Output_LayoutLMv3

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

  • Loss: 0.2507
  • Precision: 0.8319
  • Recall: 0.8319
  • F1: 0.8319
  • Accuracy: 0.9771

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: 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: 3000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 2.27 100 0.1116 0.7705 0.8319 0.8 0.9676
No log 4.55 200 0.1130 0.8319 0.8540 0.8428 0.9762
No log 6.82 300 0.1707 0.7931 0.8142 0.8035 0.9686
No log 9.09 400 0.1998 0.7521 0.7920 0.7716 0.9648
0.0744 11.36 500 0.1633 0.8210 0.8319 0.8264 0.9752
0.0744 13.64 600 0.1784 0.8182 0.8363 0.8271 0.9752
0.0744 15.91 700 0.1909 0.8095 0.8274 0.8184 0.9724
0.0744 18.18 800 0.1962 0.7974 0.8186 0.8079 0.9724
0.0744 20.45 900 0.1723 0.8412 0.8673 0.8540 0.9781
0.0081 22.73 1000 0.2109 0.8210 0.8319 0.8264 0.9733
0.0081 25.0 1100 0.2194 0.8087 0.8230 0.8158 0.9743
0.0081 27.27 1200 0.2076 0.8465 0.8540 0.8502 0.9771
0.0081 29.55 1300 0.1883 0.8688 0.8496 0.8591 0.9819
0.0081 31.82 1400 0.2042 0.8170 0.8496 0.8330 0.9771
0.0034 34.09 1500 0.2144 0.8261 0.8407 0.8333 0.9771
0.0034 36.36 1600 0.1953 0.8205 0.8496 0.8348 0.9771
0.0034 38.64 1700 0.2259 0.8267 0.8230 0.8248 0.9762
0.0034 40.91 1800 0.2553 0.7974 0.8186 0.8079 0.9714
0.0034 43.18 1900 0.2238 0.8377 0.8451 0.8414 0.9781
0.0006 45.45 2000 0.2245 0.8451 0.8451 0.8451 0.9790
0.0006 47.73 2100 0.2389 0.8326 0.8142 0.8233 0.9762
0.0006 50.0 2200 0.2500 0.8251 0.8142 0.8196 0.9752
0.0006 52.27 2300 0.2537 0.8304 0.8451 0.8377 0.9762
0.0006 54.55 2400 0.2410 0.8319 0.8319 0.8319 0.9771
0.0001 56.82 2500 0.2484 0.8319 0.8319 0.8319 0.9771
0.0001 59.09 2600 0.2517 0.8319 0.8319 0.8319 0.9771
0.0001 61.36 2700 0.2524 0.8319 0.8319 0.8319 0.9771
0.0001 63.64 2800 0.2531 0.8319 0.8319 0.8319 0.9771
0.0001 65.91 2900 0.2528 0.8319 0.8319 0.8319 0.9771
0.0 68.18 3000 0.2507 0.8319 0.8319 0.8319 0.9771

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2
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