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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_100
    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: 0.9349593495934959
          - name: Recall
            type: recall
            value: 0.9468562874251497
          - name: F1
            type: f1
            value: 0.9408702119747119
          - name: Accuracy
            type: accuracy
            value: 0.9473684210526315

layoutlmv3-finetuned-cord_100

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: 0.2702
  • Precision: 0.9350
  • Recall: 0.9469
  • F1: 0.9409
  • Accuracy: 0.9474

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: 2500

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 4.17 250 1.0496 0.6714 0.7507 0.7088 0.7746
1.4245 8.33 500 0.5492 0.8401 0.8728 0.8561 0.8735
1.4245 12.5 750 0.3773 0.8934 0.9162 0.9047 0.9240
0.3461 16.67 1000 0.3212 0.9287 0.9364 0.9325 0.9380
0.3461 20.83 1250 0.2888 0.9276 0.9401 0.9338 0.9440
0.1502 25.0 1500 0.2749 0.9299 0.9431 0.9365 0.9474
0.1502 29.17 1750 0.2741 0.9321 0.9446 0.9383 0.9469
0.0866 33.33 2000 0.2715 0.9328 0.9454 0.9390 0.9465
0.0866 37.5 2250 0.2740 0.9314 0.9446 0.9379 0.9452
0.0635 41.67 2500 0.2702 0.9350 0.9469 0.9409 0.9474

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.0+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2