https://github.com/x-lance/text2sql-gpt
[EMNLP 2023 Findings] ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought
https://github.com/x-lance/text2sql-gpt
Last synced: 5 months ago
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[EMNLP 2023 Findings] ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought
- Host: GitHub
- URL: https://github.com/x-lance/text2sql-gpt
- Owner: X-LANCE
- Created: 2023-10-20T11:28:25.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-01-11T07:09:39.000Z (over 1 year ago)
- Last Synced: 2024-11-12T20:12:06.570Z (7 months ago)
- Language: Python
- Homepage:
- Size: 60.5 KB
- Stars: 16
- Watchers: 5
- Forks: 4
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# ACT-SQL
This is the project containing the source code for the EMNLP2023 paper [*ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought*](https://arxiv.org/abs/2310.17342) in **EMNLP 2023 findings**. If you find it useful, please cite our work.
@misc{zhang2023actsql,
title={ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought},
author={Hanchong Zhang and Ruisheng Cao and Lu Chen and Hongshen Xu and Kai Yu},
year={2023},
eprint={2310.17342},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
## Run ACT-SQL
1. Create the `data` directory and move the downloaded datasets into this directory.
2. Create the `plm` directory and move the downloaded pretrained sentence BERT models into this directory.
3. As for the multi-turn text-to-SQL dataset, run `multiturn.py` firstly to convert the dataset into the single-turn text-to-SQL dataset. Here is an example.
```
python multiturn.py --dataset sparc
```
4. Run `cot.py` to automatically generate the chain-of-thoughts for all examples in the train set. Here is an example.
```
python cot.py --dataset spider
```
5. Run `main.py` to run ACT-SQL on the dev set. Here is an example.
```
python main.py --dataset spider --cot
```