layoutlmv1-er-ner / README.md
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metadata
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv1-er-ner
    results: []

layoutlmv1-er-ner

This model is a fine-tuned version of renjithks/layoutlmv1-cord-ner on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2092
  • Precision: 0.7202
  • Recall: 0.7238
  • F1: 0.7220
  • Accuracy: 0.9639

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 41 0.2444 0.4045 0.3996 0.4020 0.9226
No log 2.0 82 0.1640 0.5319 0.6098 0.5682 0.9455
No log 3.0 123 0.1531 0.6324 0.6614 0.6466 0.9578
No log 4.0 164 0.1440 0.6927 0.6743 0.6834 0.9620
No log 5.0 205 0.1520 0.6750 0.6958 0.6853 0.9613
No log 6.0 246 0.1597 0.6840 0.6987 0.6913 0.9605
No log 7.0 287 0.1910 0.7002 0.6887 0.6944 0.9605
No log 8.0 328 0.1860 0.6834 0.6923 0.6878 0.9609
No log 9.0 369 0.1665 0.6785 0.7102 0.6940 0.9624
No log 10.0 410 0.1816 0.7016 0.7052 0.7034 0.9624
No log 11.0 451 0.1808 0.6913 0.7166 0.7038 0.9638
No log 12.0 492 0.2165 0.712 0.7023 0.7071 0.9628
0.1014 13.0 533 0.2135 0.6979 0.7109 0.7043 0.9613
0.1014 14.0 574 0.2154 0.6906 0.7109 0.7006 0.9612
0.1014 15.0 615 0.2118 0.6902 0.7016 0.6958 0.9615
0.1014 16.0 656 0.2091 0.6985 0.7080 0.7032 0.9623
0.1014 17.0 697 0.2104 0.7118 0.7123 0.7121 0.9630
0.1014 18.0 738 0.2081 0.7129 0.7231 0.7179 0.9638
0.1014 19.0 779 0.2093 0.7205 0.7231 0.7218 0.9638
0.1014 20.0 820 0.2092 0.7202 0.7238 0.7220 0.9639

Framework versions

  • Transformers 4.18.0
  • Pytorch 1.11.0
  • Datasets 2.1.0
  • Tokenizers 0.12.1