https://github.com/guotong1988/weak-supervised-Rule-Text2SQL
Using Database Rule for Weak Supervised Text-to-SQL Generation
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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/heyanger/sqltools
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task
https://github.com/huyz1117/Non_Local_Net_TensorFlow
https://github.com/ist-daslab/rosa
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/jiajunhua/facebookresearch-Detectron
https://github.com/jordiae/DeepLearning-MAI
https://github.com/jpviguerasguillen/feedback-non-local-attention-fnla
https://github.com/jpviguerasguillen/feedback-non-local-attention_fNLA
https://github.com/kasnerz/tabgenie
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/kh-mo/QA_wikisql
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/llSourcell/SQL_Database_Optimization
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/louis-li/SeqGenSQL
SeqGenSQL -- A Robust Sequence Generation Model for Structured Query Language
https://github.com/microsoft/IRNet
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
https://github.com/mirthai/csa-net
https://github.com/neerajbhat98/ValueNet
ValueNet: A Natural Language-to-SQL System that Learns from Database Information
https://github.com/nikhilsab/h-star
TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition
https://github.com/open-mmlab/mmaction2
https://github.com/openbotai/nl2sql
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/owena11/papers
https://github.com/pwc-1/Paper-5/tree/main/mbart
https://github.com/racheljose21/chatbot
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
https://github.com/realsonalkumar/Mish-Mash-Hackathon
https://github.com/rijuldhir/TSM
https://github.com/rshin/seq2struct
Encoding Database Schemas with Relation-Aware Self-Attention for Text-to-SQL Parsers
https://github.com/ryanzhumich/sparc_atis_pytorch
https://github.com/salokr/email-event-extraction
Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing
https://github.com/seominseok0429/inception-I3D-NON-LOCAL
https://github.com/servicenow/picard
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
https://github.com/shivam017arora/Conversational-BI
https://github.com/sunutf/TSM
https://github.com/taoyds/grappa
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
https://github.com/taoyds/sparc
https://github.com/tea1528/Non-Local-NN-Pytorch
https://github.com/tidbcloud/chat2query_bench
Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models
https://github.com/tinymilky/RSANet
https://github.com/tiwarikajal/Seq2SQL-
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.