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---
license: mit
tags:
- generated_from_trainer
base_model: neuralmind/bert-base-portuguese-cased
model-index:
- name: bert_base_tcm_0.6
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_0.6
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: 0.0193
- Criterio Julgamento Precision: 0.8875
- Criterio Julgamento Recall: 0.8659
- Criterio Julgamento F1: 0.8765
- Criterio Julgamento Number: 82
- Data Sessao Precision: 0.7571
- Data Sessao Recall: 0.9636
- Data Sessao F1: 0.848
- Data Sessao Number: 55
- Modalidade Licitacao Precision: 0.9394
- Modalidade Licitacao Recall: 0.9718
- Modalidade Licitacao F1: 0.9553
- Modalidade Licitacao Number: 319
- Numero Exercicio Precision: 0.9172
- Numero Exercicio Recall: 0.9688
- Numero Exercicio F1: 0.9422
- Numero Exercicio Number: 160
- Objeto Licitacao Precision: 0.4659
- Objeto Licitacao Recall: 0.7069
- Objeto Licitacao F1: 0.5616
- Objeto Licitacao Number: 58
- Valor Objeto Precision: 0.8333
- Valor Objeto Recall: 0.9211
- Valor Objeto F1: 0.875
- Valor Objeto Number: 38
- Overall Precision: 0.8537
- Overall Recall: 0.9340
- Overall F1: 0.8920
- Overall Accuracy: 0.9951
## 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-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: 10.0
### 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0252 | 1.0 | 1963 | 0.0202 | 0.8022 | 0.8902 | 0.8439 | 82 | 0.7391 | 0.9273 | 0.8226 | 55 | 0.9233 | 0.9812 | 0.9514 | 319 | 0.8966 | 0.975 | 0.9341 | 160 | 0.4730 | 0.6034 | 0.5303 | 58 | 0.7083 | 0.8947 | 0.7907 | 38 | 0.8327 | 0.9298 | 0.8786 | 0.9948 |
| 0.0191 | 2.0 | 3926 | 0.0226 | 0.8554 | 0.8659 | 0.8606 | 82 | 0.5641 | 0.4 | 0.4681 | 55 | 0.9572 | 0.9812 | 0.9690 | 319 | 0.9273 | 0.9563 | 0.9415 | 160 | 0.3770 | 0.3966 | 0.3866 | 58 | 0.8571 | 0.7895 | 0.8219 | 38 | 0.8620 | 0.8596 | 0.8608 | 0.9951 |
| 0.0137 | 3.0 | 5889 | 0.0193 | 0.8875 | 0.8659 | 0.8765 | 82 | 0.7571 | 0.9636 | 0.848 | 55 | 0.9394 | 0.9718 | 0.9553 | 319 | 0.9172 | 0.9688 | 0.9422 | 160 | 0.4659 | 0.7069 | 0.5616 | 58 | 0.8333 | 0.9211 | 0.875 | 38 | 0.8537 | 0.9340 | 0.8920 | 0.9951 |
| 0.0082 | 4.0 | 7852 | 0.0210 | 0.8780 | 0.8780 | 0.8780 | 82 | 0.7966 | 0.8545 | 0.8246 | 55 | 0.9512 | 0.9781 | 0.9645 | 319 | 0.9023 | 0.9812 | 0.9401 | 160 | 0.5385 | 0.6034 | 0.5691 | 58 | 0.9 | 0.9474 | 0.9231 | 38 | 0.8810 | 0.9256 | 0.9027 | 0.9963 |
| 0.0048 | 5.0 | 9815 | 0.0222 | 0.8261 | 0.9268 | 0.8736 | 82 | 0.7969 | 0.9273 | 0.8571 | 55 | 0.9512 | 0.9781 | 0.9645 | 319 | 0.9231 | 0.975 | 0.9483 | 160 | 0.6515 | 0.7414 | 0.6935 | 58 | 0.875 | 0.9211 | 0.8974 | 38 | 0.8867 | 0.9452 | 0.9150 | 0.9964 |
| 0.0044 | 6.0 | 11778 | 0.0262 | 0.8276 | 0.8780 | 0.8521 | 82 | 0.7681 | 0.9636 | 0.8548 | 55 | 0.9541 | 0.9781 | 0.9659 | 319 | 0.9235 | 0.9812 | 0.9515 | 160 | 0.5263 | 0.6897 | 0.5970 | 58 | 0.9211 | 0.9211 | 0.9211 | 38 | 0.8722 | 0.9396 | 0.9047 | 0.9959 |
| 0.0042 | 7.0 | 13741 | 0.0246 | 0.8523 | 0.9146 | 0.8824 | 82 | 0.7656 | 0.8909 | 0.8235 | 55 | 0.9509 | 0.9718 | 0.9612 | 319 | 0.9118 | 0.9688 | 0.9394 | 160 | 0.5938 | 0.6552 | 0.6230 | 58 | 0.8974 | 0.9211 | 0.9091 | 38 | 0.8815 | 0.9298 | 0.9050 | 0.9960 |
| 0.0013 | 8.0 | 15704 | 0.0294 | 0.8295 | 0.8902 | 0.8588 | 82 | 0.7391 | 0.9273 | 0.8226 | 55 | 0.9543 | 0.9812 | 0.9675 | 319 | 0.9070 | 0.975 | 0.9398 | 160 | 0.6094 | 0.6724 | 0.6393 | 58 | 0.875 | 0.9211 | 0.8974 | 38 | 0.8765 | 0.9368 | 0.9056 | 0.9961 |
| 0.0019 | 9.0 | 17667 | 0.0303 | 0.8690 | 0.8902 | 0.8795 | 82 | 0.8305 | 0.8909 | 0.8596 | 55 | 0.9538 | 0.9718 | 0.9627 | 319 | 0.9290 | 0.9812 | 0.9544 | 160 | 0.6441 | 0.6552 | 0.6496 | 58 | 0.9211 | 0.9211 | 0.9211 | 38 | 0.9019 | 0.9298 | 0.9156 | 0.9961 |
| 0.0007 | 10.0 | 19630 | 0.0295 | 0.8488 | 0.8902 | 0.8690 | 82 | 0.7903 | 0.8909 | 0.8376 | 55 | 0.9571 | 0.9781 | 0.9674 | 319 | 0.9181 | 0.9812 | 0.9486 | 160 | 0.6393 | 0.6724 | 0.6555 | 58 | 0.9211 | 0.9211 | 0.9211 | 38 | 0.8938 | 0.9340 | 0.9135 | 0.9962 |
### Framework versions
- Transformers 4.20.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
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