Home Console Docs News Papers Tutorials Datasets Wiki SOTA LLM Models GPU Leaderboard Events

English

Code Generation On Wikisql

Metrics

Exact Match Accuracy

Execution Accuracy

Results

Performance results of various models on this benchmark

			Paper Title	Repository
NL2SQL-RULE	83.7	89.2	Content Enhanced BERT-based Text-to-SQL Generation
TypeSQL+TC (Yu et al., 2018)+	-	82.6	TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation
Tranx	68.6	78.6	TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
STAMP+RL (Sun et al., 2018)+	61.0	74.6	Semantic Parsing with Syntax- and Table-Aware SQL Generation	-
STAMP (Sun et al., 2018)+	60.7	74.4	Semantic Parsing with Syntax- and Table-Aware SQL Generation	-
TypeSQL (Yu et al., 2018)	-	73.5	TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation
PT-MAML (Huang et al., 2018)	62.8	68.0	Natural Language to Structured Query Generation via Meta-Learning
Bidirectional Attention for SQL Generation	69	62.5	Bidirectional Attention for SQL Generation
Seq2SQL (Zhong et al., 2017)	48.3	59.4	Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Seq2Seq (Zhong et al., 2017)	23.4	35.9	Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

0 of 10 row(s) selected.

Code Generation On Wikisql | SOTA | HyperAI