Code Generation on Django

Metric: Accuracy (higher is better)

LeaderboardDataset

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	MarianCG	81.83	No	-	-	Code
2	TranX + BERT w/mined	81.03	No	The impact of lexical and grammatical processing...	2022-02-28	Code
3	BERT + TAE	81.03	No	Code Generation from Natural Language with Less ...	2021-01-01	Code
4	Reranker	80.2	No	-	-	-
5	LUKEMarian	78.5	No	-	-	Code
6	RoBERTaMarian	77.95	No	-	-	Code
7	BERTMarian	76.68	No	-	-	Code
8	Tranx	73.7	No	TRANX: A Transition-based Neural Abstract Syntax...	2018-10-05	Code
9	ELECTRAMarian	65.32	No	-	-	Code
10	lpn (Ling et al., 2016)	62.3	No	Latent Predictor Networks for Code Generation	2016-03-22	Code
11	Phrasal Statistical MT (Ling et al., 2016)	31.5	No	Latent Predictor Networks for Code Generation	2016-03-22	Code

#1MarianCG
81.83
Accuracy
No paperCode
#2TranX + BERT w/mined
81.03
Accuracy· 2022-02-28
The impact of lexical and grammatical processing on generating code from natural language Code
#3BERT + TAESOTA
81.03
Accuracy· 2021-01-01
Code Generation from Natural Language with Less Prior and More Monolingual Data Code
#4Reranker
80.2
Accuracy
No paper
#5LUKEMarian
78.5
Accuracy
No paperCode
#6RoBERTaMarian
77.95
Accuracy
No paperCode
#7BERTMarian
76.68
Accuracy
No paperCode
#8TranxSOTA
73.7
Accuracy· 2018-10-05
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation Code
#9ELECTRAMarian
65.32
Accuracy
No paperCode
#10lpn (Ling et al., 2016)SOTA
62.3
Accuracy· 2016-03-22
Latent Predictor Networks for Code Generation Code
#11Phrasal Statistical MT (Ling et al., 2016)
31.5
Accuracy· 2016-03-22
Latent Predictor Networks for Code Generation Code