HenriqueLin/pt-property-record-layoutlmv3-ner

property_record

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

• Loss: 0.0036
• Article Number: {'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}
• Building Number: {'precision': 0.9876543209876543, 'recall': 1.0, 'f1': 0.9937888198757764, 'number': 80}
• Coord X: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 41}
• Coord Y: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 41}
• Distrito: {'precision': 1.0, 'recall': 0.9818181818181818, 'f1': 0.9908256880733944, 'number': 55}
• Distrito Code: {'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}
• Floor Division: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79}
• Locality: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 88}
• Municipality: {'precision': 0.9833333333333333, 'recall': 0.9672131147540983, 'f1': 0.9752066115702478, 'number': 61}
• Municipality Code: {'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}
• Owner Name: {'precision': 0.98, 'recall': 1.0, 'f1': 0.98989898989899, 'number': 98}
• Owner Nif: {'precision': 0.9880952380952381, 'recall': 1.0, 'f1': 0.9940119760479043, 'number': 83}
• Parish: {'precision': 0.9864864864864865, 'recall': 0.9733333333333334, 'f1': 0.9798657718120806, 'number': 75}
• Parish Code: {'precision': 0.9814814814814815, 'recall': 0.9814814814814815, 'f1': 0.9814814814814815, 'number': 54}
• Postal Code: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 80}
• Property Fraction: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 75}
• Registry City: {'precision': 1.0, 'recall': 0.9818181818181818, 'f1': 0.9908256880733944, 'number': 55}
• Registry Number: {'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}
• Street Name: {'precision': 0.9901960784313726, 'recall': 0.9901960784313726, 'f1': 0.9901960784313726, 'number': 102}
• Overall Precision: 0.9937
• Overall Recall: 0.9906
• Overall F1: 0.9922
• Overall Accuracy: 0.9994

