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	---
	license: mit
	tags:
	- generated_from_trainer
	model-index:
	- name: bert_base_tcm_teste
	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_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: 0.0205
	- Criterio Julgamento Precision: 0.7719
	- Criterio Julgamento Recall: 0.8462
	- Criterio Julgamento F1: 0.8073
	- Criterio Julgamento Number: 104
	- Data Sessao Precision: 0.7812
	- Data Sessao Recall: 0.9091
	- Data Sessao F1: 0.8403
	- Data Sessao Number: 55
	- Modalidade Licitacao Precision: 0.9507
	- Modalidade Licitacao Recall: 0.9620
	- Modalidade Licitacao F1: 0.9563
	- Modalidade Licitacao Number: 421
	- Numero Exercicio Precision: 0.9375
	- Numero Exercicio Recall: 0.9730
	- Numero Exercicio F1: 0.9549
	- Numero Exercicio Number: 185
	- Objeto Licitacao Precision: 0.5309
	- Objeto Licitacao Recall: 0.7288
	- Objeto Licitacao F1: 0.6143
	- Objeto Licitacao Number: 59
	- Valor Objeto Precision: 0.8409
	- Valor Objeto Recall: 0.9024
	- Valor Objeto F1: 0.8706
	- Valor Objeto Number: 41
	- Overall Precision: 0.8719
	- Overall Recall: 0.9283
	- Overall F1: 0.8992
	- Overall Accuracy: 0.9967

