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bert_base_tcm_teste

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@@ -14,35 +14,35 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0155
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- - Criterio Julgamento Precision: 0.7965
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- - Criterio Julgamento Recall: 0.8654
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- - Criterio Julgamento F1: 0.8295
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  - Criterio Julgamento Number: 104
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- - Data Sessao Precision: 0.7162
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- - Data Sessao Recall: 0.9636
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- - Data Sessao F1: 0.8217
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  - Data Sessao Number: 55
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- - Modalidade Licitacao Precision: 0.9554
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- - Modalidade Licitacao Recall: 0.9667
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- - Modalidade Licitacao F1: 0.9610
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  - Modalidade Licitacao Number: 421
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- - Numero Exercicio Precision: 0.9323
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- - Numero Exercicio Recall: 0.9676
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- - Numero Exercicio F1: 0.9496
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  - Numero Exercicio Number: 185
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- - Objeto Licitacao Precision: 0.5270
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- - Objeto Licitacao Recall: 0.6610
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- - Objeto Licitacao F1: 0.5865
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  - Objeto Licitacao Number: 59
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- - Valor Objeto Precision: 0.8444
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- - Valor Objeto Recall: 0.9268
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- - Valor Objeto F1: 0.8837
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  - Valor Objeto Number: 41
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- - Overall Precision: 0.8723
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- - Overall Recall: 0.9318
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- - Overall F1: 0.9011
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- - Overall Accuracy: 0.9966
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  ## Model description
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@@ -61,7 +61,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-06
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
@@ -73,17 +73,14 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Criterio Julgamento Precision | Criterio Julgamento Recall | Criterio Julgamento F1 | Criterio Julgamento Number | Data Sessao Precision | Data Sessao Recall | Data Sessao F1 | Data Sessao Number | Modalidade Licitacao Precision | Modalidade Licitacao Recall | Modalidade Licitacao F1 | Modalidade Licitacao Number | Numero Exercicio Precision | Numero Exercicio Recall | Numero Exercicio F1 | Numero Exercicio Number | Objeto Licitacao Precision | Objeto Licitacao Recall | Objeto Licitacao F1 | Objeto Licitacao Number | Valor Objeto Precision | Valor Objeto Recall | Valor Objeto F1 | Valor Objeto Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 0.0193 | 0.96 | 2750 | 0.0190 | 0.7016 | 0.8365 | 0.7632 | 104 | 0.6585 | 0.9818 | 0.7883 | 55 | 0.9446 | 0.9715 | 0.9578 | 421 | 0.9036 | 0.9622 | 0.9319 | 185 | 0.2261 | 0.4407 | 0.2989 | 59 | 0.7 | 0.8537 | 0.7692 | 41 | 0.7882 | 0.9121 | 0.8457 | 0.9946 |
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- | 0.0165 | 1.92 | 5500 | 0.0133 | 0.7203 | 0.8173 | 0.7658 | 104 | 0.675 | 0.9818 | 0.8 | 55 | 0.9447 | 0.9739 | 0.9591 | 421 | 0.9430 | 0.9838 | 0.9630 | 185 | 0.4691 | 0.6441 | 0.5429 | 59 | 0.8043 | 0.9024 | 0.8506 | 41 | 0.8466 | 0.9318 | 0.8872 | 0.9964 |
