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# bert_base_tcm_0.9_no_valor_objeto

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the ricardo-filho/tcm-0.9-no-valor-objeto dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0176
- Criterio Julgamento Precision: 0.8310
- Criterio Julgamento Recall: 0.8310
- Criterio Julgamento F1: 0.8310
- Criterio Julgamento Number: 142
- Data Sessao Precision: 0.7909
- Data Sessao Recall: 0.9667
- Data Sessao F1: 0.87
- Data Sessao Number: 90
- Modalidade Licitacao Precision: 0.9564
- Modalidade Licitacao Recall: 0.9815
- Modalidade Licitacao F1: 0.9688
- Modalidade Licitacao Number: 648
- Numero Exercicio Precision: 0.9362
- Numero Exercicio Recall: 0.9788
- Numero Exercicio F1: 0.9570
- Numero Exercicio Number: 330
- Objeto Licitacao Precision: 0.4460
- Objeto Licitacao Recall: 0.5849
- Objeto Licitacao F1: 0.5061
- Objeto Licitacao Number: 106
- Overall Precision: 0.8751
- Overall Recall: 0.9316
- Overall F1: 0.9025
- Overall Accuracy: 0.9953

## 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: 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 | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0159        | 1.0   | 3497  | 0.0176          | 0.8310                        | 0.8310                     | 0.8310                 | 142                        | 0.7909                | 0.9667             | 0.87           | 90                 | 0.9564                         | 0.9815                      | 0.9688                  | 648                         | 0.9362                     | 0.9788                  | 0.9570              | 330                     | 0.4460                     | 0.5849                  | 0.5061              | 106                     | 0.8751            | 0.9316         | 0.9025     | 0.9953           |
| 0.0161        | 2.0   | 6994  | 0.0191          | 0.8312                        | 0.9014                     | 0.8649                 | 142                        | 0.7890                | 0.9556             | 0.8643         | 90                 | 0.9580                         | 0.9846                      | 0.9711                  | 648                         | 0.9475                     | 0.9848                  | 0.9658              | 330                     | 0.5556                     | 0.6604                  | 0.6034              | 106                     | 0.8920            | 0.9476         | 0.9189     | 0.9954           |
| 0.0094        | 3.0   | 10491 | 0.0215          | 0.8125                        | 0.9155                     | 0.8609                 | 142                        | 0.7818                | 0.9556             | 0.86           | 90                 | 0.9608                         | 0.9846                      | 0.9726                  | 648                         | 0.9503                     | 0.9848                  | 0.9673              | 330                     | 0.5108                     | 0.6698                  | 0.5796              | 106                     | 0.8834            | 0.9498         | 0.9154     | 0.9955           |
| 0.0057        | 4.0   | 13988 | 0.0212          | 0.8269                        | 0.9085                     | 0.8658                 | 142                        | 0.8095                | 0.9444             | 0.8718         | 90                 | 0.9697                         | 0.9861                      | 0.9778                  | 648                         | 0.9501                     | 0.9818                  | 0.9657              | 330                     | 0.5290                     | 0.6887                  | 0.5984              | 106                     | 0.8935            | 0.9498         | 0.9208     | 0.9960           |
| 0.0049        | 5.0   | 17485 | 0.0214          | 0.8344                        | 0.9225                     | 0.8763                 | 142                        | 0.7905                | 0.9222             | 0.8513         | 90                 | 0.9652                         | 0.9830                      | 0.9740                  | 648                         | 0.9474                     | 0.9818                  | 0.9643              | 330                     | 0.5217                     | 0.6792                  | 0.5902              | 106                     | 0.8894            | 0.9476         | 0.9176     | 0.9957           |
| 0.0036        | 6.0   | 20982 | 0.0297          | 0.8397                        | 0.9225                     | 0.8792                 | 142                        | 0.7748                | 0.9556             | 0.8557         | 90                 | 0.9636                         | 0.9799                      | 0.9717                  | 648                         | 0.9585                     | 0.9788                  | 0.9685              | 330                     | 0.5435                     | 0.7075                  | 0.6148              | 106                     | 0.8922            | 0.9498         | 0.9201     | 0.9953           |
| 0.0016        | 7.0   | 24479 | 0.0297          | 0.8302                        | 0.9296                     | 0.8771                 | 142                        | 0.7925                | 0.9333             | 0.8571         | 90                 | 0.9652                         | 0.9830                      | 0.9740                  | 648                         | 0.9467                     | 0.9697                  | 0.9581              | 330                     | 0.5746                     | 0.7264                  | 0.6417              | 106                     | 0.8948            | 0.9498         | 0.9215     | 0.9955           |
| 0.0016        | 8.0   | 27976 | 0.0298          | 0.8212                        | 0.8732                     | 0.8464                 | 142                        | 0.8095                | 0.9444             | 0.8718         | 90                 | 0.9666                         | 0.9815                      | 0.9740                  | 648                         | 0.9524                     | 0.9697                  | 0.9610              | 330                     | 0.5746                     | 0.7264                  | 0.6417              | 106                     | 0.8974            | 0.9438         | 0.9200     | 0.9955           |
| 0.0011        | 9.0   | 31473 | 0.0319          | 0.7949                        | 0.8732                     | 0.8322                 | 142                        | 0.7788                | 0.9                | 0.8351         | 90                 | 0.9650                         | 0.9799                      | 0.9724                  | 648                         | 0.9467                     | 0.9697                  | 0.9581              | 330                     | 0.6016                     | 0.7264                  | 0.6581              | 106                     | 0.8938            | 0.9400         | 0.9163     | 0.9954           |
| 0.0011        | 10.0  | 34970 | 0.0324          | 0.8141                        | 0.8944                     | 0.8523                 | 142                        | 0.7524                | 0.8778             | 0.8103         | 90                 | 0.9680                         | 0.9815                      | 0.9747                  | 648                         | 0.9494                     | 0.9667                  | 0.9580              | 330                     | 0.5878                     | 0.7264                  | 0.6498              | 106                     | 0.8939            | 0.9407         | 0.9167     | 0.9954           |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["ricardo-filho/tcm-0.9-no-valor-objeto"], "model-index": [{"name": "bert_base_tcm_0.9_no_valor_objeto", "results": []}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:ricardo-filho/tcm-0.9-no-valor-objeto license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-ricardo-filho/tcm-0.9-no-valor-objeto #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bert\_base\_tcm\_0.9\_no\_valor\_objeto
=======================================


