Skip to main content
Journal of Big Data

Table 6 BERTweet embedding extraction (EE) result with lr = 1e-4 and eps = 1e-4

From: A BERTweet-based design for monitoring behaviour change based on five doors theory on coral bleaching campaign

Scenario

Description

Maximum accuracy

Maximum F1 score

EE#1

Last Layer

0.7714

0.7298

EE#2

All 12 Layers

0.7633

0.7406

EE#3

Last 4 Layers

0.7673

0.7833

EE#4

Last 2 Layers

0.7510

0.7484

EE#5

First 2 + Last 2

0.7510

0.7445

EE#6

First + Last

0.7673

0.7388

EE#7

Last 2 + Mid 2

0.7510

0.7589

EE#8

Last + Mid

0.7673

0.7214

  1. Scenario EE#1 is bolded because it has the highest accuracy while scenario EE#3 is bolded because it has the highest F1 score
Back to article page