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Table 6 Experimental design scenarios

From: Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging

Scenario System baseline Batch size Learning Rate
1 BERT 16 1.00E−05
2 16 3.00E−05
3 16 1.00E−05
4 16 3.00E−05
5 32 1.00E−05
6 32 3.00E−05
7 32 1.00E−05
8 32 3.00E−05
9 Roberta 16 1.00E−05
10 16 3.00E−05
11 16 1.00E−05
12 16 3.00E−05
13 32 1.00E−05
14 32 3.00E−05
15 32 1.00E−05
16 32 3.00E−05
17 XL Net 16 1.00E−05
18 16 3.00E−05
19 16 1.00E−05
20 16 3.00E−05
21 32 1.00E−05
22 32 3.00E−05
23 32 1.00E−05
24 32 3.00E−05
25 BERT + NLP Statistical Features 16 1.00E−05
26 16 3.00E−05
27 16 1.00E−05
28 16 3.00E−05
29 32 1.00E−05
30 32 3.00E−05
31 32 1.00E−05
32 32 3.00E−05
33 Roberta + NLP Statistical Features 16 1.00E−05
34 16 3.00E−05
35 16 1.00E−05
36 16 3.00E−05
37 32 1.00E−05
38 32 3.00E−05
39 32 1.00E−05
40 32 3.00E−05
41 XLNet + NLP Statistical Features 16 1.00E−05
42 16 3.00E−05
43 16 1.00E−05
44 16 3.00E−05
45 32 1.00E−05
46 32 3.00E−05
47 32 1.00E−05
48 32 3.00E−05
49 Proposed Method (Model averaging (BERT + ROBERTA + XLNet)) + NLP Statistical Features 16 1.00E−05
50 16 3.00E−05
51 16 1.00E−05
52 16 3.00E−05
53 32 1.00E−05
54 32 3.00E−05
55 32 1.00E−05
56 32 3.00E−05