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Semantic Parsing
Semantic Parsing On Spider
Semantic Parsing On Spider
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Columns
Model Name
Accuracy
Paper Title
Repository
code-davinci-002 175B (LEVER)
81.9
LEVER: Learning to Verify Language-to-Code Generation with Execution
T5-3B + PICARD
71.9
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
RATSQL + BERT
65.6
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
RESDSQL-3B + NatSQL
84.1
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
Exact Set Matching
19.7
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
Graphix-3B + PICARD
74.0
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing
SADGA + GAP
70.1
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL
RASAT+PICARD
75.5
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL
RATSQL + GAP
69.7
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
RATSQL + Grammar-Augmented Pre-Training
69.6
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
0 of 10 row(s) selected.
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