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Output_LayoutLMv3_v3

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layoutlmv3
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metadata
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-large
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: Output_LayoutLMv3_v3
    results: []

Output_LayoutLMv3_v3

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: 0.1344
  • Precision: 0.7699
  • Recall: 0.8142
  • F1: 0.7914
  • Accuracy: 0.9695

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: 3e-07
  • train_batch_size: 4
  • 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 4.55 100 0.5786 0.0 0.0 0.0 0.8867
No log 9.09 200 0.4032 0.0 0.0 0.0 0.8867
No log 13.64 300 0.2908 0.4091 0.1593 0.2293 0.9067
No log 18.18 400 0.2300 0.5858 0.4381 0.5013 0.9267
0.5251 22.73 500 0.1981 0.685 0.6062 0.6432 0.9438
0.5251 27.27 600 0.1790 0.7130 0.6814 0.6968 0.9505
0.5251 31.82 700 0.1689 0.7249 0.7345 0.7297 0.9581
0.5251 36.36 800 0.1593 0.7478 0.7478 0.7478 0.9619
0.5251 40.91 900 0.1582 0.75 0.7832 0.7662 0.9638
0.129 45.45 1000 0.1527 0.7306 0.7920 0.7601 0.9619
0.129 50.0 1100 0.1470 0.7429 0.8053 0.7728 0.9638
0.129 54.55 1200 0.1418 0.7552 0.8053 0.7794 0.9657
0.129 59.09 1300 0.1404 0.7657 0.8097 0.7871 0.9667
0.129 63.64 1400 0.1368 0.7741 0.8186 0.7957 0.9695
0.0799 68.18 1500 0.1316 0.7741 0.8186 0.7957 0.9705
0.0799 72.73 1600 0.1301 0.7764 0.8142 0.7948 0.9705
0.0799 77.27 1700 0.1326 0.7699 0.8142 0.7914 0.9695
0.0799 81.82 1800 0.1357 0.7552 0.8053 0.7794 0.9676
0.0799 86.36 1900 0.1304 0.7699 0.8142 0.7914 0.9695
0.0561 90.91 2000 0.1326 0.7699 0.8142 0.7914 0.9695
0.0561 95.45 2100 0.1340 0.7689 0.8097 0.7888 0.9695
0.0561 100.0 2200 0.1371 0.7635 0.8142 0.7880 0.9686
0.0561 104.55 2300 0.1337 0.7764 0.8142 0.7948 0.9705
0.0561 109.09 2400 0.1310 0.7764 0.8142 0.7948 0.9705
0.0451 113.64 2500 0.1353 0.7657 0.8097 0.7871 0.9686
0.0451 118.18 2600 0.1357 0.7657 0.8097 0.7871 0.9686
0.0451 122.73 2700 0.1361 0.7699 0.8142 0.7914 0.9695
0.0451 127.27 2800 0.1358 0.7667 0.8142 0.7897 0.9686
0.0451 131.82 2900 0.1347 0.7699 0.8142 0.7914 0.9695
0.0414 136.36 3000 0.1344 0.7699 0.8142 0.7914 0.9695

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.2.2+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2