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Text-To-SQL
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Text-to-SQL is a task in natural language processing (NLP) where the goal is to automatically generate SQL queries from natural language text. The task involves converting the text input into a structured representation and then using this representation to generate a semantically correct SQL query that can be executed on a database.
<span style="color:grey; opacity: 0.6">( Image credit: SyntaxSQLNet )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/vanoracai/Exploiting-Spatial-temporal-Relationships-for-3D-Pose-Estimation-via-Graph-Convolutional-Networks
https://github.com/vzhong/gazp
Grounded Adaptation for Zero-shot Executable Semantic Parsing
https://github.com/wronnyhuang/SQLNet_inference
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/xgenerationlab/xiyan_mcp_server
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
https://github.com/xiaojunxu/SQLNet
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/yangyucheng000/Paper-3/tree/main/acm-mm-2023-picr-master
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
https://github.com/yangyucheng000/University/tree/main/model-2/bert_generation
https://github.com/yangyucheng000/University/tree/main/model-2/esm
ESM+: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Models
https://github.com/yeounoh/lc_nl2sql
https://github.com/ygan/Spider-Syn
Towards Robustness of Text-to-SQL Models against Synonym Substitution
https://github.com/ygan/spider
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.