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

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