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Table 7 Model performance without using the [CLS] token in the last layer

From: Investigation of improving the pre-training and fine-tuning of BERT model for biomedical relation extraction

Model

PPI

DDI

ChemProt

P

R

F

P

R

F

P

R

F

BioBERT

79.0

83.3

81.0

79.9

78.1

79.0

74.3

76.3

75.3

BioBERT_SLL_Att

80.7

84.4

82.5

81.3

80.1

80.7

76.5

77.1

76.8

BioBERT_SLL_Att*

82.3

83.5

82.8

79.7

77.6

78.6

76.4

74.5

75.4

PubMedBERT

80.1

84.3

82.1

82.6

81.9

82.3

78.8

75.9

77.3

PubMedBERT_SLL_Att

81.3

85.0

83.1

84.3

82.7

83.5

78.3

77.6

77.9

PubMedBERT_SLL_Att*

80.0

85.2

82.4

82.5

80.9

81.7

75.7

77.7

76.7

  1. Bold values indicate better results
  2. P: Precision; R: Recall; F: F1 Score; BERT_SLL_Att*: models of fine-tuning with only the summarized information from attention mechanism (without [CLS] token)