metadata

tags:
  - generated_from_trainer
datasets:
  - data_registros_layoutv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-registros_100
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: data_registros_layoutv3
          type: data_registros_layoutv3
          config: default
          split: test
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.9967585089141004
          - name: Recall
            type: recall
            value: 0.9951456310679612
          - name: F1
            type: f1
            value: 0.9959514170040485
          - name: Accuracy
            type: accuracy
            value: 0.999531542785759

layoutlmv3-finetuned-registros_100

This model was trained from scratch on the data_registros_layoutv3 dataset. It achieves the following results on the evaluation set:

Loss: 0.0050
Precision: 0.9968
Recall: 0.9951
F1: 0.9960
Accuracy: 0.9995

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-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 600

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	4.35	100	0.0106	0.9871	0.9935	0.9903	0.9991
No log	8.7	200	0.0073	0.9984	0.9968	0.9976	0.9997
No log	13.04	300	0.0061	0.9968	0.9968	0.9968	0.9997
No log	17.39	400	0.0048	0.9968	0.9984	0.9976	0.9997
0.0109	21.74	500	0.0053	0.9968	0.9968	0.9968	0.9997
0.0109	26.09	600	0.0050	0.9968	0.9951	0.9960	0.9995

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3