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: 0.1191
Creditor address: {'precision': 0.9807692307692307, 'recall': 0.9622641509433962, 'f1': 0.9714285714285713, 'number': 53}
Creditor name: {'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 35}
Creditor proxy: {'precision': 0.75, 'recall': 0.8823529411764706, 'f1': 0.8108108108108107, 'number': 34}
Debtor address: {'precision': 0.9807692307692307, 'recall': 0.9807692307692307, 'f1': 0.9807692307692307, 'number': 52}
Debtor name: {'precision': 0.926829268292683, 'recall': 0.95, 'f1': 0.9382716049382716, 'number': 40}
Doc id: {'precision': 0.6875, 'recall': 0.6875, 'f1': 0.6875, 'number': 16}
Title: {'precision': 0.9772727272727273, 'recall': 0.7678571428571429, 'f1': 0.86, 'number': 56}
Overall Precision: 0.9217
Overall Recall: 0.9056
Overall F1: 0.9136
Overall Accuracy: 0.9755

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: 3e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Creditor address	Creditor name	Creditor proxy	Debtor address	Debtor name	Doc id	Title	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.2524	6.6667	20	0.6528	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 53}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 35}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 34}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 52}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 40}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 16}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 56}	0.0	0.0	0.0	0.8405
0.4371	13.3333	40	0.2820	{'precision': 0.7457627118644068, 'recall': 0.8301886792452831, 'f1': 0.7857142857142858, 'number': 53}	{'precision': 0.868421052631579, 'recall': 0.9428571428571428, 'f1': 0.904109589041096, 'number': 35}	{'precision': 0.9166666666666666, 'recall': 0.3235294117647059, 'f1': 0.4782608695652174, 'number': 34}	{'precision': 0.6222222222222222, 'recall': 0.5384615384615384, 'f1': 0.577319587628866, 'number': 52}	{'precision': 0.9375, 'recall': 0.375, 'f1': 0.5357142857142857, 'number': 40}	{'precision': 0.8, 'recall': 0.5, 'f1': 0.6153846153846154, 'number': 16}	{'precision': 0.8235294117647058, 'recall': 0.75, 'f1': 0.7850467289719627, 'number': 56}	0.7835	0.6329	0.7002	0.9320
0.1154	20.0	60	0.1217	{'precision': 1.0, 'recall': 0.9433962264150944, 'f1': 0.970873786407767, 'number': 53}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 35}	{'precision': 0.7666666666666667, 'recall': 0.6764705882352942, 'f1': 0.71875, 'number': 34}	{'precision': 0.8947368421052632, 'recall': 0.9807692307692307, 'f1': 0.9357798165137614, 'number': 52}	{'precision': 0.9142857142857143, 'recall': 0.8, 'f1': 0.8533333333333333, 'number': 40}	{'precision': 0.6875, 'recall': 0.6875, 'f1': 0.6875, 'number': 16}	{'precision': 0.9565217391304348, 'recall': 0.7857142857142857, 'f1': 0.8627450980392156, 'number': 56}	0.9111	0.8601	0.8849	0.9682
0.0263	26.6667	80	0.1306	{'precision': 0.9803921568627451, 'recall': 0.9433962264150944, 'f1': 0.9615384615384616, 'number': 53}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 35}	{'precision': 0.7307692307692307, 'recall': 0.5588235294117647, 'f1': 0.6333333333333334, 'number': 34}	{'precision': 0.9807692307692307, 'recall': 0.9807692307692307, 'f1': 0.9807692307692307, 'number': 52}	{'precision': 0.926829268292683, 'recall': 0.95, 'f1': 0.9382716049382716, 'number': 40}	{'precision': 0.6875, 'recall': 0.6875, 'f1': 0.6875, 'number': 16}	{'precision': 1.0, 'recall': 0.7857142857142857, 'f1': 0.88, 'number': 56}	0.9323	0.8671	0.8986	0.9704
0.0113	33.3333	100	0.1161	{'precision': 0.9803921568627451, 'recall': 0.9433962264150944, 'f1': 0.9615384615384616, 'number': 53}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 35}	{'precision': 0.75, 'recall': 0.8823529411764706, 'f1': 0.8108108108108107, 'number': 34}	{'precision': 1.0, 'recall': 0.9807692307692307, 'f1': 0.9902912621359222, 'number': 52}	{'precision': 0.9285714285714286, 'recall': 0.975, 'f1': 0.951219512195122, 'number': 40}	{'precision': 0.6875, 'recall': 0.6875, 'f1': 0.6875, 'number': 16}	{'precision': 1.0, 'recall': 0.75, 'f1': 0.8571428571428571, 'number': 56}	0.9281	0.9021	0.9149	0.9755
0.0079	40.0	120	0.1306	{'precision': 0.9803921568627451, 'recall': 0.9433962264150944, 'f1': 0.9615384615384616, 'number': 53}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 35}	{'precision': 0.7272727272727273, 'recall': 0.7058823529411765, 'f1': 0.7164179104477613, 'number': 34}	{'precision': 1.0, 'recall': 0.9807692307692307, 'f1': 0.9902912621359222, 'number': 52}	{'precision': 0.926829268292683, 'recall': 0.95, 'f1': 0.9382716049382716, 'number': 40}	{'precision': 0.6875, 'recall': 0.6875, 'f1': 0.6875, 'number': 16}	{'precision': 1.0, 'recall': 0.7678571428571429, 'f1': 0.8686868686868687, 'number': 56}	0.9299	0.8811	0.9048	0.9727
0.0064	46.6667	140	0.1191	{'precision': 0.9807692307692307, 'recall': 0.9622641509433962, 'f1': 0.9714285714285713, 'number': 53}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 35}	{'precision': 0.75, 'recall': 0.8823529411764706, 'f1': 0.8108108108108107, 'number': 34}	{'precision': 0.9807692307692307, 'recall': 0.9807692307692307, 'f1': 0.9807692307692307, 'number': 52}	{'precision': 0.926829268292683, 'recall': 0.95, 'f1': 0.9382716049382716, 'number': 40}	{'precision': 0.6875, 'recall': 0.6875, 'f1': 0.6875, 'number': 16}	{'precision': 0.9772727272727273, 'recall': 0.7678571428571429, 'f1': 0.86, 'number': 56}	0.9217	0.9056	0.9136	0.9755

Framework versions

Transformers 4.40.1
Pytorch 2.3.0+cu118
Datasets 2.19.0
Tokenizers 0.19.1

Szczotar93/Layoutlm_Inkaso_2

Layoutlm_Inkaso_2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Szczotar93/
Layoutlm_Inkaso_2