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https://github.com/Microsoft/rat-sql
A relation-aware semantic parsing model from English to SQL
https://github.com/Microsoft/rat-sql
dbqa nl2sql nlp program-synthesis question-answering semantic-parsing transformers
Last synced: 19 days ago
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A relation-aware semantic parsing model from English to SQL
- Host: GitHub
- URL: https://github.com/Microsoft/rat-sql
- Owner: microsoft
- License: mit
- Created: 2020-05-19T03:01:10.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-08-22T16:19:45.000Z (over 1 year ago)
- Last Synced: 2024-04-08T00:52:13.194Z (9 months ago)
- Topics: dbqa, nl2sql, nlp, program-synthesis, question-answering, semantic-parsing, transformers
- Language: Python
- Homepage: https://arxiv.org/abs/1911.04942
- Size: 110 KB
- Stars: 390
- Watchers: 18
- Forks: 117
- Open Issues: 41
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
Awesome Lists containing this project
- Awesome-Text2SQL - [code
README
# RAT-SQL
This repository contains code for the ACL 2020 paper ["RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers"](https://arxiv.org/abs/1911.04942).
If you use RAT-SQL in your work, please cite it as follows:
``` bibtex
@inproceedings{rat-sql,
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"
}
```
## Changelog
**2020-08-14:**
- 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.
## Usage
### Step 1: Download third-party datasets & dependencies
Download the datasets: [Spider](https://yale-lily.github.io/spider) and [WikiSQL](https://github.com/salesforce/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:
```
/path/to/data
├── 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:
``` bash
mkdir -p third_party
git clone https://github.com/salesforce/WikiSQL 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:
``` bash
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](https://hub.docker.com/editions/community/docker-ce-desktop-mac/) and [Docker Desktop for Windows](https://hub.docker.com/editions/community/docker-ce-desktop-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](https://stanfordnlp.github.io/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:
``` bash
python run.py preprocess experiment_config_file # Step 3a: preprocess the data
python run.py train experiment_config_file # Step 3b: train a model
python run.py 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](https://arxiv.org/abs/1911.04942) for details.
## Contributing
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 https://cla.opensource.microsoft.com.
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](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [[email protected]](mailto:[email protected]) with any additional questions or comments.