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metadata
license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - cord-layoutlmv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-cord_300
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: cord-layoutlmv3
          type: cord-layoutlmv3
          config: cord
          split: train
          args: cord
        metrics:
          - name: Precision
            type: precision
            value: 0.9325426241660489
          - name: Recall
            type: recall
            value: 0.9416167664670658
          - name: F1
            type: f1
            value: 0.9370577281191806
          - name: Accuracy
            type: accuracy
            value: 0.9363327674023769

layoutlmv3-finetuned-cord_300

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

  • Loss: 0.3434
  • Precision: 0.9325
  • Recall: 0.9416
  • F1: 0.9371
  • Accuracy: 0.9363

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: 5
  • eval_batch_size: 5
  • 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 4.17 250 1.0379 0.7204 0.7829 0.7504 0.7941
1.4162 8.33 500 0.5642 0.8462 0.8772 0.8614 0.8820
1.4162 12.5 750 0.3836 0.9055 0.9184 0.9119 0.9206
0.3211 16.67 1000 0.3209 0.9139 0.9296 0.9217 0.9334
0.3211 20.83 1250 0.2962 0.9275 0.9386 0.9330 0.9435
0.1191 25.0 1500 0.2979 0.9254 0.9379 0.9316 0.9402
0.1191 29.17 1750 0.3079 0.9282 0.9386 0.9334 0.9355
0.059 33.33 2000 0.3039 0.9232 0.9364 0.9298 0.9325
0.059 37.5 2250 0.3254 0.9248 0.9386 0.9316 0.9355
0.0342 41.67 2500 0.3404 0.9246 0.9364 0.9305 0.9334
0.0342 45.83 2750 0.3386 0.9354 0.9431 0.9392 0.9355
0.0226 50.0 3000 0.3274 0.9354 0.9431 0.9392 0.9359
0.0226 54.17 3250 0.3282 0.9341 0.9446 0.9393 0.9393
0.017 58.33 3500 0.3475 0.9319 0.9424 0.9371 0.9363
0.017 62.5 3750 0.3367 0.9340 0.9431 0.9385 0.9372
0.0145 66.67 4000 0.3434 0.9325 0.9416 0.9371 0.9363

Framework versions

  • Transformers 4.21.2
  • Pytorch 1.12.1+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1