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Code Generation On Django

Metriken

Accuracy

BLEU Score

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	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