bert_base_tcm_0.6

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: 0.0193
• Criterio Julgamento Precision: 0.8875
• Criterio Julgamento Recall: 0.8659
• Criterio Julgamento F1: 0.8765
• Criterio Julgamento Number: 82
• Data Sessao Precision: 0.7571
• Data Sessao Recall: 0.9636
• Data Sessao F1: 0.848
• Data Sessao Number: 55
• Modalidade Licitacao Precision: 0.9394
• Modalidade Licitacao Recall: 0.9718
• Modalidade Licitacao F1: 0.9553
• Modalidade Licitacao Number: 319
• Numero Exercicio Precision: 0.9172
• Numero Exercicio Recall: 0.9688
• Numero Exercicio F1: 0.9422
• Numero Exercicio Number: 160
• Objeto Licitacao Precision: 0.4659
• Objeto Licitacao Recall: 0.7069
• Objeto Licitacao F1: 0.5616
• Objeto Licitacao Number: 58
• Valor Objeto Precision: 0.8333
• Valor Objeto Recall: 0.9211
• Valor Objeto F1: 0.875
• Valor Objeto Number: 38
• Overall Precision: 0.8537
• Overall Recall: 0.9340
• Overall F1: 0.8920
• Overall Accuracy: 0.9951

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
0.0252	1.0	1963	0.0202	0.8022	0.8902	0.8439	82	0.7391	0.9273	0.8226	55	0.9233	0.9812	0.9514	319	0.8966	0.975	0.9341	160	0.4730	0.6034	0.5303	58	0.7083	0.8947	0.7907	38	0.8327	0.9298	0.8786	0.9948
0.0191	2.0	3926	0.0226	0.8554	0.8659	0.8606	82	0.5641	0.4	0.4681	55	0.9572	0.9812	0.9690	319	0.9273	0.9563	0.9415	160	0.3770	0.3966	0.3866	58	0.8571	0.7895	0.8219	38	0.8620	0.8596	0.8608	0.9951
0.0137	3.0	5889	0.0193	0.8875	0.8659	0.8765	82	0.7571	0.9636	0.848	55	0.9394	0.9718	0.9553	319	0.9172	0.9688	0.9422	160	0.4659	0.7069	0.5616	58	0.8333	0.9211	0.875	38	0.8537	0.9340	0.8920	0.9951
0.0082	4.0	7852	0.0210	0.8780	0.8780	0.8780	82	0.7966	0.8545	0.8246	55	0.9512	0.9781	0.9645	319	0.9023	0.9812	0.9401	160	0.5385	0.6034	0.5691	58	0.9	0.9474	0.9231	38	0.8810	0.9256	0.9027	0.9963
0.0048	5.0	9815	0.0222	0.8261	0.9268	0.8736	82	0.7969	0.9273	0.8571	55	0.9512	0.9781	0.9645	319	0.9231	0.975	0.9483	160	0.6515	0.7414	0.6935	58	0.875	0.9211	0.8974	38	0.8867	0.9452	0.9150	0.9964
0.0044	6.0	11778	0.0262	0.8276	0.8780	0.8521	82	0.7681	0.9636	0.8548	55	0.9541	0.9781	0.9659	319	0.9235	0.9812	0.9515	160	0.5263	0.6897	0.5970	58	0.9211	0.9211	0.9211	38	0.8722	0.9396	0.9047	0.9959
0.0042	7.0	13741	0.0246	0.8523	0.9146	0.8824	82	0.7656	0.8909	0.8235	55	0.9509	0.9718	0.9612	319	0.9118	0.9688	0.9394	160	0.5938	0.6552	0.6230	58	0.8974	0.9211	0.9091	38	0.8815	0.9298	0.9050	0.9960
0.0013	8.0	15704	0.0294	0.8295	0.8902	0.8588	82	0.7391	0.9273	0.8226	55	0.9543	0.9812	0.9675	319	0.9070	0.975	0.9398	160	0.6094	0.6724	0.6393	58	0.875	0.9211	0.8974	38	0.8765	0.9368	0.9056	0.9961
0.0019	9.0	17667	0.0303	0.8690	0.8902	0.8795	82	0.8305	0.8909	0.8596	55	0.9538	0.9718	0.9627	319	0.9290	0.9812	0.9544	160	0.6441	0.6552	0.6496	58	0.9211	0.9211	0.9211	38	0.9019	0.9298	0.9156	0.9961
0.0007	10.0	19630	0.0295	0.8488	0.8902	0.8690	82	0.7903	0.8909	0.8376	55	0.9571	0.9781	0.9674	319	0.9181	0.9812	0.9486	160	0.6393	0.6724	0.6555	58	0.9211	0.9211	0.9211	38	0.8938	0.9340	0.9135	0.9962

Framework versions

• Transformers 4.20.0.dev0
• Pytorch 1.11.0+cu113
• Datasets 2.2.2
• Tokenizers 0.12.1