This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the ricardo-filho/tcm-0.9-no-valor-objeto dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0176
* Criterio Julgamento Precision: 0.8310
* Criterio Julgamento Recall: 0.8310
* Criterio Julgamento F1: 0.8310
* Criterio Julgamento Number: 142
* Data Sessao Precision: 0.7909
* Data Sessao Recall: 0.9667
* Data Sessao F1: 0.87
* Data Sessao Number: 90
* Modalidade Licitacao Precision: 0.9564
* Modalidade Licitacao Recall: 0.9815
* Modalidade Licitacao F1: 0.9688
* Modalidade Licitacao Number: 648
* Numero Exercicio Precision: 0.9362
* Numero Exercicio Recall: 0.9788
* Numero Exercicio F1: 0.9570
* Numero Exercicio Number: 330
* Objeto Licitacao Precision: 0.4460
* Objeto Licitacao Recall: 0.5849
* Objeto Licitacao F1: 0.5061
* Objeto Licitacao Number: 106
* Overall Precision: 0.8751
* Overall Recall: 0.9316
* Overall F1: 0.9025
* Overall Accuracy: 0.9953


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: 10.0


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3



### 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: 10.0 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-ricardo-filho/tcm-0.9-no-valor-objeto #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### 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: 10.0 ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-ricardo-filho/tcm-0.9-no-valor-objeto #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### 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: 10.0### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu118
* Datasets 2.11.0
* Tokenizers 0.13.3