This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6440

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10000
num_epochs: 15.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.1985	0.22	2500	1.0940
1.0937	0.44	5000	1.0033
1.0675	0.66	7500	0.9753
1.0565	0.87	10000	0.9801
1.0244	1.09	12500	0.9526
0.9943	1.31	15000	0.9298
0.9799	1.53	17500	0.9035
0.95	1.75	20000	0.8835
0.933	1.97	22500	0.8636
0.9079	2.18	25000	0.8507
0.8938	2.4	27500	0.8397
0.8781	2.62	30000	0.8195
0.8647	2.84	32500	0.8088
0.8422	3.06	35000	0.7954
0.831	3.28	37500	0.7871
0.8173	3.5	40000	0.7721
0.8072	3.71	42500	0.7611
0.8011	3.93	45000	0.7532
0.7828	4.15	47500	0.7431
0.7691	4.37	50000	0.7367
0.7659	4.59	52500	0.7292
0.7606	4.81	55000	0.7245
0.8082	5.02	57500	0.7696
0.8114	5.24	60000	0.7695
0.8022	5.46	62500	0.7613
0.7986	5.68	65000	0.7558
0.8018	5.9	67500	0.7478
0.782	6.12	70000	0.7435
0.7743	6.34	72500	0.7367
0.774	6.55	75000	0.7313
0.7692	6.77	77500	0.7270
0.7604	6.99	80000	0.7200
0.7468	7.21	82500	0.7164
0.7486	7.43	85000	0.7117
0.7399	7.65	87500	0.7043
0.7306	7.86	90000	0.6956
0.7243	8.08	92500	0.6959
0.7132	8.3	95000	0.6916
0.71	8.52	97500	0.6853
0.7128	8.74	100000	0.6855
0.7088	8.96	102500	0.6809
0.7002	9.18	105000	0.6784
0.6953	9.39	107500	0.6737
0.695	9.61	110000	0.6714
0.6871	9.83	112500	0.6687
0.7161	10.05	115000	0.6961
0.7265	10.27	117500	0.7006
0.7284	10.49	120000	0.6941
0.724	10.7	122500	0.6887
0.7266	10.92	125000	0.6931
0.7051	11.14	127500	0.6846
0.7106	11.36	130000	0.6816
0.7011	11.58	132500	0.6830
0.6997	11.8	135000	0.6784
0.6969	12.02	137500	0.6734
0.6968	12.23	140000	0.6709
0.6867	12.45	142500	0.6656
0.6925	12.67	145000	0.6661
0.6795	12.89	147500	0.6606
0.6774	13.11	150000	0.6617
0.6756	13.33	152500	0.6563
0.6728	13.54	155000	0.6547
0.6732	13.76	157500	0.6520
0.6704	13.98	160000	0.6492
0.6666	14.2	162500	0.6446
0.6615	14.42	165000	0.6488
0.6638	14.64	167500	0.6523
0.6588	14.85	170000	0.6415

Framework versions

Transformers 4.12.5
Pytorch 1.10.1+cu113
Datasets 1.17.0
Tokenizers 0.10.3

Citing & Authors

If you use our work, please cite:

@incollection{Viegas_2023,
	doi = {10.1007/978-3-031-36805-9_24},
	url = {https://doi.org/10.1007%2F978-3-031-36805-9_24},
	year = 2023,
	publisher = {Springer Nature Switzerland},
	pages = {349--365},
	author = {Charles F. O. Viegas and Bruno C. Costa and Renato P. Ishii},
	title = {{JurisBERT}: A New Approach that~Converts a~Classification Corpus into~an~{STS} One},
	booktitle = {Computational Science and Its Applications {\textendash} {ICCSA} 2023}
}

alfaneo/bertimbaulaw-base-portuguese-cased

output

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Citing & Authors

alfaneo/
bertimbaulaw-base-portuguese-cased