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As of my last knowledge update in January 2022, there were several text-to-SQL (TXT2SQL) models that showed promise in converting natural language queries to SQL queries. Keep in mind that the field of natural language processing (NLP) is rapidly evolving, and newer models may have been developed since then. Here are some models that were notable at that time:
Seq2SQL:
Description: Seq2SQL is an early model that used a sequence-to-sequence architecture for generating SQL queries from natural language questions. It was trained on a large dataset of question-SQL pairs.
Paper: Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
SQLNet:
Description: SQLNet is an improvement over Seq2SQL, utilizing a more sophisticated attention mechanism. It focuses on predicting the columns and conditions separately and then combining them to generate the final SQL query.
Paper: SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
BERT for SQL Query Generation:
Description: Some researchers have explored using pre-trained language models like BERT for SQL query generation tasks. By fine-tuning BERT on a dataset of natural language SQL queries, these models aim to capture contextual information effectively.
Paper: Investigating BERT's Knowledge of Language: Five Analysis Methods with NLP Benchmarks
Spider:
Description: Spider is a dataset and a challenge designed for evaluating the performance of text-to-SQL models. It includes a diverse set of databases and complex queries. Several models have been benchmarked on the Spider dataset.
Paper: Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
TAPAS (Tabular Pre-trained Language Model):
Description: TAPAS is designed specifically for tabular data. It extends BERT to understand and generate queries for tabular data, making it suitable for tasks involving databases with tables.
Paper: TAPAS: Entity-Wide Search for Tabular Data
Keep in mind that the field of text-to-SQL and natural language processing, in general, is dynamic, and newer models may have been introduced since my last update. It's a good idea to check the latest literature and research publications for the most recent advances in TXT2SQL models.
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面试中巧妙的提问 (2006-9-25) poloand 
楼主:jungle
发表于 2023-11-22 14:47
发表于 2023-11-22 16:57
