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This repository contains code for the ACL 2020 paper "RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers".

If you use RAT-SQL in your work, please cite it as follows:

    title = "{RAT-SQL}: Relation-Aware Schema Encoding and Linking for Text-to-{SQL} Parsers",
    author = "Wang, Bailin and Shin, Richard and Liu, Xiaodong and Polozov, Oleksandr and Richardson, Matthew",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    pages = "7567--7578"



  • The Docker image now inherits from a CUDA-enabled base image.
  • Clarified memory and dataset requirements on the image.
  • Fixed the issue where token IDs were not converted to word-piece IDs for BERT value linking.


Step 1: Download third-party datasets & dependencies

Download the datasets: Spider and WikiSQL. In case of Spider, make sure to download the 08/03/2020 version or newer. Unpack the datasets somewhere outside this project to create the following directory structure:

├── spider
│   ├── database
│   │   └── ...
│   ├── dev.json
│   ├── dev_gold.sql
│   ├── tables.json
│   ├── train_gold.sql
│   ├── train_others.json
│   └── train_spider.json
└── wikisql
    ├── dev.db
    ├── dev.jsonl
    ├── dev.tables.jsonl
    ├── test.db
    ├── test.jsonl
    ├── test.tables.jsonl
    ├── train.db
    ├── train.jsonl
    └── train.tables.jsonl

To work with the WikiSQL dataset, clone its evaluation scripts into this project:

mkdir -p third_party
git clone third_party/wikisql

Step 2: Build and run the Docker image

We have provided a Dockerfile that sets up the entire environment for you. It assumes that you mount the datasets downloaded in Step 1 as a volume /mnt/data into a running image. Thus, the environment setup for RAT-SQL is:

docker build -t ratsql .
docker run --rm -m4g -v /path/to/data:/mnt/data -it ratsql

Note that the image requires at least 4 GB of RAM to run preprocessing. By default, Docker Desktop for Mac and Docker Desktop for Windows run containers with 2 GB of RAM. The -m4g switch overrides it; alternatively, you can increase the default limit in the Docker Desktop settings.

If you prefer to set up and run the codebase without Docker, follow the steps in Dockerfile one by one. Note that this repository requires Python 3.7 or higher and a JVM to run Stanford CoreNLP.

Step 3: Run the experiments

Every experiment has its own config file in experiments. The pipeline of working with any model version or dataset is:

python preprocess experiment_config_file  # Step 3a: preprocess the data
python train experiment_config_file       # Step 3b: train a model
python eval experiment_config_file        # Step 3b: evaluate the results

Use the following experiment config files to reproduce our results:

  • Spider, GloVE version: experiments/spider-glove-run.jsonnet
  • Spider, BERT version (requires a GPU with at least 16GB memory): experiments/spider-bert-run.jsonnet
  • WikiSQL, GloVE version: experiments/wikisql-glove-run.jsonnet

The exact model accuracy may vary by ±2% depending on a random seed. See paper for details.


This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact with any additional questions or comments.