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: 0.0801
Creditor address: {'precision': 1.0, 'recall': 0.875, 'f1': 0.9333333333333333, 'number': 48}
Creditor name: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}
Creditor proxy: {'precision': 0.8333333333333334, 'recall': 0.8108108108108109, 'f1': 0.8219178082191781, 'number': 37}
Debtor address: {'precision': 0.9636363636363636, 'recall': 1.0, 'f1': 0.9814814814814815, 'number': 53}
Debtor name: {'precision': 0.9428571428571428, 'recall': 1.0, 'f1': 0.9705882352941176, 'number': 33}
Doc id: {'precision': 0.85, 'recall': 0.8947368421052632, 'f1': 0.8717948717948718, 'number': 19}
Title: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}
Overall Precision: 0.9492
Overall Recall: 0.9419
Overall F1: 0.9455
Overall Accuracy: 0.9831

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
0.4642	6.6667	20	0.2502	{'precision': 0.782608695652174, 'recall': 0.75, 'f1': 0.7659574468085107, 'number': 48}	{'precision': 0.9354838709677419, 'recall': 0.8529411764705882, 'f1': 0.8923076923076922, 'number': 34}	{'precision': 0.8, 'recall': 0.6486486486486487, 'f1': 0.7164179104477612, 'number': 37}	{'precision': 0.8205128205128205, 'recall': 0.6037735849056604, 'f1': 0.6956521739130435, 'number': 53}	{'precision': 0.95, 'recall': 0.5757575757575758, 'f1': 0.7169811320754716, 'number': 33}	{'precision': 1.0, 'recall': 0.2631578947368421, 'f1': 0.4166666666666667, 'number': 19}	{'precision': 0.8461538461538461, 'recall': 0.3235294117647059, 'f1': 0.46808510638297873, 'number': 34}	0.8478	0.6047	0.7059	0.9330
0.1387	13.3333	40	0.0914	{'precision': 1.0, 'recall': 0.9166666666666666, 'f1': 0.9565217391304348, 'number': 48}	{'precision': 0.9714285714285714, 'recall': 1.0, 'f1': 0.9855072463768115, 'number': 34}	{'precision': 0.7777777777777778, 'recall': 0.7567567567567568, 'f1': 0.7671232876712328, 'number': 37}	{'precision': 0.9444444444444444, 'recall': 0.9622641509433962, 'f1': 0.9532710280373832, 'number': 53}	{'precision': 0.8918918918918919, 'recall': 1.0, 'f1': 0.9428571428571428, 'number': 33}	{'precision': 0.8095238095238095, 'recall': 0.8947368421052632, 'f1': 0.8500000000000001, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}	0.9234	0.9341	0.9287	0.9795
0.0431	20.0	60	0.0774	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 48}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}	{'precision': 0.8181818181818182, 'recall': 0.7297297297297297, 'f1': 0.7714285714285715, 'number': 37}	{'precision': 0.9636363636363636, 'recall': 1.0, 'f1': 0.9814814814814815, 'number': 53}	{'precision': 0.9428571428571428, 'recall': 1.0, 'f1': 0.9705882352941176, 'number': 33}	{'precision': 0.7727272727272727, 'recall': 0.8947368421052632, 'f1': 0.8292682926829269, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}	0.9425	0.9535	0.9480	0.9837
0.0216	26.6667	80	0.0842	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 48}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}	{'precision': 0.7631578947368421, 'recall': 0.7837837837837838, 'f1': 0.7733333333333334, 'number': 37}	{'precision': 0.9454545454545454, 'recall': 0.9811320754716981, 'f1': 0.9629629629629629, 'number': 53}	{'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 33}	{'precision': 0.8095238095238095, 'recall': 0.8947368421052632, 'f1': 0.8500000000000001, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}	0.9286	0.9574	0.9427	0.9825
0.0142	33.3333	100	0.0840	{'precision': 1.0, 'recall': 0.875, 'f1': 0.9333333333333333, 'number': 48}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}	{'precision': 0.8333333333333334, 'recall': 0.8108108108108109, 'f1': 0.8219178082191781, 'number': 37}	{'precision': 0.9629629629629629, 'recall': 0.9811320754716981, 'f1': 0.9719626168224299, 'number': 53}	{'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 33}	{'precision': 0.8095238095238095, 'recall': 0.8947368421052632, 'f1': 0.8500000000000001, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}	0.9416	0.9380	0.9398	0.9819
0.0105	40.0	120	0.0838	{'precision': 0.9772727272727273, 'recall': 0.8958333333333334, 'f1': 0.9347826086956522, 'number': 48}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}	{'precision': 0.8333333333333334, 'recall': 0.8108108108108109, 'f1': 0.8219178082191781, 'number': 37}	{'precision': 0.9636363636363636, 'recall': 1.0, 'f1': 0.9814814814814815, 'number': 53}	{'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 33}	{'precision': 0.8095238095238095, 'recall': 0.8947368421052632, 'f1': 0.8500000000000001, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}	0.9385	0.9457	0.9421	0.9819
0.0081	46.6667	140	0.0801	{'precision': 1.0, 'recall': 0.875, 'f1': 0.9333333333333333, 'number': 48}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}	{'precision': 0.8333333333333334, 'recall': 0.8108108108108109, 'f1': 0.8219178082191781, 'number': 37}	{'precision': 0.9636363636363636, 'recall': 1.0, 'f1': 0.9814814814814815, 'number': 53}	{'precision': 0.9428571428571428, 'recall': 1.0, 'f1': 0.9705882352941176, 'number': 33}	{'precision': 0.85, 'recall': 0.8947368421052632, 'f1': 0.8717948717948718, 'number': 19}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 34}	0.9492	0.9419	0.9455	0.9831

Framework versions

Transformers 4.40.1
Pytorch 2.3.0+cu118
Datasets 2.19.0
Tokenizers 0.19.1

Szczotar93/Inkaso_beta

Inkaso_beta

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Szczotar93/
Inkaso_beta