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- | 0.0089 | 2.88 | 8250 | 0.0150 | 0.7636 | 0.8077 | 0.7850 | 104 | 0.7895 | 0.8182 | 0.8036 | 55 | 0.9491 | 0.9739 | 0.9613 | 421 | 0.9282 | 0.9784 | 0.9526 | 185 | 0.4444 | 0.6102 | 0.5143 | 59 | 0.8636 | 0.9268 | 0.8941 | 41 | 0.8640 | 0.9179 | 0.8901 | 0.9965 |
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- | 0.0066 | 3.84 | 11000 | 0.0150 | 0.7692 | 0.8654 | 0.8145 | 104 | 0.7333 | 0.8 | 0.7652 | 55 | 0.9464 | 0.9644 | 0.9553 | 421 | 0.9278 | 0.9730 | 0.9499 | 185 | 0.5 | 0.6780 | 0.5755 | 59 | 0.7708 | 0.9024 | 0.8315 | 41 | 0.8588 | 0.9214 | 0.8890 | 0.9966 |
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- | 0.0055 | 4.8 | 13750 | 0.0176 | 0.75 | 0.8654 | 0.8036 | 104 | 0.7903 | 0.8909 | 0.8376 | 55 | 0.9490 | 0.9715 | 0.9601 | 421 | 0.9326 | 0.9730 | 0.9524 | 185 | 0.4568 | 0.6271 | 0.5286 | 59 | 0.7872 | 0.9024 | 0.8409 | 41 | 0.8587 | 0.9272 | 0.8916 | 0.9963 |
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- | 0.0066 | 5.76 | 16500 | 0.0155 | 0.7965 | 0.8654 | 0.8295 | 104 | 0.7162 | 0.9636 | 0.8217 | 55 | 0.9554 | 0.9667 | 0.9610 | 421 | 0.9323 | 0.9676 | 0.9496 | 185 | 0.5270 | 0.6610 | 0.5865 | 59 | 0.8444 | 0.9268 | 0.8837 | 41 | 0.8723 | 0.9318 | 0.9011 | 0.9966 |
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- | 0.0031 | 6.72 | 19250 | 0.0181 | 0.775 | 0.8942 | 0.8304 | 104 | 0.7879 | 0.9455 | 0.8595 | 55 | 0.9533 | 0.9691 | 0.9611 | 421 | 0.9326 | 0.9730 | 0.9524 | 185 | 0.4875 | 0.6610 | 0.5612 | 59 | 0.8261 | 0.9268 | 0.8736 | 41 | 0.8682 | 0.9364 | 0.9010 | 0.9965 |
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- | 0.0066 | 7.68 | 22000 | 0.0192 | 0.7798 | 0.8173 | 0.7981 | 104 | 0.6986 | 0.9273 | 0.7969 | 55 | 0.9353 | 0.9620 | 0.9485 | 421 | 0.8995 | 0.9676 | 0.9323 | 185 | 0.4 | 0.5763 | 0.4722 | 59 | 0.7551 | 0.9024 | 0.8222 | 41 | 0.8344 | 0.9145 | 0.8726 | 0.9961 |
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- | 0.0052 | 8.64 | 24750 | 0.0201 | 0.8036 | 0.8654 | 0.8333 | 104 | 0.7869 | 0.8727 | 0.8276 | 55 | 0.9465 | 0.9667 | 0.9565 | 421 | 0.9326 | 0.9730 | 0.9524 | 185 | 0.5060 | 0.7119 | 0.5915 | 59 | 0.8043 | 0.9024 | 0.8506 | 41 | 0.8692 | 0.9295 | 0.8983 | 0.9966 |
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- | 0.0015 | 9.61 | 27500 | 0.0202 | 0.7838 | 0.8365 | 0.8093 | 104 | 0.7313 | 0.8909 | 0.8033 | 55 | 0.9482 | 0.9572 | 0.9527 | 421 | 0.9326 | 0.9730 | 0.9524 | 185 | 0.4865 | 0.6102 | 0.5414 | 59 | 0.8043 | 0.9024 | 0.8506 | 41 | 0.8646 | 0.9156 | 0.8894 | 0.9966 |
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- | 0.0015 | 10.57 | 30250 | 0.0225 | 0.7798 | 0.8173 | 0.7981 | 104 | 0.6912 | 0.8545 | 0.7642 | 55 | 0.9508 | 0.9644 | 0.9575 | 421 | 0.9375 | 0.9730 | 0.9549 | 185 | 0.5395 | 0.6949 | 0.6074 | 59 | 0.8478 | 0.9512 | 0.8966 | 41 | 0.8693 | 0.9225 | 0.8951 | 0.9964 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0328
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+ - Criterio Julgamento Precision: 0.675
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+ - Criterio Julgamento Recall: 0.7788
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+ - Criterio Julgamento F1: 0.7232
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  - Criterio Julgamento Number: 104
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+ - Data Sessao Precision: 0.6604
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+ - Data Sessao Recall: 0.6364
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+ - Data Sessao F1: 0.6481
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  - Data Sessao Number: 55
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+ - Modalidade Licitacao Precision: 0.9263
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+ - Modalidade Licitacao Recall: 0.9549
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+ - Modalidade Licitacao F1: 0.9404
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  - Modalidade Licitacao Number: 421
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+ - Numero Exercicio Precision: 0.8535
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+ - Numero Exercicio Recall: 0.9135
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+ - Numero Exercicio F1: 0.8825
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  - Numero Exercicio Number: 185
