lisaterumi
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Create README.md
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README.md
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---
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language: "pt"
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widget:
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- text: "Dispneia importante aos esforços + dor tipo peso no peito no esforço."
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- text: "Obeso, has, icc c # cintilografia miocardica para avaliar angina. Discreto edema mmii pricn a esquerda."
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- text: "Plastia Mitral ( Insuficiencia ), CRM Saf-2Mg e e Saf-3MG ).(09/03/16). Nega palpitação."
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- text: "Uso: AAS 100 -1xd; Metoprolol 25 -1xd; FSM -1xd ; Levotiroxina 175 -1xd; Sinva 40 -1xd; Fluoxetina 20-1xd."
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- text: "Refere melhora da dispneia depois da cx porem mantem aos mdoeardos-leves esforço."
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datasets:
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- TempClinBr
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---
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# Portuguese NER- TempClinBr - BioBERTpt(all)
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Treinado com BioBERTpt(all), com o corpus TempClinBr.
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Metricas:
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```
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precision recall f1-score support
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0 0.75 0.90 0.82 291
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1 0.77 1.00 0.87 33
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2 1.00 0.25 0.40 28
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3 0.90 0.99 0.94 71
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4 0.79 0.91 0.85 112
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5 0.72 0.83 0.77 420
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6 0.62 0.45 0.53 11
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7 0.96 0.85 0.91 2236
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8 0.61 0.67 0.64 78
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9 0.61 0.98 0.76 124
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10 0.81 0.87 0.84 503
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11 0.67 0.60 0.63 10
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accuracy 0.86 3917
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macro avg 0.77 0.78 0.74 3917
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weighted avg 0.87 0.86 0.86 3917
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F1: 0.8588744393393593 Accuracy: 0.8565228491192239
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```
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Parâmetros:
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```
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device = cuda (Colab)
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nclasses = len(tag2id)
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nepochs = 50 => parou na 9
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batch_size = 16
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batch_status = 32
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learning_rate = 3e-5
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early_stop = 5
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max_length = 256
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write_path = 'model'
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```
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Eval no conjunto de teste - TempClinBr
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OBS: Avaliação com tag "O" (label 7), se necessário fazer a média sem essa tag.
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```
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{'B-Tratamento': 0,
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'I-Teste': 1,
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'I-Ocorrencia': 2,
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'B-Evidencia': 3,
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'B-Teste': 4,
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'I-Problema': 5,
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'B-DepartamentoClinico': 6,
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'O': 7,
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'I-Tratamento': 8,
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'B-Ocorrencia': 9,
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'B-Problema': 10,
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'I-DepartamentoClinico': 11,
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'<pad>': 12}
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precision recall f1-score support
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0 0.82 0.92 0.87 261
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1 0.81 0.58 0.67 99
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2 0.56 0.20 0.29 51
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3 1.00 0.94 0.97 128
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4 0.81 0.86 0.83 194
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5 0.81 0.87 0.84 645
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6 0.96 0.80 0.87 30
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7 0.95 0.90 0.93 2431
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8 0.73 0.81 0.77 146
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9 0.74 0.88 0.80 146
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10 0.87 0.95 0.91 713
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11 0.83 0.71 0.77 14
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12 0.00 0.00 0.00 0
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accuracy 0.89 4858
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macro avg 0.76 0.72 0.73 4858
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weighted avg 0.89 0.89 0.89 4858
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```
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Como citar: **em breve**
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