metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - generated
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-invoice
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: generated
          type: generated
          config: sroie
          split: train
          args: sroie
        metrics:
          - name: Precision
            type: precision
            value: 0.9959514170040485
          - name: Recall
            type: recall
            value: 0.9979716024340771
          - name: F1
            type: f1
            value: 0.9969604863221885
          - name: Accuracy
            type: accuracy
            value: 0.9995786812723826

layoutlmv3-finetuned-invoice

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

Loss: 0.0028
Precision: 0.9960
Recall: 0.9980
F1: 0.9970
Accuracy: 0.9996

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: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.0	100	0.0502	0.97	0.9838	0.9768	0.9968
No log	4.0	200	0.0194	0.972	0.9858	0.9789	0.9971
No log	6.0	300	0.0160	0.972	0.9858	0.9789	0.9971
No log	8.0	400	0.0123	0.972	0.9858	0.9789	0.9971
0.053	10.0	500	0.0089	0.9757	0.9757	0.9757	0.9966
0.053	12.0	600	0.0058	0.9959	0.9919	0.9939	0.9992
0.053	14.0	700	0.0046	0.9939	0.9919	0.9929	0.9989
0.053	16.0	800	0.0037	0.9960	0.9980	0.9970	0.9996
0.053	18.0	900	0.0068	0.9959	0.9878	0.9919	0.9987
0.0057	20.0	1000	0.0054	0.9919	0.9959	0.9939	0.9992
0.0057	22.0	1100	0.0057	0.9919	0.9959	0.9939	0.9992
0.0057	24.0	1200	0.0049	0.9919	0.9959	0.9939	0.9992
0.0057	26.0	1300	0.0052	0.9919	0.9959	0.9939	0.9992
0.0057	28.0	1400	0.0030	0.9960	0.9980	0.9970	0.9996
0.0022	30.0	1500	0.0028	0.9960	0.9980	0.9970	0.9996
0.0022	32.0	1600	0.0030	0.9960	0.9980	0.9970	0.9996
0.0022	34.0	1700	0.0030	0.9960	0.9980	0.9970	0.9996
0.0022	36.0	1800	0.0037	0.9960	0.9980	0.9970	0.9996
0.0022	38.0	1900	0.0037	0.9960	0.9980	0.9970	0.9996
0.0017	40.0	2000	0.0037	0.9960	0.9980	0.9970	0.9996

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1