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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_v7
    results: []
datasets:
  - Noureddinesa/LayoutLmv3_v1

Output_LayoutLMv3_v7

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

  • Loss: 0.1075
  • Precision: 0.7928
  • Recall: 0.8
  • F1: 0.7964
  • Accuracy: 0.9723

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-06
  • train_batch_size: 12
  • eval_batch_size: 12
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • training_steps: 2600

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 9.09 100 0.4138 0.0 0.0 0.0 0.8962
No log 18.18 200 0.2709 0.1667 0.0273 0.0469 0.9014
No log 27.27 300 0.2003 0.6234 0.4364 0.5134 0.9360
No log 36.36 400 0.1711 0.6496 0.6909 0.6696 0.9481
0.3384 45.45 500 0.1624 0.6667 0.7273 0.6957 0.9498
0.3384 54.55 600 0.1502 0.6803 0.7545 0.7155 0.9550
0.3384 63.64 700 0.1428 0.7227 0.7818 0.7511 0.9602
0.3384 72.73 800 0.1452 0.7049 0.7818 0.7414 0.9550
0.3384 81.82 900 0.1260 0.7544 0.7818 0.7679 0.9671
0.0995 90.91 1000 0.1254 0.7544 0.7818 0.7679 0.9671
0.0995 100.0 1100 0.1211 0.7863 0.8364 0.8106 0.9706
0.0995 109.09 1200 0.1093 0.7739 0.8091 0.7911 0.9706
0.0995 118.18 1300 0.1081 0.7946 0.8091 0.8018 0.9723
0.0995 127.27 1400 0.1108 0.7778 0.8273 0.8018 0.9723
0.0608 136.36 1500 0.1115 0.7627 0.8182 0.7895 0.9706
0.0608 145.45 1600 0.1034 0.8053 0.8273 0.8161 0.9740
0.0608 154.55 1700 0.1050 0.7895 0.8182 0.8036 0.9723
0.0608 163.64 1800 0.1093 0.7739 0.8091 0.7911 0.9706
0.0608 172.73 1900 0.1043 0.7965 0.8182 0.8072 0.9723
0.0443 181.82 2000 0.1048 0.8036 0.8182 0.8108 0.9758
0.0443 190.91 2100 0.1067 0.8036 0.8182 0.8108 0.9758
0.0443 200.0 2200 0.1069 0.8036 0.8182 0.8108 0.9740
0.0443 209.09 2300 0.1083 0.7928 0.8 0.7964 0.9723
0.0443 218.18 2400 0.1079 0.7928 0.8 0.7964 0.9723
0.0381 227.27 2500 0.1076 0.7928 0.8 0.7964 0.9723
0.0381 236.36 2600 0.1075 0.7928 0.8 0.7964 0.9723

Framework versions

  • Transformers 4.29.2
  • Pytorch 2.2.2+cu121
  • Datasets 2.18.0
  • Tokenizers 0.13.3