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: 2e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 50
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Article Number	Building Number	Coord X	Coord Y	Distrito	Distrito Code	Floor Division	Locality	Municipality	Municipality Code	Owner Name	Owner Nif	Parish	Parish Code	Postal Code	Property Fraction	Registry City	Registry Number	Street Name	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0098	1.0	769	0.0090	{'precision': 1.0, 'recall': 0.9629629629629629, 'f1': 0.9811320754716981, 'number': 54}	{'precision': 1.0, 'recall': 0.9875, 'f1': 0.9937106918238994, 'number': 80}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 41}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 41}	{'precision': 0.9629629629629629, 'recall': 0.9454545454545454, 'f1': 0.9541284403669724, 'number': 55}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 88}	{'precision': 0.95, 'recall': 0.9344262295081968, 'f1': 0.9421487603305784, 'number': 61}	{'precision': 0.9814814814814815, 'recall': 0.9814814814814815, 'f1': 0.9814814814814815, 'number': 54}	{'precision': 0.9607843137254902, 'recall': 1.0, 'f1': 0.98, 'number': 98}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 83}	{'precision': 0.948051948051948, 'recall': 0.9733333333333334, 'f1': 0.9605263157894737, 'number': 75}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 80}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 75}	{'precision': 1.0, 'recall': 0.9818181818181818, 'f1': 0.9908256880733944, 'number': 55}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 54}	{'precision': 0.9595959595959596, 'recall': 0.9313725490196079, 'f1': 0.945273631840796, 'number': 102}	0.9859	0.9821	0.9840	0.9991
0.0024	2.0	1538	0.0050	{'precision': 0.9814814814814815, 'recall': 0.9814814814814815, 'f1': 0.9814814814814815, 'number': 54}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 80}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 41}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 41}	{'precision': 0.9818181818181818, 'recall': 0.9818181818181818, 'f1': 0.9818181818181818, 'number': 55}	{'precision': 0.9814814814814815, 'recall': 0.9814814814814815, 'f1': 0.9814814814814815, 'number': 54}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 88}	{'precision': 0.9655172413793104, 'recall': 0.9180327868852459, 'f1': 0.9411764705882353, 'number': 61}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 0.98, 'recall': 1.0, 'f1': 0.98989898989899, 'number': 98}	{'precision': 1.0, 'recall': 0.9879518072289156, 'f1': 0.993939393939394, 'number': 83}	{'precision': 0.972972972972973, 'recall': 0.96, 'f1': 0.9664429530201343, 'number': 75}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 54}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 80}	{'precision': 1.0, 'recall': 0.9866666666666667, 'f1': 0.9932885906040269, 'number': 75}	{'precision': 1.0, 'recall': 0.9818181818181818, 'f1': 0.9908256880733944, 'number': 55}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 1.0, 'recall': 0.9901960784313726, 'f1': 0.9950738916256158, 'number': 102}	0.9929	0.9867	0.9898	0.9994
0.0012	3.0	2307	0.0061	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 0.9875, 'recall': 0.9875, 'f1': 0.9875, 'number': 80}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 41}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 41}	{'precision': 1.0, 'recall': 0.9636363636363636, 'f1': 0.9814814814814815, 'number': 55}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 88}	{'precision': 0.9672131147540983, 'recall': 0.9672131147540983, 'f1': 0.9672131147540983, 'number': 61}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 98}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 83}	{'precision': 1.0, 'recall': 0.9733333333333334, 'f1': 0.9864864864864865, 'number': 75}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 80}	{'precision': 1.0, 'recall': 0.9866666666666667, 'f1': 0.9932885906040269, 'number': 75}	{'precision': 1.0, 'recall': 0.9818181818181818, 'f1': 0.9908256880733944, 'number': 55}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 1.0, 'recall': 0.9901960784313726, 'f1': 0.9950738916256158, 'number': 102}	0.9976	0.9883	0.9930	0.9994
0.0032	4.0	3076	0.0041	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 0.9876543209876543, 'recall': 1.0, 'f1': 0.9937888198757764, 'number': 80}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 41}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 41}	{'precision': 0.9821428571428571, 'recall': 1.0, 'f1': 0.9909909909909909, 'number': 55}	{'precision': 0.9814814814814815, 'recall': 0.9814814814814815, 'f1': 0.9814814814814815, 'number': 54}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 88}	{'precision': 0.9827586206896551, 'recall': 0.9344262295081968, 'f1': 0.9579831932773109, 'number': 61}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 0.98, 'recall': 1.0, 'f1': 0.98989898989899, 'number': 98}	{'precision': 0.9880952380952381, 'recall': 1.0, 'f1': 0.9940119760479043, 'number': 83}	{'precision': 0.9605263157894737, 'recall': 0.9733333333333334, 'f1': 0.9668874172185431, 'number': 75}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 80}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 75}	{'precision': 1.0, 'recall': 0.9818181818181818, 'f1': 0.9908256880733944, 'number': 55}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 0.9901960784313726, 'recall': 0.9901960784313726, 'f1': 0.9901960784313726, 'number': 102}	0.9914	0.9899	0.9906	0.9993
0.0009	5.0	3845	0.0036	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 0.9876543209876543, 'recall': 1.0, 'f1': 0.9937888198757764, 'number': 80}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 41}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 41}	{'precision': 1.0, 'recall': 0.9818181818181818, 'f1': 0.9908256880733944, 'number': 55}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 79}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 88}	{'precision': 0.9833333333333333, 'recall': 0.9672131147540983, 'f1': 0.9752066115702478, 'number': 61}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 0.98, 'recall': 1.0, 'f1': 0.98989898989899, 'number': 98}	{'precision': 0.9880952380952381, 'recall': 1.0, 'f1': 0.9940119760479043, 'number': 83}	{'precision': 0.9864864864864865, 'recall': 0.9733333333333334, 'f1': 0.9798657718120806, 'number': 75}	{'precision': 0.9814814814814815, 'recall': 0.9814814814814815, 'f1': 0.9814814814814815, 'number': 54}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 80}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 75}	{'precision': 1.0, 'recall': 0.9818181818181818, 'f1': 0.9908256880733944, 'number': 55}	{'precision': 1.0, 'recall': 0.9814814814814815, 'f1': 0.9906542056074767, 'number': 54}	{'precision': 0.9901960784313726, 'recall': 0.9901960784313726, 'f1': 0.9901960784313726, 'number': 102}	0.9937	0.9906	0.9922	0.9994

Framework versions

• Transformers 4.51.3
• Pytorch 2.7.1+cu128
• Datasets 3.5.1
• Tokenizers 0.21.1