This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the layoutlmv3 dataset. It achieves the following results on the evaluation set:

Loss: 0.0219
Ax Amount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
Endor Name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
Nvoice Number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
Otal Amount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
Ustomer Address: {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11}
Ustomer Name: {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}
Overall Precision: 0.9846
Overall Recall: 0.9697
Overall F1: 0.9771
Overall Accuracy: 0.9939

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: 6
eval_batch_size: 3
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 300

Training results

Training Loss	Epoch	Step	Validation Loss	Ax Amount	Endor Name	Nvoice Number	Otal Amount	Ustomer Address	Ustomer Name	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.8763	6.25	50	0.2290	{'precision': 1.0, 'recall': 0.5454545454545454, 'f1': 0.7058823529411764, 'number': 11}	{'precision': 0.8181818181818182, 'recall': 0.8181818181818182, 'f1': 0.8181818181818182, 'number': 11}	{'precision': 1.0, 'recall': 0.8181818181818182, 'f1': 0.9, 'number': 11}	{'precision': 0.5454545454545454, 'recall': 0.5454545454545454, 'f1': 0.5454545454545454, 'number': 11}	{'precision': 0.7692307692307693, 'recall': 0.9090909090909091, 'f1': 0.8333333333333333, 'number': 11}	{'precision': 0.75, 'recall': 0.8181818181818182, 'f1': 0.7826086956521738, 'number': 11}	0.7903	0.7424	0.7656	0.9666
0.1315	12.5	100	0.0312	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11}	{'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11}	0.9701	0.9848	0.9774	0.9970
0.0239	18.75	150	0.0371	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11}	{'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}	0.9846	0.9697	0.9771	0.9939
0.0098	25.0	200	0.0450	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11}	{'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}	0.9846	0.9697	0.9771	0.9939
0.0085	31.25	250	0.0360	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11}	{'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}	0.9846	0.9697	0.9771	0.9939
0.0065	37.5	300	0.0219	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}	{'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11}	{'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}	0.9846	0.9697	0.9771	0.9939

Framework versions

Transformers 4.32.1
Pytorch 2.2.0+cpu
Datasets 2.12.0
Tokenizers 0.13.2

Szczotar93/LayoutLM_Invoice6

LayoutLM_Invoice6

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Szczotar93/
LayoutLM_Invoice6