clasificador-ser-estar-window-3-bert-base

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

Model description

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The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 20

Training Loss	Epoch	Step	Validation Loss	F1 Score	Recall	Precision	Roc Auc
No log	1.0	25	0.6602	0.7465	1.0	0.5955	0.6476
No log	2.0	50	0.5149	0.8342	0.9873	0.7222	0.8281
No log	3.0	75	0.4397	0.8561	0.9916	0.7532	0.8710
No log	4.0	100	0.4293	0.8566	0.9325	0.7921	0.8897
No log	5.0	125	0.4076	0.8566	0.9325	0.7921	0.8882
No log	6.0	150	0.4416	0.8648	0.9578	0.7882	0.8802
No log	7.0	175	0.4894	0.8499	0.9198	0.7899	0.8800
No log	8.0	200	0.5129	0.8577	0.9283	0.7971	0.8907
No log	9.0	225	0.5474	0.8532	0.9198	0.7956	0.8817
No log	10.0	250	0.6858	0.8377	0.8819	0.7977	0.8750
No log	11.0	275	0.6811	0.8465	0.9072	0.7934	0.8740
No log	12.0	300	0.7265	0.8538	0.9367	0.7845	0.8761
No log	13.0	325	0.7422	0.8532	0.9198	0.7956	0.8825
No log	14.0	350	0.8648	0.8409	0.9030	0.7868	0.8743
No log	15.0	375	0.8326	0.8498	0.9072	0.7993	0.8816
No log	16.0	400	0.8516	0.8504	0.9114	0.7970	0.8776
No log	17.0	425	0.8633	0.8487	0.9114	0.7941	0.8862
No log	18.0	450	0.9064	0.8475	0.9030	0.7985	0.8856
No log	19.0	475	0.9145	0.8475	0.9030	0.7985	0.8856
0.2549	20.0	500	0.9146	0.8475	0.9030	0.7985	0.8860