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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: 0.0273
- Criterio Julgamento Precision: 0.7925
- Criterio Julgamento Recall: 0.8077
- Criterio Julgamento F1: 0.8000
- Criterio Julgamento Number: 104
- Data Sessao Precision: 0.7246
- Data Sessao Recall: 0.9091
- Data Sessao F1: 0.8065
- Data Sessao Number: 55
- Modalidade Licitacao Precision: 0.9485
- Modalidade Licitacao Recall: 0.9620
- Modalidade Licitacao F1: 0.9552
- Modalidade Licitacao Number: 421
- Numero Exercicio Precision: 0.9282
- Numero Exercicio Recall: 0.9784
- Numero Exercicio F1: 0.9526
- Numero Exercicio Number: 185
- Objeto Licitacao Precision: 0.56
- Objeto Licitacao Recall: 0.7119
- Objeto Licitacao F1: 0.6269
- Objeto Licitacao Number: 59
- Valor Objeto Precision: 0.8409
- Valor Objeto Recall: 0.9024
- Valor Objeto F1: 0.8706
- Valor Objeto Number: 41
- Overall Precision: 0.8723
- Overall Recall: 0.9237
- Overall F1: 0.8972
- Overall Accuracy: 0.9964
## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0168 | 0.96 | 2750 | 0.0169 | 0.7016 | 0.8365 | 0.7632 | 104 | 0.6707 | 1.0 | 0.8029 | 55 | 0.9424 | 0.9715 | 0.9567 | 421 | 0.9110 | 0.9405 | 0.9255 | 185 | 0.3304 | 0.6271 | 0.4327 | 59 | 0.76 | 0.9268 | 0.8352 | 41 | 0.8056 | 0.9249 | 0.8611 | 0.9950 |
| 0.0164 | 1.92 | 5500 | 0.0125 | 0.7565 | 0.8365 | 0.7945 | 104 | 0.6923 | 0.9818 | 0.8120 | 55 | 0.9491 | 0.9739 | 0.9613 | 421 | 0.9375 | 0.9730 | 0.9549 | 185 | 0.4138 | 0.6102 | 0.4932 | 59 | 0.8085 | 0.9268 | 0.8636 | 41 | 0.8465 | 0.9306 | 0.8866 | 0.9965 |
| 0.0076 | 2.88 | 8250 | 0.0204 | 0.7184 | 0.7115 | 0.7150 | 104 | 0.8070 | 0.8364 | 0.8214 | 55 | 0.9468 | 0.9715 | 0.9590 | 421 | 0.9282 | 0.9784 | 0.9526 | 185 | 0.4783 | 0.5593 | 0.5156 | 59 | 0.7209 | 0.7561 | 0.7381 | 41 | 0.8610 | 0.8948 | 0.8776 | 0.9961 |
| 0.0067 | 3.84 | 11000 | 0.0168 | 0.7589 | 0.8173 | 0.7870 | 104 | 0.8 | 0.8 | 0.8000 | 55 | 0.9487 | 0.9667 | 0.9576 | 421 | 0.9319 | 0.9622 | 0.9468 | 185 | 0.5309 | 0.7288 | 0.6143 | 59 | 0.8636 | 0.9268 | 0.8941 | 41 | 0.8717 | 0.9191 | 0.8948 | 0.9965 |
| 0.0043 | 4.8 | 13750 | 0.0144 | 0.736 | 0.8846 | 0.8035 | 104 | 0.8033 | 0.8909 | 0.8448 | 55 | 0.9512 | 0.9715 | 0.9612 | 421 | 0.9316 | 0.9568 | 0.944 | 185 | 0.5135 | 0.6441 | 0.5714 | 59 | 0.8444 | 0.9268 | 0.8837 | 41 | 0.8681 | 0.9283 | 0.8972 | 0.9967 |
| 0.0072 | 5.76 | 16500 | 0.0161 | 0.8091 | 0.8558 | 0.8318 | 104 | 0.7237 | 1.0 | 0.8397 | 55 | 0.9487 | 0.9667 | 0.9576 | 421 | 0.9326 | 0.9730 | 0.9524 | 185 | 0.4318 | 0.6441 | 0.5170 | 59 | 0.8222 | 0.9024 | 0.8605 | 41 | 0.8565 | 0.9318 | 0.8926 | 0.9966 |
| 0.003 | 6.72 | 19250 | 0.0205 | 0.7719 | 0.8462 | 0.8073 | 104 | 0.7812 | 0.9091 | 0.8403 | 55 | 0.9507 | 0.9620 | 0.9563 | 421 | 0.9375 | 0.9730 | 0.9549 | 185 | 0.5309 | 0.7288 | 0.6143 | 59 | 0.8409 | 0.9024 | 0.8706 | 41 | 0.8719 | 0.9283 | 0.8992 | 0.9967 |
| 0.0033 | 7.68 | 22000 | 0.0197 | 0.7736 | 0.7885 | 0.7810 | 104 | 0.7463 | 0.9091 | 0.8197 | 55 | 0.9466 | 0.9691 | 0.9577 | 421 | 0.9227 | 0.9676 | 0.9446 | 185 | 0.5286 | 0.6271 | 0.5736 | 59 | 0.7442 | 0.7805 | 0.7619 | 41 | 0.8650 | 0.9110 | 0.8874 | 0.9964 |
| 0.0043 | 8.64 | 24750 | 0.0250 | 0.7607 | 0.8558 | 0.8054 | 104 | 0.7612 | 0.9273 | 0.8361 | 55 | 0.9400 | 0.9667 | 0.9532 | 421 | 0.9427 | 0.9784 | 0.9602 | 185 | 0.5479 | 0.6780 | 0.6061 | 59 | 0.8043 | 0.9024 | 0.8506 | 41 | 0.8675 | 0.9306 | 0.8979 | 0.9965 |
| 0.0014 | 9.61 | 27500 | 0.0257 | 0.8018 | 0.8558 | 0.8279 | 104 | 0.7391 | 0.9273 | 0.8226 | 55 | 0.9417 | 0.9596 | 0.9506 | 421 | 0.9372 | 0.9676 | 0.9521 | 185 | 0.5143 | 0.6102 | 0.5581 | 59 | 0.8 | 0.8780 | 0.8372 | 41 | 0.8689 | 0.9191 | 0.8933 | 0.9966 |
| 0.0025 | 10.57 | 30250 | 0.0258 | 0.7798 | 0.8173 | 0.7981 | 104 | 0.7424 | 0.8909 | 0.8099 | 55 | 0.9465 | 0.9667 | 0.9565 | 421 | 0.9424 | 0.9730 | 0.9574 | 185 | 0.5352 | 0.6441 | 0.5846 | 59 | 0.8222 | 0.9024 | 0.8605 | 41 | 0.8728 | 0.9202 | 0.8959 | 0.9963 |
| 0.0016 | 11.53 | 33000 | 0.0273 | 0.7925 | 0.8077 | 0.8000 | 104 | 0.7246 | 0.9091 | 0.8065 | 55 | 0.9485 | 0.9620 | 0.9552 | 421 | 0.9282 | 0.9784 | 0.9526 | 185 | 0.56 | 0.7119 | 0.6269 | 59 | 0.8409 | 0.9024 | 0.8706 | 41 | 0.8723 | 0.9237 | 0.8972 | 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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