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: 0.9979716024340771
          - name: Recall
            type: recall
            value: 0.9979716024340771
          - name: F1
            type: f1
            value: 0.9979716024340771
          - name: Accuracy
            type: accuracy
            value: 0.9997893406361913

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: 0.0040
Precision: 0.9980
Recall: 0.9980
F1: 0.9980
Accuracy: 0.9998

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: 1
eval_batch_size: 1
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	1.0	100	0.1249	0.796	0.8073	0.8016	0.9785
No log	2.0	200	0.0338	0.972	0.9858	0.9789	0.9971
No log	3.0	300	0.0194	0.972	0.9858	0.9789	0.9971
No log	4.0	400	0.0153	0.972	0.9858	0.9789	0.9971
0.1446	5.0	500	0.0126	0.972	0.9858	0.9789	0.9971
0.1446	6.0	600	0.0102	0.9739	0.9858	0.9798	0.9973
0.1446	7.0	700	0.0065	0.9959	0.9939	0.9949	0.9994
0.1446	8.0	800	0.0045	0.9959	0.9959	0.9959	0.9996
0.1446	9.0	900	0.0052	0.9960	0.9980	0.9970	0.9996
0.0103	10.0	1000	0.0040	0.9980	0.9980	0.9980	0.9998

Framework versions

Transformers 4.35.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.14.6
Tokenizers 0.14.1