Loss: 0.0009
Address: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 116}
Article Number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}
Certificate Number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}
Co2 Emissions: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}
Energy Class: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 118}
Gps: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 113}
Last Updated: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 76}
Locality: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 105}
Municipality: {'precision': 1.0, 'recall': 0.9904761904761905, 'f1': 0.9952153110047847, 'number': 105}
Parish: {'precision': 0.9905660377358491, 'recall': 1.0, 'f1': 0.9952606635071091, 'number': 105}
Performance Index: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 113}
Property Fraction: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}
Registry City: {'precision': 0.96875, 'recall': 0.9789473684210527, 'f1': 0.9738219895287958, 'number': 95}
Registry Number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}
Renewable Energy: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}
Valid Until: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}
Overall Precision: 0.9976
Overall Recall: 0.9982
Overall F1: 0.9979
Overall Accuracy: 0.9999

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	Address	Article Number	Certificate Number	Co2 Emissions	Energy Class	Gps	Last Updated	Locality	Municipality	Parish	Performance Index	Property Fraction	Registry City	Registry Number	Renewable Energy	Valid Until	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0071	1.0	799	0.0047	{'precision': 0.9827586206896551, 'recall': 0.9827586206896551, 'f1': 0.9827586206896551, 'number': 116}	{'precision': 0.9900990099009901, 'recall': 1.0, 'f1': 0.9950248756218906, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 118}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 113}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 76}	{'precision': 0.9905660377358491, 'recall': 1.0, 'f1': 0.9952606635071091, 'number': 105}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 105}	{'precision': 1.0, 'recall': 0.9904761904761905, 'f1': 0.9952153110047847, 'number': 105}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 113}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 0.9574468085106383, 'recall': 0.9473684210526315, 'f1': 0.9523809523809523, 'number': 95}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 0.9900990099009901, 'recall': 1.0, 'f1': 0.9950248756218906, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	0.9945	0.9951	0.9948	0.9996
0.0017	2.0	1598	0.0040	{'precision': 0.9666666666666667, 'recall': 1.0, 'f1': 0.983050847457627, 'number': 116}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 118}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 113}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 76}	{'precision': 1.0, 'recall': 0.9523809523809523, 'f1': 0.975609756097561, 'number': 105}	{'precision': 1.0, 'recall': 0.9809523809523809, 'f1': 0.9903846153846153, 'number': 105}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 105}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 113}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 0.9270833333333334, 'recall': 0.9368421052631579, 'f1': 0.9319371727748692, 'number': 95}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 0.9900990099009901, 'recall': 1.0, 'f1': 0.9950248756218906, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	0.9909	0.9921	0.9915	0.9995
0.0007	3.0	2397	0.0015	{'precision': 1.0, 'recall': 0.9913793103448276, 'f1': 0.9956709956709957, 'number': 116}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 118}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 113}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 76}	{'precision': 0.9904761904761905, 'recall': 0.9904761904761905, 'f1': 0.9904761904761905, 'number': 105}	{'precision': 0.9904761904761905, 'recall': 0.9904761904761905, 'f1': 0.9904761904761905, 'number': 105}	{'precision': 0.9904761904761905, 'recall': 0.9904761904761905, 'f1': 0.9904761904761905, 'number': 105}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 113}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 0.9587628865979382, 'recall': 0.9789473684210527, 'f1': 0.96875, 'number': 95}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	0.9957	0.9964	0.9961	0.9998
0.0007	4.0	3196	0.0012	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 116}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 118}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 113}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 76}	{'precision': 0.9903846153846154, 'recall': 0.9809523809523809, 'f1': 0.985645933014354, 'number': 105}	{'precision': 1.0, 'recall': 0.9904761904761905, 'f1': 0.9952153110047847, 'number': 105}	{'precision': 0.9722222222222222, 'recall': 1.0, 'f1': 0.9859154929577464, 'number': 105}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 113}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 0.9787234042553191, 'recall': 0.968421052631579, 'f1': 0.9735449735449735, 'number': 95}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	0.9964	0.9964	0.9964	0.9998
0.0022	5.0	3995	0.0009	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 116}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 118}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 113}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 76}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 105}	{'precision': 1.0, 'recall': 0.9904761904761905, 'f1': 0.9952153110047847, 'number': 105}	{'precision': 0.9905660377358491, 'recall': 1.0, 'f1': 0.9952606635071091, 'number': 105}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 113}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 0.96875, 'recall': 0.9789473684210527, 'f1': 0.9738219895287958, 'number': 95}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 100}	0.9976	0.9982	0.9979	0.9999

Framework versions

Transformers 4.51.3
Pytorch 2.7.1+cu128
Datasets 3.5.1
Tokenizers 0.21.1

HenriqueLin/pt-energy-certificate-layoutlmv3-ner

energy_certificate

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

HenriqueLin/
pt-energy-certificate-layoutlmv3-ner