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OCR-LayoutLMv3

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

  • Loss: 0.9788
  • Precision: 0.8989
  • Recall: 0.9051
  • F1: 0.9020
  • Accuracy: 0.8404

Model description

LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 can be fine-tuned for both text-centric tasks, including form understanding, receipt understanding, and document visual question answering, and image-centric tasks such as document image classification and document layout analysis.

LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei, Preprint 2022.

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: 2000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.33 100 0.6966 0.7418 0.8063 0.7727 0.7801
No log 2.67 200 0.5767 0.8104 0.8644 0.8365 0.8117
No log 4.0 300 0.5355 0.8246 0.8852 0.8539 0.8295
No log 5.33 400 0.5240 0.8706 0.8922 0.8813 0.8427
0.5326 6.67 500 0.6337 0.8528 0.8778 0.8651 0.8260
0.5326 8.0 600 0.6870 0.8698 0.8828 0.8762 0.8240
0.5326 9.33 700 0.6584 0.8723 0.9061 0.8889 0.8342
0.5326 10.67 800 0.7186 0.8868 0.9031 0.8949 0.8335
0.5326 12.0 900 0.6822 0.9040 0.9076 0.9058 0.8526
0.1248 13.33 1000 0.7042 0.8872 0.9021 0.8946 0.8511
0.1248 14.67 1100 0.7920 0.9027 0.9036 0.9032 0.8480
0.1248 16.0 1200 0.8052 0.8964 0.9151 0.9056 0.8389
0.1248 17.33 1300 0.8932 0.8995 0.9066 0.9030 0.8329
0.1248 18.67 1400 0.8728 0.8950 0.9061 0.9005 0.8398
0.0442 20.0 1500 0.9051 0.8960 0.9116 0.9037 0.8347
0.0442 21.33 1600 0.9587 0.8947 0.9031 0.8989 0.8401
0.0442 22.67 1700 0.9822 0.9042 0.9046 0.9044 0.8389
0.0442 24.0 1800 0.9734 0.9043 0.9061 0.9052 0.8391
0.0442 25.33 1900 0.9842 0.9042 0.9091 0.9066 0.8410
0.0225 26.67 2000 0.9788 0.8989 0.9051 0.9020 0.8404

Framework versions

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