Linguistic Acceptability on CoLA Dev

Metric: Accuracy (higher is better)

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	En-BERT + TDA	88.6	No	Acceptability Judgements via Examining the Topol...	2022-05-19	Code
2	XLM-R (pre-trained) + TDA	73	No	Acceptability Judgements via Examining the Topol...	2022-05-19	Code
3	DeBERTa (large)	69.5	No	DeBERTa: Decoding-enhanced BERT with Disentangle...	2020-06-05	Code
4	TinyBERT-6 67M	54	No	TinyBERT: Distilling BERT for Natural Language U...	2019-09-23	Code
5	Synthesizer (R+V)	53.3	No	Synthesizer: Rethinking Self-Attention in Transf...	2020-05-02	Code

#1En-BERT + TDASOTA
88.6
Accuracy· 2022-05-19
Acceptability Judgements via Examining the Topology of Attention Maps Code
#2XLM-R (pre-trained) + TDA
73
Accuracy· 2022-05-19
Acceptability Judgements via Examining the Topology of Attention Maps Code
#3DeBERTa (large)SOTA
69.5
Accuracy· 2020-06-05
DeBERTa: Decoding-enhanced BERT with Disentangled Attention Code
#4TinyBERT-6 67MSOTA
54
Accuracy· 2019-09-23
TinyBERT: Distilling BERT for Natural Language Understanding Code
#5Synthesizer (R+V)
53.3
Accuracy· 2020-05-02
Synthesizer: Rethinking Self-Attention in Transformer Models Code