NLU: domain-intent-slot; text2SQL
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Updated
Apr 18, 2020
NLU: domain-intent-slot; text2SQL
Convert natural language query to appropriate SQL, make ERPs cool again.
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
Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data
The dataset and source code for our paper: "Did You Ask a Good Question? A Cross-Domain Question IntentionClassification Benchmark for Text-to-SQL"
🌶️ R²SQL: "Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing." (AAAI 2021)
The Resources for "Natural Language to Logical Form" ; "自然语言转逻辑形式"研究资料收集。
Python 3 reimplementation of SyntaxSQLNet, including several improvements
Repositório referente a mentoria Desmestificando Banco de Dados SQL e NoSQL com ChatGT. Mentoria para os alunos participantes dos Bootcamps oferecido pela DIO em parceria com o Santander.
[ICML 2023] Official code for our paper: 'Conditional Tree Matching for Inference-Time Adaptation of Tree Prediction Models'
Project proposal for solving the "Talk to your data" task at hackathon "HackYeah 2023"
Text2SQL project comparing different LLM models
GAP-text2SQL: Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Content Enhanced BERT-based Text-to-SQL Generation https://arxiv.org/abs/1910.07179
LLM evaluation framework
Polish translation of spider dataset.
Fine Tuning is a cost-efficient way of preparing a model for specialized tasks. Fine-tuning reduces required training time as well as training datasets. We have open-source pre-trained models. Hence, we do not need to perform full training every time we create a model.
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