Sentiment-jpn-Suzuki
Task Identifier: sentiment-2022-suzuki-jpn
Cluster: Sentiment Analysis
Data Type: jpn
Score Metric: Accuracy
Paper/GitHub/Website URL:


RankSubmission Title Model
URL
Score
Details
1
Finetuned LMs XLM-RoBERTa-Large
62.3333
2
Finetuned LMs TwHIN-BERT
61.8667
3
Finetuned LMs InfoDCL
61.4667
4
Finetuned LMs Bernice
61.1333
5
Finetuned LMs XLM-RoBERTa-Base
59.6667
6
Finetuned LMs XLM-Twitter
59.1333
7
Zero-shot Chatgpt
55.8
8
Finetuned LMs mBERT
54.3333
9
Zero-shot mT0-XL
47.6
10
Zero-shot Chatgpt with translated prompts
46.6
11
Zero-shot BLOOMZ-7B
45.2
12
Zero-shot BLOOMZ-P3-7B
44.2
13
three-shot in-context learning LLaMA-7B
38.4
14
five-shot in-context learning LLaMA-7B
36.2
15
five-shot in-context learning BLOOMZ-P3-7B
35
16
five-shot in-context learning BLOOM-7B
34.4
17
three-shot in-context learning BLOOMZ-P3-7B
33.6
18
Zero-shot Bactrian-BLOOM
33.4
19
Zero-shot Bactrian-LLaMA-7B
32.4
20
three-shot in-context learning BLOOM-7B
32.2
21
Baseline Majority
32.08
22
Zero-shot LLaMA-7B
32
23
Zero-shot BLOOM-7B
32
24
five-shot in-context learning mT0-XL
31.8
25
three-shot in-context learning mT0-XL
31
26
five-shot in-context learning Vicuna-7B
28
27
three-shot in-context learning Vicuna-7B
27.6
28
Zero-shot Vicuna-7B
26.8
29
five-shot in-context learning mT5-XL
26
30
three-shot in-context learning mT5-XL
25.8
31
Zero-shot mT5-XL
25.8
32
Zero-shot Alpaca-7B
19.6
33
Baseline Random
18