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

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