metadata

license: cc-by-nc-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - sroie
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: nexon_jan_2023
    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: 0.975609756097561
          - name: Recall
            type: recall
            value: 0.9302325581395349
          - name: F1
            type: f1
            value: 0.9523809523809524
          - name: Accuracy
            type: accuracy
            value: 0.9971428571428571

nexon_jan_2023

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: 0.0380
Precision: 0.9756
Recall: 0.9302
F1: 0.9524
Accuracy: 0.9971

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: 1500

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	16.67	100	0.1998	0.6286	0.5116	0.5641	0.9571
No log	33.33	200	0.0616	0.9756	0.9302	0.9524	0.9971
No log	50.0	300	0.0439	0.9756	0.9302	0.9524	0.9971
No log	66.67	400	0.0404	0.9756	0.9302	0.9524	0.9971
0.1151	83.33	500	0.0389	0.9756	0.9302	0.9524	0.9971
0.1151	100.0	600	0.0380	0.9756	0.9302	0.9524	0.9971
0.1151	116.67	700	0.0378	0.9756	0.9302	0.9524	0.9971
0.1151	133.33	800	0.0379	0.9756	0.9302	0.9524	0.9971
0.1151	150.0	900	0.0378	0.9756	0.9302	0.9524	0.9971
0.009	166.67	1000	0.0378	0.9756	0.9302	0.9524	0.9971
0.009	183.33	1100	0.0378	0.9756	0.9302	0.9524	0.9971
0.009	200.0	1200	0.0379	0.9756	0.9302	0.9524	0.9971
0.009	216.67	1300	0.0379	0.9756	0.9302	0.9524	0.9971
0.009	233.33	1400	0.0379	0.9756	0.9302	0.9524	0.9971
0.0064	250.0	1500	0.0380	0.9756	0.9302	0.9524	0.9971

Framework versions

Transformers 4.27.0.dev0
Pytorch 1.13.1+cu116
Datasets 2.2.2
Tokenizers 0.13.2