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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/JD-AI-Research-Silicon-Valley/auxiliary-task-for-text-to-sql
https://github.com/JiaPeng1234/MRI-Segmentation-Transformer
https://github.com/LRacoci/permutation-graphml
https://github.com/LittleYUYU/Interactive-Semantic-Parsing
Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study
https://github.com/MIT-HAN-LAB/temporal-shift-module
https://github.com/MS-Mind/MS-Code-06/tree/main/Non_local
https://github.com/MS-Mind/MS-Code-08/tree/main/Non_local
https://github.com/Microsoft/PointerSQL
Robust Text-to-SQL Generation with Execution-Guided Decoding
https://github.com/Mind23-2/MindCode-101/tree/main/Non_local
https://github.com/Mind23-2/MindCode-3/tree/main/I3D
https://github.com/MindSpore-paper-code-2/code2/tree/main/Non_local
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/text_to_sql/IGSQL
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/text_to_sql/RAT-SQL
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
https://github.com/PriyankaDatar/NLP_Project_Modfications
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/PriyankaDatar/NLP_SQL_Bert
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
https://github.com/PriyankaDatar/NLP_SQL_Project
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/PriyankaDatar/NLP_project_BIDAF
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
https://github.com/TooTouch/SPARTA
https://github.com/WavesUR/embedded_TSM
https://github.com/XGenerationLab/M-Schema
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
https://github.com/XGenerationLab/XiYan-DateResolver
A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL
https://github.com/abhishekchugh17/sql12
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/amazon-science/SDFeedback
https://github.com/amazon-science/self_debug
https://github.com/amolk/editsql
https://github.com/awslabs/diagnostic-robustness-text-to-sql
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
https://github.com/awslabs/gap-text2sql
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
https://github.com/bird-bench/mini_dev
https://github.com/changliu816/CV-paper-review
https://github.com/cmsflash/efficient-attention
https://github.com/code-implementation1/Code6/tree/main/Non_local
https://github.com/facebookresearch/SlowFast
https://github.com/facebookresearch/detectron
https://github.com/flitternie/graphq_ir
Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations
https://github.com/franknb/Self-attention-DCGAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.