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Output_LayoutLMv3_v4

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metadata
license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Output_LayoutLMv3_v99
    results: []
datasets:
  - Noureddinesa/LayoutLmv3_v1

Output_LayoutLMv3_v99

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: 0.1581
  • Precision: 0.7822
  • Recall: 0.7182
  • F1: 0.7488
  • Accuracy: 0.9619

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-07
  • train_batch_size: 3
  • eval_batch_size: 3
  • 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 2.38 100 1.4434 0.0283 0.0636 0.0392 0.6938
No log 4.76 200 0.7802 0.0 0.0 0.0 0.8945
No log 7.14 300 0.5023 0.0 0.0 0.0 0.8962
No log 9.52 400 0.4425 0.0 0.0 0.0 0.8962
0.8848 11.9 500 0.3951 0.0 0.0 0.0 0.8962
0.8848 14.29 600 0.3557 0.0 0.0 0.0 0.8962
0.8848 16.67 700 0.3236 0.0 0.0 0.0 0.8962
0.8848 19.05 800 0.2988 0.2143 0.0273 0.0484 0.8997
0.8848 21.43 900 0.2787 0.4167 0.0909 0.1493 0.9066
0.3328 23.81 1000 0.2623 0.4839 0.1364 0.2128 0.9100
0.3328 26.19 1100 0.2474 0.5238 0.2 0.2895 0.9187
0.3328 28.57 1200 0.2358 0.6038 0.2909 0.3926 0.9308
0.3328 30.95 1300 0.2267 0.6 0.3 0.4 0.9325
0.3328 33.33 1400 0.2172 0.6032 0.3455 0.4393 0.9343
0.2435 35.71 1500 0.2113 0.5821 0.3545 0.4407 0.9343
0.2435 38.1 1600 0.2042 0.5634 0.3636 0.4420 0.9343
0.2435 40.48 1700 0.1981 0.6203 0.4455 0.5185 0.9429
0.2435 42.86 1800 0.1923 0.6628 0.5182 0.5816 0.9446
0.2435 45.24 1900 0.1895 0.6818 0.5455 0.6061 0.9481
0.1971 47.62 2000 0.1846 0.7128 0.6091 0.6569 0.9533
0.1971 50.0 2100 0.1811 0.7526 0.6636 0.7053 0.9585
0.1971 52.38 2200 0.1797 0.7396 0.6455 0.6893 0.9567
0.1971 54.76 2300 0.1755 0.7653 0.6818 0.7212 0.9602
0.1971 57.14 2400 0.1745 0.7526 0.6636 0.7053 0.9585
0.1722 59.52 2500 0.1707 0.7526 0.6636 0.7053 0.9585
0.1722 61.9 2600 0.1672 0.7526 0.6636 0.7053 0.9585
0.1722 64.29 2700 0.1662 0.7677 0.6909 0.7273 0.9602
0.1722 66.67 2800 0.1659 0.7677 0.6909 0.7273 0.9602
0.1722 69.05 2900 0.1650 0.78 0.7091 0.7429 0.9619
0.1558 71.43 3000 0.1633 0.78 0.7091 0.7429 0.9619
0.1558 73.81 3100 0.1613 0.78 0.7091 0.7429 0.9619
0.1558 76.19 3200 0.1605 0.78 0.7091 0.7429 0.9619
0.1558 78.57 3300 0.1600 0.78 0.7091 0.7429 0.9619
0.1558 80.95 3400 0.1594 0.78 0.7091 0.7429 0.9619
0.1461 83.33 3500 0.1588 0.7822 0.7182 0.7488 0.9619
0.1461 85.71 3600 0.1588 0.7822 0.7182 0.7488 0.9619
0.1461 88.1 3700 0.1584 0.7822 0.7182 0.7488 0.9619
0.1461 90.48 3800 0.1583 0.7822 0.7182 0.7488 0.9619
0.1461 92.86 3900 0.1581 0.7822 0.7182 0.7488 0.9619
0.1438 95.24 4000 0.1581 0.7822 0.7182 0.7488 0.9619

Framework versions

  • Transformers 4.29.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.13.3