8399 0.8400 0.8784

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.19.1