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: 0.972
          - name: Recall
            type: recall
            value: 0.9858012170385395
          - name: F1
            type: f1
            value: 0.9788519637462235
          - name: Accuracy
            type: accuracy
            value: 0.9970507689066779

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: 0.0116
Precision: 0.972
Recall: 0.9858
F1: 0.9789
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: 875

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	2.0	100	0.0898	0.972	0.9858	0.9789	0.9971
No log	4.0	200	0.0251	0.972	0.9858	0.9789	0.9971
No log	6.0	300	0.0176	0.972	0.9858	0.9789	0.9971
No log	8.0	400	0.0148	0.972	0.9858	0.9789	0.9971
0.1241	10.0	500	0.0116	0.972	0.9858	0.9789	0.9971
0.1241	12.0	600	0.0072	0.9919	0.9959	0.9939	0.9992
0.1241	14.0	700	0.0059	0.9960	0.9980	0.9970	0.9996
0.1241	16.0	800	0.0044	0.9980	0.9980	0.9980	0.9998

Framework versions

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