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Code Generation On Django

Métriques

Accuracy

BLEU Score

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	BLEU Score	Paper Title	Repository
LUKEMarian	78.50	89.34	Leveraging pre-trained language models for code generation
Reranker	80.2	-	Reranking for Neural Semantic Parsing	-
MarianCG	81.83	90.41	MarianCG: a code generation transformer model inspired by machine translation
lpn (Ling et al., 2016)	62.3	77.6	Latent Predictor Networks for Code Generation	-
RoBERTaMarian	77.95	88.91	Leveraging pre-trained language models for code generation
Tranx	73.7	-	TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation	-
BERTMarian	76.68	56.55	Leveraging pre-trained language models for code generation
BERT + TAE	81.03	-	Code Generation from Natural Language with Less Prior and More Monolingual Data	-
TranX + BERT w/mined	81.03	79.86	The impact of lexical and grammatical processing on generating code from natural language	-
Phrasal Statistical MT (Ling et al., 2016)	31.5	47.6	Latent Predictor Networks for Code Generation	-
ELECTRAMarian	65.32	53.02	Leveraging pre-trained language models for code generation

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Code Generation On Django | SOTA | HyperAI