metadata

license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
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: test
          args: sroie
        metrics:
          - name: Precision
            type: precision
            value: 1
          - name: Recall
            type: recall
            value: 1
          - name: F1
            type: f1
            value: 1
          - name: Accuracy
            type: accuracy
            value: 1

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: 0.0014
Precision: 1.0
Recall: 1.0
F1: 1.0
Accuracy: 1.0

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: 5000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.0	100	0.0766	0.97	0.9838	0.9768	0.9968
No log	4.0	200	0.0214	0.972	0.9858	0.9789	0.9971
No log	6.0	300	0.0157	0.972	0.9858	0.9789	0.9971
No log	8.0	400	0.0142	0.972	0.9858	0.9789	0.9971
0.1264	10.0	500	0.0129	0.972	0.9858	0.9789	0.9971
0.1264	12.0	600	0.0118	0.972	0.9858	0.9789	0.9971
0.1264	14.0	700	0.0038	0.9980	0.9959	0.9970	0.9996
0.1264	16.0	800	0.0020	1.0	1.0	1.0	1.0
0.1264	18.0	900	0.0016	1.0	1.0	1.0	1.0
0.0064	20.0	1000	0.0014	1.0	1.0	1.0	1.0
0.0064	22.0	1100	0.0012	1.0	1.0	1.0	1.0
0.0064	24.0	1200	0.0010	1.0	1.0	1.0	1.0
0.0064	26.0	1300	0.0009	1.0	1.0	1.0	1.0
0.0064	28.0	1400	0.0008	1.0	1.0	1.0	1.0
0.0018	30.0	1500	0.0008	1.0	1.0	1.0	1.0
0.0018	32.0	1600	0.0007	1.0	1.0	1.0	1.0
0.0018	34.0	1700	0.0006	1.0	1.0	1.0	1.0
0.0018	36.0	1800	0.0006	1.0	1.0	1.0	1.0
0.0018	38.0	1900	0.0005	1.0	1.0	1.0	1.0
0.0011	40.0	2000	0.0005	1.0	1.0	1.0	1.0
0.0011	42.0	2100	0.0005	1.0	1.0	1.0	1.0
0.0011	44.0	2200	0.0004	1.0	1.0	1.0	1.0
0.0011	46.0	2300	0.0004	1.0	1.0	1.0	1.0
0.0011	48.0	2400	0.0004	1.0	1.0	1.0	1.0
0.0008	50.0	2500	0.0004	1.0	1.0	1.0	1.0
0.0008	52.0	2600	0.0003	1.0	1.0	1.0	1.0
0.0008	54.0	2700	0.0003	1.0	1.0	1.0	1.0
0.0008	56.0	2800	0.0003	1.0	1.0	1.0	1.0
0.0008	58.0	2900	0.0003	1.0	1.0	1.0	1.0
0.0006	60.0	3000	0.0003	1.0	1.0	1.0	1.0
0.0006	62.0	3100	0.0003	1.0	1.0	1.0	1.0
0.0006	64.0	3200	0.0003	1.0	1.0	1.0	1.0
0.0006	66.0	3300	0.0002	1.0	1.0	1.0	1.0
0.0006	68.0	3400	0.0002	1.0	1.0	1.0	1.0
0.0005	70.0	3500	0.0002	1.0	1.0	1.0	1.0
0.0005	72.0	3600	0.0002	1.0	1.0	1.0	1.0
0.0005	74.0	3700	0.0002	1.0	1.0	1.0	1.0
0.0005	76.0	3800	0.0002	1.0	1.0	1.0	1.0
0.0005	78.0	3900	0.0002	1.0	1.0	1.0	1.0
0.0004	80.0	4000	0.0002	1.0	1.0	1.0	1.0
0.0004	82.0	4100	0.0002	1.0	1.0	1.0	1.0
0.0004	84.0	4200	0.0002	1.0	1.0	1.0	1.0
0.0004	86.0	4300	0.0002	1.0	1.0	1.0	1.0
0.0004	88.0	4400	0.0002	1.0	1.0	1.0	1.0
0.0003	90.0	4500	0.0002	1.0	1.0	1.0	1.0
0.0003	92.0	4600	0.0002	1.0	1.0	1.0	1.0
0.0003	94.0	4700	0.0002	1.0	1.0	1.0	1.0
0.0003	96.0	4800	0.0002	1.0	1.0	1.0	1.0
0.0003	98.0	4900	0.0002	1.0	1.0	1.0	1.0
0.0003	100.0	5000	0.0002	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.32.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.14.3
Tokenizers 0.13.3