metadata

tags:
  - generated_from_trainer
datasets:
  - sroie
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: layoutlmv3-finetuned-invoice
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: sroie
          type: sroie
          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 sroie dataset. It achieves the following results on the evaluation set:

Loss: 0.0018
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: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.0	100	0.0967	0.958	0.9716	0.9648	0.9956
No log	4.0	200	0.0222	0.972	0.9858	0.9789	0.9971
No log	6.0	300	0.0171	0.972	0.9858	0.9789	0.9971
No log	8.0	400	0.0136	0.972	0.9858	0.9789	0.9971
0.1307	10.0	500	0.0117	0.964	0.9777	0.9708	0.9962
0.1307	12.0	600	0.0099	0.972	0.9858	0.9789	0.9971
0.1307	14.0	700	0.0094	0.972	0.9858	0.9789	0.9971
0.1307	16.0	800	0.0071	0.9918	0.9838	0.9878	0.9983
0.1307	18.0	900	0.0026	0.9980	0.9980	0.9980	0.9998
0.0089	20.0	1000	0.0018	1.0	1.0	1.0	1.0
0.0089	22.0	1100	0.0016	1.0	1.0	1.0	1.0
0.0089	24.0	1200	0.0015	1.0	0.9980	0.9990	0.9998
0.0089	26.0	1300	0.0015	0.9980	0.9980	0.9980	0.9998
0.0089	28.0	1400	0.0014	0.9980	0.9980	0.9980	0.9998
0.0025	30.0	1500	0.0011	1.0	1.0	1.0	1.0
0.0025	32.0	1600	0.0012	1.0	1.0	1.0	1.0
0.0025	34.0	1700	0.0011	1.0	1.0	1.0	1.0
0.0025	36.0	1800	0.0010	1.0	1.0	1.0	1.0
0.0025	38.0	1900	0.0010	1.0	1.0	1.0	1.0
0.0019	40.0	2000	0.0011	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.21.0.dev0
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1