A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL
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Updated
Aug 5, 2024 - Python
A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL
Content Enhanced BERT-based Text-to-SQL Generation https://arxiv.org/abs/1910.07179
GAP-text2SQL: Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
A solution guidance for Generative BI using Amazon Bedrock, Amazon OpenSearch with RAG
Convert natural language query to appropriate SQL, make ERPs cool again.
🌶️ R²SQL: "Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing." (AAAI 2021)
The dataset and source code for our paper: "Did You Ask a Good Question? A Cross-Domain Question IntentionClassification Benchmark for Text-to-SQL"
Table2answer: Read the database and answer without SQL https://arxiv.org/abs/1902.04260
Using Database Rule for Weak Supervised Text-to-SQL Generation https://arxiv.org/abs/1907.00620
Python 3 reimplementation of SyntaxSQLNet, including several improvements
[ICML 2023] Official code for our paper: 'Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models'
Polish translation of spider dataset.
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