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updated readme.md
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metadata
license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - dataset
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: sougemi_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: dataset
          type: dataset
          config: discharge
          split: test
          args: discharge
        metrics:
          - name: Precision
            type: precision
            value: 0.845360824742268
          - name: Recall
            type: recall
            value: 0.8913043478260869
          - name: F1
            type: f1
            value: 0.8677248677248677
          - name: Accuracy
            type: accuracy
            value: 0.9533678756476683

sougemi_model

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

  • Loss: 0.1812
  • Precision: 0.8454
  • Recall: 0.8913
  • F1: 0.8677
  • Accuracy: 0.9534

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: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 1000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 33.33 100 0.7803 0.8966 0.8478 0.8715 0.9663
No log 66.67 200 0.3016 0.8696 0.8696 0.8696 0.9767
No log 100.0 300 0.1623 0.9130 0.9130 0.9130 0.9819
No log 133.33 400 0.1680 0.8454 0.8913 0.8677 0.9637
0.5801 166.67 500 0.1812 0.8454 0.8913 0.8677 0.9534
0.5801 200.0 600 0.1231 0.8947 0.9239 0.9091 0.9715
0.5801 233.33 700 0.1363 0.8617 0.8804 0.8710 0.9663
0.5801 266.67 800 0.1949 0.8333 0.8696 0.8511 0.9508
0.5801 300.0 900 0.1749 0.8163 0.8696 0.8421 0.9534
0.0607 333.33 1000 0.1817 0.8163 0.8696 0.8421 0.9534

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu116
  • Datasets 2.8.0
  • Tokenizers 0.13.2