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+ - Objeto Licitacao Precision: 0.2471
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+ - Objeto Licitacao Recall: 0.3559
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+ - Objeto Licitacao F1: 0.2917
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  - Objeto Licitacao Number: 59
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+ - Valor Objeto Precision: 0.5091
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+ - Valor Objeto Recall: 0.6829
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+ - Valor Objeto F1: 0.5833
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  - Valor Objeto Number: 41
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+ - Overall Precision: 0.7788
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+ - Overall Recall: 0.8509
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+ - Overall F1: 0.8133
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+ - Overall Accuracy: 0.9948
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Criterio Julgamento Precision | Criterio Julgamento Recall | Criterio Julgamento F1 | Criterio Julgamento Number | Data Sessao Precision | Data Sessao Recall | Data Sessao F1 | Data Sessao Number | Modalidade Licitacao Precision | Modalidade Licitacao Recall | Modalidade Licitacao F1 | Modalidade Licitacao Number | Numero Exercicio Precision | Numero Exercicio Recall | Numero Exercicio F1 | Numero Exercicio Number | Objeto Licitacao Precision | Objeto Licitacao Recall | Objeto Licitacao F1 | Objeto Licitacao Number | Valor Objeto Precision | Valor Objeto Recall | Valor Objeto F1 | Valor Objeto Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.0346 | 0.96 | 2750 | 0.0329 | 0.6154 | 0.8462 | 0.7126 | 104 | 0.5495 | 0.9091 | 0.6849 | 55 | 0.8482 | 0.9287 | 0.8866 | 421 | 0.7438 | 0.9730 | 0.8431 | 185 | 0.0525 | 0.3220 | 0.0903 | 59 | 0.4762 | 0.7317 | 0.5769 | 41 | 0.5565 | 0.8763 | 0.6807 | 0.9880 |
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+ | 0.0309 | 1.92 | 5500 | 0.0322 | 0.6694 | 0.7788 | 0.72 | 104 | 0.5976 | 0.8909 | 0.7153 | 55 | 0.9178 | 0.9549 | 0.9360 | 421 | 0.8211 | 0.8432 | 0.8320 | 185 | 0.15 | 0.2034 | 0.1727 | 59 | 0.2203 | 0.3171 | 0.26 | 41 | 0.7351 | 0.8243 | 0.7771 | 0.9934 |
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+ | 0.0179 | 2.88 | 8250 | 0.0192 | 0.7209 | 0.8942 | 0.7983 | 104 | 0.6351 | 0.8545 | 0.7287 | 55 | 0.9224 | 0.9596 | 0.9406 | 421 | 0.8872 | 0.9351 | 0.9105 | 185 | 0.2348 | 0.4576 | 0.3103 | 59 | 0.5424 | 0.7805 | 0.64 | 41 | 0.7683 | 0.8971 | 0.8277 | 0.9948 |
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+ | 0.0174 | 3.84 | 11000 | 0.0320 | 0.7522 | 0.8173 | 0.7834 | 104 | 0.5741 | 0.5636 | 0.5688 | 55 | 0.8881 | 0.9430 | 0.9147 | 421 | 0.8490 | 0.8811 | 0.8647 | 185 | 0.2436 | 0.3220 | 0.2774 | 59 | 0.5370 | 0.7073 | 0.6105 | 41 | 0.7719 | 0.8370 | 0.8031 | 0.9946 |
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+ | 0.0192 | 4.8 | 13750 | 0.0261 | 0.6744 | 0.8365 | 0.7468 | 104 | 0.6190 | 0.7091 | 0.6610 | 55 | 0.9169 | 0.9430 | 0.9297 | 421 | 0.8404 | 0.8541 | 0.8472 | 185 | 0.2059 | 0.3559 | 0.2609 | 59 | 0.5088 | 0.7073 | 0.5918 | 41 | 0.7521 | 0.8451 | 0.7959 | 0.9949 |
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+ | 0.0158 | 5.76 | 16500 | 0.0250 | 0.6641 | 0.8173 | 0.7328 | 104 | 0.5610 | 0.8364 | 0.6715 | 55 | 0.9199 | 0.9549 | 0.9371 | 421 | 0.9167 | 0.9514 | 0.9337 | 185 | 0.1912 | 0.4407 | 0.2667 | 59 | 0.4828 | 0.6829 | 0.5657 | 41 | 0.7386 | 0.8821 | 0.8040 | 0.9948 |
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+ | 0.0126 | 6.72 | 19250 | 0.0267 | 0.6694 | 0.7981 | 0.7281 | 104 | 0.6386 | 0.9636 | 0.7681 | 55 | 0.8723 | 0.9572 | 0.9128 | 421 | 0.8812 | 0.9622 | 0.9199 | 185 | 0.2180 | 0.4915 | 0.3021 | 59 | 0.5323 | 0.8049 | 0.6408 | 41 | 0.7308 | 0.9006 | 0.8068 | 0.9945 |
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+ | 0.0162 | 7.68 | 22000 | 0.0328 | 0.675 | 0.7788 | 0.7232 | 104 | 0.6604 | 0.6364 | 0.6481 | 55 | 0.9263 | 0.9549 | 0.9404 | 421 | 0.8535 | 0.9135 | 0.8825 | 185 | 0.2471 | 0.3559 | 0.2917 | 59 | 0.5091 | 0.6829 | 0.5833 | 41 | 0.7788 | 0.8509 | 0.8133 | 0.9948 |
 
 
 
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  ### Framework versions