https://github.com/lyuqin/HydraNet-WikiSQL

Code and trained model for Hybrid ranking network for text-to-SQL on WikiSQL
https://github.com/lyuqin/HydraNet-WikiSQL

nl2sql text-to-sql wikisql

Last synced: 2 months ago
JSON representation

Code and trained model for Hybrid ranking network for text-to-SQL on WikiSQL

• Host: GitHub
• URL: https://github.com/lyuqin/HydraNet-WikiSQL
• Owner: lyuqin
• License: mit
• Created: 2020-07-29T19:08:43.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-12-20T21:10:52.000Z (about 1 year ago)
• Last Synced: 2024-08-18T11:13:39.371Z (6 months ago)
• Topics: nl2sql, text-to-sql, wikisql
• Language: Python
• Homepage: https://arxiv.org/abs/2008.04759
• Size: 17.6 KB
• Stars: 65
• Watchers: 2
• Forks: 18
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-Text2SQL - [code

README

        
# Hybrid Ranking Network for Text-to-SQL

Code for our paper [Hybrid Ranking Network for Text-to-SQL](https://arxiv.org/abs/2008.04759) 

## Environment Setup

* `Python 3.8`

* `Pytorch 1.7.1` or higher

* `pip install -r requirements.txt`

We can also run experiments with docker image:

`docker build -t hydranet -f Dockerfile .`

The built image above contains processed data and is ready for training and evaluation.

## Data Preprocessing

1. Create data folder and output folder first: `mkdir data && mkdir output`

2. Clone WikiSQL repo: 

`git clone https://github.com/salesforce/WikiSQL && tar xvjf WikiSQL/data.tar.bz2 -C WikiSQL`

3. Preprocess data:

`python wikisql_gendata.py`

   

## Training

1. Run `python main.py train --conf conf/wikisql.conf --gpu 0,1,2,3 --note "some note"`.

2. Model will be saved to `output` folder, named by training start datetime.

## Evaluation

1. Modify model, input and output settings in `wikisql_prediction.py` and run it.

2. Run WikiSQL evaluation script to get official numbers: `cd WikiSQL && python evaluate.py data/test.jsonl data/test.db ../output/test_out.jsonl`

Note: the WikiSQL evaluation script will encounter error when running in Windows system. Hence we included the fixed version for Windows User (run in root folder): `python wikisql_evaluate.py WikiSQL/data/test.jsonl WikiSQL/data/test.db output/test_out.jsonl`

## Trained Model

Trained model that can reproduce reported number on WikiSQL leaderboard is attached in the releases (see under "Releases" in the right column). Model prediction outputs are also attached.