	## 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: 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 \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-----------------------------:\|:--------------------------:\|:----------------------:\|:--------------------------:\|:---------------------:\|:------------------:\|:--------------:\|:------------------:\|:------------------------------:\|:---------------------------:\|:-----------------------:\|:---------------------------:\|:--------------------------:\|:-----------------------:\|:-------------------:\|:-----------------------:\|:--------------------------:\|:-----------------------:\|:-------------------:\|:-----------------------:\|:----------------------:\|:-------------------:\|:---------------:\|:-------------------:\|:-----------------:\|:--------------:\|:----------:\|:----------------:\|
	\| 0.0168 \| 0.96 \| 2750 \| 0.0169 \| 0.7016 \| 0.8365 \| 0.7632 \| 104 \| 0.6707 \| 1.0 \| 0.8029 \| 55 \| 0.9424 \| 0.9715 \| 0.9567 \| 421 \| 0.9110 \| 0.9405 \| 0.9255 \| 185 \| 0.3304 \| 0.6271 \| 0.4327 \| 59 \| 0.76 \| 0.9268 \| 0.8352 \| 41 \| 0.8056 \| 0.9249 \| 0.8611 \| 0.9950 \|
	\| 0.0164 \| 1.92 \| 5500 \| 0.0125 \| 0.7565 \| 0.8365 \| 0.7945 \| 104 \| 0.6923 \| 0.9818 \| 0.8120 \| 55 \| 0.9491 \| 0.9739 \| 0.9613 \| 421 \| 0.9375 \| 0.9730 \| 0.9549 \| 185 \| 0.4138 \| 0.6102 \| 0.4932 \| 59 \| 0.8085 \| 0.9268 \| 0.8636 \| 41 \| 0.8465 \| 0.9306 \| 0.8866 \| 0.9965 \|
	\| 0.0076 \| 2.88 \| 8250 \| 0.0204 \| 0.7184 \| 0.7115 \| 0.7150 \| 104 \| 0.8070 \| 0.8364 \| 0.8214 \| 55 \| 0.9468 \| 0.9715 \| 0.9590 \| 421 \| 0.9282 \| 0.9784 \| 0.9526 \| 185 \| 0.4783 \| 0.5593 \| 0.5156 \| 59 \| 0.7209 \| 0.7561 \| 0.7381 \| 41 \| 0.8610 \| 0.8948 \| 0.8776 \| 0.9961 \|
	\| 0.0067 \| 3.84 \| 11000 \| 0.0168 \| 0.7589 \| 0.8173 \| 0.7870 \| 104 \| 0.8 \| 0.8 \| 0.8000 \| 55 \| 0.9487 \| 0.9667 \| 0.9576 \| 421 \| 0.9319 \| 0.9622 \| 0.9468 \| 185 \| 0.5309 \| 0.7288 \| 0.6143 \| 59 \| 0.8636 \| 0.9268 \| 0.8941 \| 41 \| 0.8717 \| 0.9191 \| 0.8948 \| 0.9965 \|
	\| 0.0043 \| 4.8 \| 13750 \| 0.0144 \| 0.736 \| 0.8846 \| 0.8035 \| 104 \| 0.8033 \| 0.8909 \| 0.8448 \| 55 \| 0.9512 \| 0.9715 \| 0.9612 \| 421 \| 0.9316 \| 0.9568 \| 0.944 \| 185 \| 0.5135 \| 0.6441 \| 0.5714 \| 59 \| 0.8444 \| 0.9268 \| 0.8837 \| 41 \| 0.8681 \| 0.9283 \| 0.8972 \| 0.9967 \|
	\| 0.0072 \| 5.76 \| 16500 \| 0.0161 \| 0.8091 \| 0.8558 \| 0.8318 \| 104 \| 0.7237 \| 1.0 \| 0.8397 \| 55 \| 0.9487 \| 0.9667 \| 0.9576 \| 421 \| 0.9326 \| 0.9730 \| 0.9524 \| 185 \| 0.4318 \| 0.6441 \| 0.5170 \| 59 \| 0.8222 \| 0.9024 \| 0.8605 \| 41 \| 0.8565 \| 0.9318 \| 0.8926 \| 0.9966 \|
	\| 0.003 \| 6.72 \| 19250 \| 0.0205 \| 0.7719 \| 0.8462 \| 0.8073 \| 104 \| 0.7812 \| 0.9091 \| 0.8403 \| 55 \| 0.9507 \| 0.9620 \| 0.9563 \| 421 \| 0.9375 \| 0.9730 \| 0.9549 \| 185 \| 0.5309 \| 0.7288 \| 0.6143 \| 59 \| 0.8409 \| 0.9024 \| 0.8706 \| 41 \| 0.8719 \| 0.9283 \| 0.8992 \| 0.9967 \|
	\| 0.0033 \| 7.68 \| 22000 \| 0.0197 \| 0.7736 \| 0.7885 \| 0.7810 \| 104 \| 0.7463 \| 0.9091 \| 0.8197 \| 55 \| 0.9466 \| 0.9691 \| 0.9577 \| 421 \| 0.9227 \| 0.9676 \| 0.9446 \| 185 \| 0.5286 \| 0.6271 \| 0.5736 \| 59 \| 0.7442 \| 0.7805 \| 0.7619 \| 41 \| 0.8650 \| 0.9110 \| 0.8874 \| 0.9964 \|
	\| 0.0043 \| 8.64 \| 24750 \| 0.0250 \| 0.7607 \| 0.8558 \| 0.8054 \| 104 \| 0.7612 \| 0.9273 \| 0.8361 \| 55 \| 0.9400 \| 0.9667 \| 0.9532 \| 421 \| 0.9427 \| 0.9784 \| 0.9602 \| 185 \| 0.5479 \| 0.6780 \| 0.6061 \| 59 \| 0.8043 \| 0.9024 \| 0.8506 \| 41 \| 0.8675 \| 0.9306 \| 0.8979 \| 0.9965 \|
	\| 0.0014 \| 9.61 \| 27500 \| 0.0257 \| 0.8018 \| 0.8558 \| 0.8279 \| 104 \| 0.7391 \| 0.9273 \| 0.8226 \| 55 \| 0.9417 \| 0.9596 \| 0.9506 \| 421 \| 0.9372 \| 0.9676 \| 0.9521 \| 185 \| 0.5143 \| 0.6102 \| 0.5581 \| 59 \| 0.8 \| 0.8780 \| 0.8372 \| 41 \| 0.8689 \| 0.9191 \| 0.8933 \| 0.9966 \|
	\| 0.0025 \| 10.57 \| 30250 \| 0.0258 \| 0.7798 \| 0.8173 \| 0.7981 \| 104 \| 0.7424 \| 0.8909 \| 0.8099 \| 55 \| 0.9465 \| 0.9667 \| 0.9565 \| 421 \| 0.9424 \| 0.9730 \| 0.9574 \| 185 \| 0.5352 \| 0.6441 \| 0.5846 \| 59 \| 0.8222 \| 0.9024 \| 0.8605 \| 41 \| 0.8728 \| 0.9202 \| 0.8959 \| 0.9963 \|
	\| 0.0016 \| 11.53 \| 33000 \| 0.0273 \| 0.7925 \| 0.8077 \| 0.8000 \| 104 \| 0.7246 \| 0.9091 \| 0.8065 \| 55 \| 0.9485 \| 0.9620 \| 0.9552 \| 421 \| 0.9282 \| 0.9784 \| 0.9526 \| 185 \| 0.56 \| 0.7119 \| 0.6269 \| 59 \| 0.8409 \| 0.9024 \| 0.8706 \| 41 \| 0.8723 \| 0.9237 \| 0.8972 \| 0.9964 \|


	### Framework versions

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