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# 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: 0.0192
- Criterio Julgamento Precision: 0.7209
- Criterio Julgamento Recall: 0.8942
- Criterio Julgamento F1: 0.7983
- Criterio Julgamento Number: 104
- Data Sessao Precision: 0.6351
- Data Sessao Recall: 0.8545
- Data Sessao F1: 0.7287
- Data Sessao Number: 55
- Modalidade Licitacao Precision: 0.9224
- Modalidade Licitacao Recall: 0.9596
- Modalidade Licitacao F1: 0.9406
- Modalidade Licitacao Number: 421
- Numero Exercicio Precision: 0.8872
- Numero Exercicio Recall: 0.9351
- Numero Exercicio F1: 0.9105
- Numero Exercicio Number: 185
- Objeto Licitacao Precision: 0.2348
- Objeto Licitacao Recall: 0.4576
- Objeto Licitacao F1: 0.3103
- Objeto Licitacao Number: 59
- Valor Objeto Precision: 0.5424
- Valor Objeto Recall: 0.7805
- Valor Objeto F1: 0.64
- Valor Objeto Number: 41
- Overall Precision: 0.7683
- Overall Recall: 0.8971
- Overall F1: 0.8277
- Overall Accuracy: 0.9948

## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0346        | 0.96  | 2750  | 0.0329          | 0.6154                        | 0.8462                     | 0.7126                 | 104                        | 0.5495                | 0.9091             | 0.6849         | 55                 | 0.8482                         | 0.9287                      | 0.8866                  | 421                         | 0.7438                     | 0.9730                  | 0.8431              | 185                     | 0.0525                     | 0.3220                  | 0.0903              | 59                      | 0.4762                 | 0.7317              | 0.5769          | 41                  | 0.5565            | 0.8763         | 0.6807     | 0.9880           |
| 0.0309        | 1.92  | 5500  | 0.0322          | 0.6694                        | 0.7788                     | 0.72                   | 104                        | 0.5976                | 0.8909             | 0.7153         | 55                 | 0.9178                         | 0.9549                      | 0.9360                  | 421                         | 0.8211                     | 0.8432                  | 0.8320              | 185                     | 0.15                       | 0.2034                  | 0.1727              | 59                      | 0.2203                 | 0.3171              | 0.26            | 41                  | 0.7351            | 0.8243         | 0.7771     | 0.9934           |
| 0.0179        | 2.88  | 8250  | 0.0192          | 0.7209                        | 0.8942                     | 0.7983                 | 104                        | 0.6351                | 0.8545             | 0.7287         | 55                 | 0.9224                         | 0.9596                      | 0.9406                  | 421                         | 0.8872                     | 0.9351                  | 0.9105              | 185                     | 0.2348                     | 0.4576                  | 0.3103              | 59                      | 0.5424                 | 0.7805              | 0.64            | 41                  | 0.7683            | 0.8971         | 0.8277     | 0.9948           |
| 0.0174        | 3.84  | 11000 | 0.0320          | 0.7522                        | 0.8173                     | 0.7834                 | 104                        | 0.5741                | 0.5636             | 0.5688         | 55                 | 0.8881                         | 0.9430                      | 0.9147                  | 421                         | 0.8490                     | 0.8811                  | 0.8647              | 185                     | 0.2436                     | 0.3220                  | 0.2774              | 59                      | 0.5370                 | 0.7073              | 0.6105          | 41                  | 0.7719            | 0.8370         | 0.8031     | 0.9946           |
| 0.0192        | 4.8   | 13750 | 0.0261          | 0.6744                        | 0.8365                     | 0.7468                 | 104                        | 0.6190                | 0.7091             | 0.6610         | 55                 | 0.9169                         | 0.9430                      | 0.9297                  | 421                         | 0.8404                     | 0.8541                  | 0.8472              | 185                     | 0.2059                     | 0.3559                  | 0.2609              | 59                      | 0.5088                 | 0.7073              | 0.5918          | 41                  | 0.7521            | 0.8451         | 0.7959     | 0.9949           |
| 0.0158        | 5.76  | 16500 | 0.0250          | 0.6641                        | 0.8173                     | 0.7328                 | 104                        | 0.5610                | 0.8364             | 0.6715         | 55                 | 0.9199                         | 0.9549                      | 0.9371                  | 421                         | 0.9167                     | 0.9514                  | 0.9337              | 185                     | 0.1912                     | 0.4407                  | 0.2667              | 59                      | 0.4828                 | 0.6829              | 0.5657          | 41                  | 0.7386            | 0.8821         | 0.8040     | 0.9948           |
| 0.0126        | 6.72  | 19250 | 0.0267          | 0.6694                        | 0.7981                     | 0.7281                 | 104                        | 0.6386                | 0.9636             | 0.7681         | 55                 | 0.8723                         | 0.9572                      | 0.9128                  | 421                         | 0.8812                     | 0.9622                  | 0.9199              | 185                     | 0.2180                     | 0.4915                  | 0.3021              | 59                      | 0.5323                 | 0.8049              | 0.6408          | 41                  | 0.7308            | 0.9006         | 0.8068     | 0.9945           |
| 0.0162        | 7.68  | 22000 | 0.0328          | 0.675                         | 0.7788                     | 0.7232                 | 104                        | 0.6604                | 0.6364             | 0.6481         | 55                 | 0.9263                         | 0.9549                      | 0.9404                  | 421                         | 0.8535                     | 0.9135                  | 0.8825              | 185                     | 0.2471                     | 0.3559                  | 0.2917              | 59                      | 0.5091                 | 0.6829              | 0.5833          | 41                  | 0.7788            | 0.8509         | 0.8133     | 0.9948           |


### Framework versions

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

{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert_base_tcm_teste", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bert\_base\_tcm\_teste
======================


This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0192
* Criterio Julgamento Precision: 0.7209
* Criterio Julgamento Recall: 0.8942
* Criterio Julgamento F1: 0.7983
* Criterio Julgamento Number: 104
* Data Sessao Precision: 0.6351
* Data Sessao Recall: 0.8545
* Data Sessao F1: 0.7287
* Data Sessao Number: 55
* Modalidade Licitacao Precision: 0.9224
* Modalidade Licitacao Recall: 0.9596
* Modalidade Licitacao F1: 0.9406
* Modalidade Licitacao Number: 421
* Numero Exercicio Precision: 0.8872
* Numero Exercicio Recall: 0.9351
* Numero Exercicio F1: 0.9105
* Numero Exercicio Number: 185
* Objeto Licitacao Precision: 0.2348
* Objeto Licitacao Recall: 0.4576
* Objeto Licitacao F1: 0.3103
* Objeto Licitacao Number: 59
* Valor Objeto Precision: 0.5424
* Valor Objeto Recall: 0.7805
* Valor Objeto F1: 0.64
* Valor Objeto Number: 41
* Overall Precision: 0.7683
* Overall Recall: 0.8971
* Overall F1: 0.8277
* Overall Accuracy: 0.9948


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: 0.0001
* 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



### Framework versions


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



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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 ### Framework versions


* Transformers 4.21.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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 ### Framework versions


* Transformers 4.21.0.dev0
* Pytorch 1.11.0+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* 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### Framework versions


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