pytorch tensorboard bert token-classification transformers generated_from_trainer license:mit model-index autotrain_compatible
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bert_base_tcm_0.8
  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_0.8

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: 0.7542
- Criterio Julgamento Recall: 0.7807
- Criterio Julgamento F1: 0.7672
- Criterio Julgamento Number: 114
- Data Sessao Precision: 0.7606
- Data Sessao Recall: 0.9153
- Data Sessao F1: 0.8308
- Data Sessao Number: 59
- Modalidade Licitacao Precision: 0.9349
- Modalidade Licitacao Recall: 0.9632
- Modalidade Licitacao F1: 0.9488
- Modalidade Licitacao Number: 462
- Numero Exercicio Precision: 0.9238
- Numero Exercicio Recall: 0.9810
- Numero Exercicio F1: 0.9515
- Numero Exercicio Number: 210
- Objeto Licitacao Precision: 0.4302
- Objeto Licitacao Recall: 0.6167
- Objeto Licitacao F1: 0.5068
- Objeto Licitacao Number: 60
- Valor Objeto Precision: 0.8222
- Valor Objeto Recall: 0.9024
- Valor Objeto F1: 0.8605
- Valor Objeto Number: 41
- Overall Precision: 0.8518
- Overall Recall: 0.9175
- Overall F1: 0.8835
- Overall Accuracy: 0.9963

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0211        | 1.0   | 2863  | 0.0182          | 0.7669                        | 0.8947                     | 0.8259                 | 114                        | 0.7342                | 0.9831             | 0.8406         | 59                 | 0.9413                         | 0.9719                      | 0.9563                  | 462                         | 0.9140                     | 0.9619                  | 0.9374              | 210                     | 0.3373                     | 0.4667                  | 0.3916              | 60                      | 0.7255                 | 0.9024              | 0.8043          | 41                  | 0.8391            | 0.9260         | 0.8804     | 0.9953           |
| 0.0165        | 2.0   | 5726  | 0.0174          | 0.7364                        | 0.8333                     | 0.7819                 | 114                        | 0.6912                | 0.7966             | 0.7402         | 59                 | 0.9368                         | 0.9632                      | 0.9498                  | 462                         | 0.8826                     | 0.9667                  | 0.9227              | 210                     | 0.2877                     | 0.35                    | 0.3158              | 60                      | 0.7660                 | 0.8780              | 0.8182          | 41                  | 0.8288            | 0.8953         | 0.8608     | 0.9955           |
| 0.0109        | 3.0   | 8589  | 0.0175          | 0.7293                        | 0.8509                     | 0.7854                 | 114                        | 0.6628                | 0.9661             | 0.7862         | 59                 | 0.9396                         | 0.9762                      | 0.9575                  | 462                         | 0.9067                     | 0.9714                  | 0.9379              | 210                     | 0.3333                     | 0.5167                  | 0.4052              | 60                      | 0.8                    | 0.8780              | 0.8372          | 41                  | 0.8249            | 0.9260         | 0.8725     | 0.9957           |
| 0.0068        | 4.0   | 11452 | 0.0180          | 0.8016                        | 0.8860                     | 0.8417                 | 114                        | 0.7333                | 0.9322             | 0.8209         | 59                 | 0.9354                         | 0.9719                      | 0.9533                  | 462                         | 0.9224                     | 0.9619                  | 0.9417              | 210                     | 0.3495                     | 0.6                     | 0.4417              | 60                      | 0.8043                 | 0.9024              | 0.8506          | 41                  | 0.8389            | 0.9302         | 0.8822     | 0.9962           |
| 0.0083        | 5.0   | 14315 | 0.0155          | 0.7542                        | 0.7807                     | 0.7672                 | 114                        | 0.7606                | 0.9153             | 0.8308         | 59                 | 0.9349                         | 0.9632                      | 0.9488                  | 462                         | 0.9238                     | 0.9810                  | 0.9515              | 210                     | 0.4302                     | 0.6167                  | 0.5068              | 60                      | 0.8222                 | 0.9024              | 0.8605          | 41                  | 0.8518            | 0.9175         | 0.8835     | 0.9963           |
| 0.0046        | 6.0   | 17178 | 0.0174          | 0.8017                        | 0.8509                     | 0.8255                 | 114                        | 0.8182                | 0.9153             | 0.8640         | 59                 | 0.9454                         | 0.9740                      | 0.9595                  | 462                         | 0.9196                     | 0.9810                  | 0.9493              | 210                     | 0.4598                     | 0.6667                  | 0.5442              | 60                      | 0.8222                 | 0.9024              | 0.8605          | 41                  | 0.8675            | 0.9345         | 0.8997     | 0.9962           |
| 0.0037        | 7.0   | 20041 | 0.0176          | 0.7983                        | 0.8333                     | 0.8155                 | 114                        | 0.8333                | 0.8475             | 0.8403         | 59                 | 0.9388                         | 0.9632                      | 0.9509                  | 462                         | 0.9152                     | 0.9762                  | 0.9447              | 210                     | 0.5479                     | 0.6667                  | 0.6015              | 60                      | 0.7609                 | 0.8537              | 0.8046          | 41                  | 0.8735            | 0.9197         | 0.8960     | 0.9965           |
| 0.0015        | 8.0   | 22904 | 0.0179          | 0.8083                        | 0.8509                     | 0.8291                 | 114                        | 0.8209                | 0.9322             | 0.8730         | 59                 | 0.9448                         | 0.9632                      | 0.9539                  | 462                         | 0.9234                     | 0.9762                  | 0.9491              | 210                     | 0.5125                     | 0.6833                  | 0.5857              | 60                      | 0.7778                 | 0.8537              | 0.8140          | 41                  | 0.8736            | 0.9281         | 0.9001     | 0.9967           |
| 0.0016        | 9.0   | 25767 | 0.0214          | 0.7934                        | 0.8421                     | 0.8170                 | 114                        | 0.7971                | 0.9322             | 0.8594         | 59                 | 0.9368                         | 0.9632                      | 0.9498                  | 462                         | 0.9152                     | 0.9762                  | 0.9447              | 210                     | 0.5068                     | 0.6167                  | 0.5564              | 60                      | 0.7778                 | 0.8537              | 0.8140          | 41                  | 0.8669            | 0.9228         | 0.8940     | 0.9966           |
| 0.0008        | 10.0  | 28630 | 0.0211          | 0.8051                        | 0.8333                     | 0.8190                 | 114                        | 0.7714                | 0.9153             | 0.8372         | 59                 | 0.9428                         | 0.9632                      | 0.9529                  | 462                         | 0.9152                     | 0.9762                  | 0.9447              | 210                     | 0.5556                     | 0.6667                  | 0.6061              | 60                      | 0.7778                 | 0.8537              | 0.8140          | 41                  | 0.8731            | 0.9239         | 0.8978     | 0.9966           |


### Framework versions

- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
