Portuguese NER- TempClinBr - BioBERTpt(clin)
Treinado com BioBERTpt(clin), com o corpus TempClinBr.
Metricas:
precision recall f1-score support
0 1.00 0.85 0.92 33
1 0.73 0.69 0.71 78
2 0.75 0.55 0.63 11
3 0.70 0.70 0.70 10
4 0.90 1.00 0.95 71
5 0.75 0.90 0.82 503
6 0.83 0.90 0.87 112
7 0.93 0.90 0.92 2236
8 0.78 0.50 0.61 28
9 0.82 0.84 0.83 291
10 0.79 0.96 0.87 124
11 0.82 0.73 0.77 420
accuracy 0.87 3917
macro avg 0.82 0.79 0.80 3917
weighted avg 0.88 0.87 0.87 3917
Parâmetros:
device = cuda (Colab)
nclasses = len(tag2id)
nepochs = 50 => parou na 16
batch_size = 16
batch_status = 32
learning_rate = 3e-5
early_stop = 5
max_length = 256
write_path = 'model'
Eval no conjunto de teste - TempClinBr OBS: Avaliação com tag "O" (label 7), se necessário fazer a média sem essa tag.
tag2id ={'<pad>': 12,
'B-DepartamentoClinico': 2,
'B-Evidencia': 4,
'B-Ocorrencia': 10,
'B-Problema': 5,
'B-Teste': 6,
'B-Tratamento': 9,
'I-DepartamentoClinico': 3,
'I-Ocorrencia': 8,
'I-Problema': 11,
'I-Teste': 0,
'I-Tratamento': 1,
'O': 7}
precision recall f1-score support
0 0.70 0.30 0.42 99
1 0.84 0.75 0.79 146
2 1.00 0.90 0.95 30
3 0.93 0.93 0.93 14
4 1.00 0.95 0.98 128
5 0.83 0.97 0.89 713
6 0.80 0.80 0.80 194
7 0.93 0.93 0.93 2431
8 0.56 0.20 0.29 51
9 0.86 0.85 0.85 261
10 0.77 0.88 0.82 146
11 0.85 0.82 0.83 645
12 0.00 0.00 0.00 0
accuracy 0.88 4858
macro avg 0.77 0.71 0.73 4858
weighted avg 0.88 0.88 0.88 4858
Como citar: em breve
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