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: 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 generated dataset. It achieves the following results on the evaluation set:

Loss: 0.0012
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	0.0877	0.94	0.9533	0.9466	0.9937
No log	4.0	200	0.0244	0.972	0.9858	0.9789	0.9971
No log	6.0	300	0.0162	0.972	0.9858	0.9789	0.9971
No log	8.0	400	0.0142	0.972	0.9858	0.9789	0.9971
0.1178	10.0	500	0.0119	0.972	0.9858	0.9789	0.9971
0.1178	12.0	600	0.0122	0.972	0.9858	0.9789	0.9971
0.1178	14.0	700	0.0035	1.0	0.9980	0.9990	0.9998
0.1178	16.0	800	0.0023	1.0	1.0	1.0	1.0
0.1178	18.0	900	0.0029	0.9960	0.9980	0.9970	0.9996
0.0064	20.0	1000	0.0027	0.9960	0.9980	0.9970	0.9996
0.0064	22.0	1100	0.0020	0.9980	1.0	0.9990	0.9998
0.0064	24.0	1200	0.0022	0.9980	1.0	0.9990	0.9998
0.0064	26.0	1300	0.0013	1.0	1.0	1.0	1.0
0.0064	28.0	1400	0.0014	0.9980	1.0	0.9990	0.9998
0.0025	30.0	1500	0.0012	1.0	1.0	1.0	1.0
0.0025	32.0	1600	0.0011	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	0.0010	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.32.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3