metadata

license: cc-by-nc-sa-4.0
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: 0.9871382636655949
          - name: Recall
            type: recall
            value: 0.9935275080906149
          - name: F1
            type: f1
            value: 0.9903225806451612
          - name: Accuracy
            type: accuracy
            value: 0.9992192379762649

layoutlmv3-finetuned-registros_100

This model is a fine-tuned version of microsoft/layoutlmv3-base on the data_registros_layoutv3 dataset. It achieves the following results on the evaluation set:

Loss: 0.0110
Precision: 0.9871
Recall: 0.9935
F1: 0.9903
Accuracy: 0.9992

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: 3000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	10.87	250	0.4325	0.2663	0.2638	0.2650	0.8982
0.6304	21.74	500	0.2065	0.7715	0.8139	0.7921	0.9622
0.6304	32.61	750	0.1058	0.9048	0.9385	0.9214	0.9866
0.1413	43.48	1000	0.0600	0.9314	0.9660	0.9484	0.9944
0.1413	54.35	1250	0.0377	0.9451	0.9741	0.9594	0.9969
0.0558	65.22	1500	0.0277	0.9697	0.9838	0.9767	0.9981
0.0558	76.09	1750	0.0199	0.9792	0.9903	0.9847	0.9988
0.0307	86.96	2000	0.0160	0.9824	0.9919	0.9871	0.9989
0.0307	97.83	2250	0.0147	0.9823	0.9903	0.9863	0.9988
0.0211	108.7	2500	0.0122	0.9871	0.9935	0.9903	0.9992
0.0211	119.57	2750	0.0113	0.9871	0.9935	0.9903	0.9992
0.0174	130.43	3000	0.0110	0.9871	0.9935	0.9903	0.9992

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3