metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - data_cedulas_layoutv3
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-cedulas_v3
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: data_cedulas_layoutv3
          type: data_cedulas_layoutv3
          config: default
          split: test
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.8991596638655462
          - name: Recall
            type: recall
            value: 0.9067796610169492
          - name: F1
            type: f1
            value: 0.9029535864978903
          - name: Accuracy
            type: accuracy
            value: 0.9816565809379728

layoutlmv3-finetuned-cedulas_v3

This model is a fine-tuned version of microsoft/layoutlmv3-base on the data_cedulas_layoutv3 dataset. It achieves the following results on the evaluation set:

Loss: 0.0832
Precision: 0.8992
Recall: 0.9068
F1: 0.9030
Accuracy: 0.9817

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: 2500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	3.12	250	0.7409	0.2850	0.2729	0.2788	0.8614
0.9048	6.25	500	0.3660	0.6222	0.6559	0.6386	0.9393
0.9048	9.38	750	0.2132	0.7492	0.7593	0.7542	0.9544
0.2923	12.5	1000	0.1467	0.7830	0.7949	0.7889	0.9661
0.2923	15.62	1250	0.1172	0.8114	0.8237	0.8175	0.9701
0.1445	18.75	1500	0.1013	0.8560	0.8763	0.8660	0.9766
0.1445	21.88	1750	0.0952	0.8811	0.8915	0.8863	0.9794
0.0956	25.0	2000	0.0876	0.8923	0.8983	0.8953	0.9807
0.0956	28.12	2250	0.0840	0.9005	0.9051	0.9028	0.9811
0.0766	31.25	2500	0.0832	0.8992	0.9068	0.9030	0.9817

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3