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Sentiment Analysis on SST-5 (test)

62.27Accuracy

CAPO

Updated 1mo ago

Evaluation Results

Method	Links
CAPO 2025.04		62.27
OPRO 2025.04		60.87
EvoPromptGA 2025.04		60.53
CAPO 2025.04		60.2
Fine-tuning* 2023.05		59.9
CAPO 2025.04		59.07
EvoPromptGA 2025.04		58.6
Initial 2025.04		58.47
OPRO 2025.04		57
Initial 2025.04		56.68
CAPO 2025.12		56.3
OPRO 2025.12		56
AutoDiff 2025.12		55.7
LoRA-nBAR 2024.10		55
EvoPromptGA 2025.04		54.93
BCN+ELMo 2018.02		54.7
ELMo + Baseline 2018.02		54.7
LoRA-oBAR 2024.10		54.5
LoRA-SAM 2024.10		54.2
BCN+Char+CoVe 2017.08		53.7
BCN+Char+CoVe 2018.02		53.7
McCann et al. (2017) 2018.02		53.7
EvoPrompt 2025.12		53.3
OPRO 2025.04		53.2
NTI 2017.08		53.1
NTI 2018.02		53.1
TE-LSTM 2017.08		52.6
LSTM-CNN 2017.08		52.4
LSTM-CNN 2018.02		52.4
LoRA 2024.10		52.3
DMN 2017.08		52.1
DMN 2015.06		52.1
DMN 2018.02		52.1
MVN 2017.08		51.5
d-TBCNN 2015.04		51.4
Our baseline 2018.02		51.4
Molding-CNN 2015.11		51.2
T-CNN 2016.01		51.2
Constituency Tree-LSTM 2015.02		51
Constituency Tree-LSTM 2015.11		51
CT-LSTM 2015.06		51
CT-LSTM 2016.01		51
Tree LSTM (variant 2) 2015.04		50.6
CNN with self-attention 2018.08		50.6
Dynamic Self-Attention 2018.08		50.6
c-TBCNN 2015.04		50.4
BiLSTM with self-attention 2018.08		50.4
Tree LSTM (variant 3) 2015.04		49.9
DRNN 2015.02		49.8
Deep RNN 2015.04		49.8
DRNN 2015.11		49.8
DRNN 2015.06		49.8
DRNN 2016.01		49.8
Constituency Tree-LSTM 2015.02		49.7
BiLSTM 2018.08		49.5
C-LSTM 2015.11		49.2
Bidirectional LSTM 2015.02		49.1
bi-LSTM 2015.04		49.1
TE-MNLI 2023.05		48.8
Paragraph-Vec 2015.02		48.7
Paragraph vector 2015.04		48.7
Paragraph Vector 2015.11		48.7
PVec 2015.06		48.7
PV 2016.01		48.7
Initial 2025.04		48.69
DCNN 2015.02		48.5
2-layer Bidirectional LSTM 2015.02		48.5
Deep CNN 2015.04		48.5
DCNN 2015.11		48.5
DCNN 2015.06		48.5
DCNN 2016.01		48.5
Dependency Tree-LSTM 2015.02		48.4
Dependency Tree-LSTM 2015.11		48.4
CNN-non-static 2015.02		48
Non-static 2015.04		48
Tree LSTM (variant 1) 2015.04		48
CNN-non-static 2015.11		48
CNN-MC 2016.01		48
CNN 2018.08		48
2-layer LSTMN 2016.01		47.9
Bi-LSTM 2015.11		47.8
TE-MNLI 2023.05		47.7
LSTMN 2016.01		47.6
CNN-multichannel 2015.02		47.4
Multichannel 2015.04		47.4
CNN-multichannel 2015.11		47.4
CNN-MC 2015.06		47.4
SSTuning-large 2023.05		46.9
LSTM 2015.11		46.6
LSTM 2015.02		46.4
LSTM 2016.01		46.4
2-layer LSTM 2015.02		46
2-layer LSTM 2016.01		46
LSTM 2015.04		45.8
RNTN 2015.02		45.7
Tensor 2015.04		45.7
RNTN 2015.11		45.7
RNTN 2015.06		45.7
RNTN 2016.01		45.7
UniMC (Rerun) 2023.05		45.6

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