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OCR-LayoutLMv3-Invoice

Token Classification
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layoutlmv3
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OCR-LayoutLMv3-Invoice / README.md
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metadata
license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - wild_receipt
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: microsoft/layoutlmv3-base
model-index:
  - name: OCR-LayoutLMv3-Invoice
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: wild_receipt
          type: wild_receipt
          config: WildReceipt
          split: train
          args: WildReceipt
        metrics:
          - type: precision
            value: 0.8765398302764851
            name: Precision
          - type: recall
            value: 0.8812439796339617
            name: Recall
          - type: f1
            value: 0.8788856103753516
            name: F1
          - type: accuracy
            value: 0.92678512668641
            name: Accuracy

OCR-LayoutLMv3-Invoice

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: 0.3159
  • Precision: 0.8765
  • Recall: 0.8812
  • F1: 0.8789
  • Accuracy: 0.9268

Model description

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: 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: 6000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 0.16 100 1.5032 0.4934 0.1444 0.2234 0.6064
No log 0.32 200 1.0282 0.5884 0.4420 0.5048 0.7385
No log 0.47 300 0.7856 0.7448 0.6205 0.6770 0.8133
No log 0.63 400 0.6464 0.7736 0.6689 0.7174 0.8399
1.1733 0.79 500 0.5672 0.7609 0.7303 0.7453 0.8557
1.1733 0.95 600 0.5055 0.7658 0.7652 0.7655 0.8677
1.1733 1.1 700 0.4735 0.7946 0.7848 0.7897 0.8784
1.1733 1.26 800 0.4414 0.7962 0.7946 0.7954 0.8818
1.1733 1.42 900 0.4094 0.8176 0.8064 0.8120 0.8894
0.5047 1.58 1000 0.3971 0.8219 0.8248 0.8234 0.8961
0.5047 1.74 1100 0.4082 0.7993 0.8362 0.8174 0.8927
0.5047 1.89 1200 0.3797 0.8240 0.8317 0.8278 0.8962
0.5047 2.05 1300 0.3597 0.8326 0.8331 0.8329 0.9020
0.5047 2.21 1400 0.3544 0.8462 0.8283 0.8371 0.9020
0.368 2.37 1500 0.3374 0.8428 0.8435 0.8432 0.9056
0.368 2.52 1600 0.3364 0.8406 0.8522 0.8464 0.9089
0.368 2.68 1700 0.3404 0.8467 0.8536 0.8501 0.9107
0.368 2.84 1800 0.3319 0.8405 0.8501 0.8453 0.9090
0.368 3.0 1900 0.3324 0.8584 0.8492 0.8538 0.9117
0.2949 3.15 2000 0.3204 0.8691 0.8404 0.8545 0.9119
0.2949 3.31 2100 0.3107 0.8599 0.8547 0.8573 0.9162
0.2949 3.47 2200 0.3169 0.8680 0.8489 0.8584 0.9146
0.2949 3.63 2300 0.3190 0.8683 0.8519 0.8600 0.9152
0.2949 3.79 2400 0.2975 0.8631 0.8617 0.8624 0.9182
0.2438 3.94 2500 0.3040 0.8566 0.8640 0.8603 0.9171
0.2438 4.1 2600 0.3045 0.8585 0.8642 0.8613 0.9181
0.2438 4.26 2700 0.3139 0.8498 0.8748 0.8621 0.9160
0.2438 4.42 2800 0.2985 0.8642 0.8672 0.8657 0.9214
0.2438 4.57 2900 0.3047 0.8688 0.8694 0.8691 0.9214
0.2028 4.73 3000 0.2986 0.8686 0.8695 0.8691 0.9207
0.2028 4.89 3100 0.3135 0.8628 0.8755 0.8691 0.9197
0.2028 5.05 3200 0.2927 0.8656 0.8755 0.8705 0.9217
0.2028 5.21 3300 0.2992 0.8724 0.8697 0.8711 0.9228
0.2028 5.36 3400 0.2975 0.8831 0.8639 0.8734 0.9244
0.1814 5.52 3500 0.2897 0.8736 0.8788 0.8762 0.9250
0.1814 5.68 3600 0.3118 0.8674 0.8751 0.8712 0.9216
0.1814 5.84 3700 0.2974 0.8735 0.8779 0.8757 0.9237
0.1814 5.99 3800 0.2957 0.8696 0.8815 0.8755 0.9240
0.1814 6.15 3900 0.3120 0.8698 0.8817 0.8757 0.9250
0.1602 6.31 4000 0.3080 0.8715 0.8800 0.8757 0.9238
0.1602 6.47 4100 0.3031 0.8767 0.8788 0.8777 0.9261
0.1602 6.62 4200 0.3146 0.8699 0.8784 0.8741 0.9227
0.1602 6.78 4300 0.3085 0.8717 0.8788 0.8752 0.9248
0.1602 6.94 4400 0.3023 0.8749 0.8756 0.8752 0.9250
0.1383 7.1 4500 0.3025 0.8860 0.8735 0.8797 0.9252
0.1383 7.26 4600 0.3026 0.8775 0.8810 0.8792 0.9272
0.1383 7.41 4700 0.3146 0.8715 0.8832 0.8773 0.9251
0.1383 7.57 4800 0.3113 0.8769 0.8803 0.8786 0.9275
0.1383 7.73 4900 0.3073 0.8797 0.8786 0.8792 0.9261
0.1306 7.89 5000 0.3163 0.8714 0.8828 0.8770 0.9248
0.1306 8.04 5100 0.3163 0.8753 0.8810 0.8781 0.9250
0.1306 8.2 5200 0.3132 0.8743 0.8804 0.8773 0.9257
0.1306 8.36 5300 0.3119 0.8735 0.8837 0.8786 0.9264
0.1306 8.52 5400 0.3145 0.8826 0.8779 0.8802 0.9272
0.1174 8.68 5500 0.3166 0.8776 0.8811 0.8794 0.9261
0.1174 8.83 5600 0.3146 0.8776 0.8814 0.8795 0.9260
0.1174 8.99 5700 0.3135 0.8763 0.8826 0.8795 0.9271
0.1174 9.15 5800 0.3154 0.8794 0.8818 0.8806 0.9275
0.1174 9.31 5900 0.3152 0.8788 0.8817 0.8802 0.9274
0.11 9.46 6000 0.3159 0.8765 0.8812 0.8789 0.9268

Framework versions

  • Transformers 4.25.0.dev0
  • Pytorch 1.12.1
  • Datasets 2.6.1
  • Tokenizers 0.13.1