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---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bert_base_tcm_teste
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_tcm_teste
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.
It achieves the following results on the evaluation set:
- Loss: 0.0155
- Criterio Julgamento Precision: 0.7965
- Criterio Julgamento Recall: 0.8654
- Criterio Julgamento F1: 0.8295
- Criterio Julgamento Number: 104
- Data Sessao Precision: 0.7162
- Data Sessao Recall: 0.9636
- Data Sessao F1: 0.8217
- Data Sessao Number: 55
- Modalidade Licitacao Precision: 0.9554
- Modalidade Licitacao Recall: 0.9667
- Modalidade Licitacao F1: 0.9610
- Modalidade Licitacao Number: 421
- Numero Exercicio Precision: 0.9323
- Numero Exercicio Recall: 0.9676
- Numero Exercicio F1: 0.9496
- Numero Exercicio Number: 185
- Objeto Licitacao Precision: 0.5270
- Objeto Licitacao Recall: 0.6610
- Objeto Licitacao F1: 0.5865
- Objeto Licitacao Number: 59
- Valor Objeto Precision: 0.8444
- Valor Objeto Recall: 0.9268
- Valor Objeto F1: 0.8837
- Valor Objeto Number: 41
- Overall Precision: 0.8723
- Overall Recall: 0.9318
- Overall F1: 0.9011
- Overall Accuracy: 0.9966
## 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50.0
### Training results
| 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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