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_0.7
  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_0.7

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: 0.0128
- Criterio Julgamento Precision: 0.8235
- Criterio Julgamento Recall: 0.9032
- Criterio Julgamento F1: 0.8615
- Criterio Julgamento Number: 93
- Data Sessao Precision: 0.7324
- Data Sessao Recall: 0.9286
- Data Sessao F1: 0.8189
- Data Sessao Number: 56
- Modalidade Licitacao Precision: 0.9415
- Modalidade Licitacao Recall: 0.9769
- Modalidade Licitacao F1: 0.9589
- Modalidade Licitacao Number: 346
- Numero Exercicio Precision: 0.9486
- Numero Exercicio Recall: 0.9486
- Numero Exercicio F1: 0.9486
- Numero Exercicio Number: 175
- Objeto Licitacao Precision: 0.5352
- Objeto Licitacao Recall: 0.6909
- Objeto Licitacao F1: 0.6032
- Objeto Licitacao Number: 55
- Valor Objeto Precision: 0.8
- Valor Objeto Recall: 0.8649
- Valor Objeto F1: 0.8312
- Valor Objeto Number: 37
- Overall Precision: 0.8680
- Overall Recall: 0.9318
- Overall F1: 0.8987
- 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-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 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0267        | 1.0   | 2332  | 0.0175          | 0.8333                        | 0.9140                     | 0.8718                 | 93                         | 0.6825                | 0.7679             | 0.7227         | 56                 | 0.9342                         | 0.9855                      | 0.9592                  | 346                         | 0.9194                     | 0.9771                  | 0.9474              | 175                     | 0.4154                     | 0.4909                  | 0.45                | 55                      | 0.5                    | 0.7568              | 0.6022          | 37                  | 0.8303            | 0.9121         | 0.8693     | 0.9954           |
| 0.0211        | 2.0   | 4664  | 0.0158          | 0.7154                        | 0.9462                     | 0.8148                 | 93                         | 0.7812                | 0.8929             | 0.8333         | 56                 | 0.9319                         | 0.9884                      | 0.9593                  | 346                         | 0.9605                     | 0.9714                  | 0.9659              | 175                     | 0.4                        | 0.6545                  | 0.4966              | 55                      | 0.8293                 | 0.9189              | 0.8718          | 37                  | 0.8353            | 0.9449         | 0.8867     | 0.9956           |
| 0.0127        | 3.0   | 6996  | 0.0157          | 0.8218                        | 0.8925                     | 0.8557                 | 93                         | 0.8254                | 0.9286             | 0.8739         | 56                 | 0.9522                         | 0.9798                      | 0.9658                  | 346                         | 0.96                       | 0.96                    | 0.96                | 175                     | 0.5735                     | 0.7091                  | 0.6341              | 55                      | 0.6857                 | 0.6486              | 0.6667          | 37                  | 0.8835            | 0.9252         | 0.9038     | 0.9957           |
| 0.0074        | 4.0   | 9328  | 0.0128          | 0.8235                        | 0.9032                     | 0.8615                 | 93                         | 0.7324                | 0.9286             | 0.8189         | 56                 | 0.9415                         | 0.9769                      | 0.9589                  | 346                         | 0.9486                     | 0.9486                  | 0.9486              | 175                     | 0.5352                     | 0.6909                  | 0.6032              | 55                      | 0.8                    | 0.8649              | 0.8312          | 37                  | 0.8680            | 0.9318         | 0.8987     | 0.9966           |
| 0.0065        | 5.0   | 11660 | 0.0177          | 0.8113                        | 0.9247                     | 0.8643                 | 93                         | 0.675                 | 0.9643             | 0.7941         | 56                 | 0.9444                         | 0.9827                      | 0.9632                  | 346                         | 0.9392                     | 0.9714                  | 0.9551              | 175                     | 0.5075                     | 0.6182                  | 0.5574              | 55                      | 0.7674                 | 0.8919              | 0.825           | 37                  | 0.8566            | 0.9409         | 0.8968     | 0.9958           |
| 0.005         | 6.0   | 13992 | 0.0161          | 0.8485                        | 0.9032                     | 0.875                  | 93                         | 0.7164                | 0.8571             | 0.7805         | 56                 | 0.9496                         | 0.9798                      | 0.9644                  | 346                         | 0.9556                     | 0.9829                  | 0.9690              | 175                     | 0.6290                     | 0.7091                  | 0.6667              | 55                      | 0.8108                 | 0.8108              | 0.8108          | 37                  | 0.8878            | 0.9344         | 0.9105     | 0.9967           |
| 0.0039        | 7.0   | 16324 | 0.0185          | 0.8925                        | 0.8925                     | 0.8925                 | 93                         | 0.7812                | 0.8929             | 0.8333         | 56                 | 0.9602                         | 0.9769                      | 0.9685                  | 346                         | 0.9607                     | 0.9771                  | 0.9688              | 175                     | 0.5224                     | 0.6364                  | 0.5738              | 55                      | 0.8378                 | 0.8378              | 0.8378          | 37                  | 0.8951            | 0.9291         | 0.9118     | 0.9966           |
| 0.0035        | 8.0   | 18656 | 0.0188          | 0.8431                        | 0.9247                     | 0.8821                 | 93                         | 0.7903                | 0.875              | 0.8305         | 56                 | 0.9571                         | 0.9682                      | 0.9626                  | 346                         | 0.9605                     | 0.9714                  | 0.9659              | 175                     | 0.6981                     | 0.6727                  | 0.6852              | 55                      | 0.8462                 | 0.8919              | 0.8684          | 37                  | 0.9068            | 0.9318         | 0.9191     | 0.9969           |
| 0.0017        | 9.0   | 20988 | 0.0207          | 0.8529                        | 0.9355                     | 0.8923                 | 93                         | 0.7727                | 0.9107             | 0.8361         | 56                 | 0.9630                         | 0.9769                      | 0.9699                  | 346                         | 0.9605                     | 0.9714                  | 0.9659              | 175                     | 0.7143                     | 0.6364                  | 0.6731              | 55                      | 0.8462                 | 0.8919              | 0.8684          | 37                  | 0.9107            | 0.9370         | 0.9237     | 0.9968           |
| 0.002         | 10.0  | 23320 | 0.0191          | 0.8614                        | 0.9355                     | 0.8969                 | 93                         | 0.7647                | 0.9286             | 0.8387         | 56                 | 0.9549                         | 0.9798                      | 0.9672                  | 346                         | 0.9553                     | 0.9771                  | 0.9661              | 175                     | 0.6167                     | 0.6727                  | 0.6435              | 55                      | 0.825                  | 0.8919              | 0.8571          | 37                  | 0.8954            | 0.9436         | 0.9188     | 0.9968           |


### Framework versions

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