metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - sroie
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: test_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: sroie
          type: sroie
          config: discharge
          split: test
          args: discharge
        metrics:
          - name: Precision
            type: precision
            value: 0.9343065693430657
          - name: Recall
            type: recall
            value: 0.9696969696969697
          - name: F1
            type: f1
            value: 0.9516728624535317
          - name: Accuracy
            type: accuracy
            value: 0.9976019184652278

test_model

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: 0.0114
Precision: 0.9343
Recall: 0.9697
F1: 0.9517
Accuracy: 0.9976

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: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	8.33	100	0.0292	0.8732	0.9394	0.9051	0.9928
No log	16.67	200	0.0110	0.9343	0.9697	0.9517	0.9976
No log	25.0	300	0.0130	0.9209	0.9697	0.9446	0.9971
No log	33.33	400	0.0110	0.9412	0.9697	0.9552	0.9981
0.0466	41.67	500	0.0114	0.9275	0.9697	0.9481	0.9976
0.0466	50.0	600	0.0117	0.9275	0.9697	0.9481	0.9976
0.0466	58.33	700	0.0114	0.9275	0.9697	0.9481	0.9976
0.0466	66.67	800	0.0114	0.9343	0.9697	0.9517	0.9976
0.0466	75.0	900	0.0115	0.9343	0.9697	0.9517	0.9976
0.0006	83.33	1000	0.0114	0.9343	0.9697	0.9517	0.9976

Framework versions

Transformers 4.28.0
Pytorch 2.0.0+cu118
Datasets 2.2.2
Tokenizers 0.13.3