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T<span style="font-size:0.8em;">A</span>PE<span style="font-size:0.8em;">X</span>: Table Pre-training via Learning a Neural SQL Executor

The official repository which contains the code and pre-trained models for our paper T<span style="font-size:0.8em;">A</span>PE<span style="font-size:0.8em;">X</span>: Table Pre-training via Learning a Neural SQL Executor.

🔥 Updates

• [2022-10-10]: We released the data generator code for SQL execution data synthesis. You can check it out and try to synthesize your own pre-training data.
• [2022-04-19]: TAPEX is officially supported by 🤗 transformers! Now you can find the example fine-tuning script and the tapex model on the huggingface master branch. Have fun!
• [2022-03-09]: We have fixed the issue in tapex-large! Now you can view here to see how to fine-tune TAPEX using 🤗 transformers and 🤗 datasets! They will be merged into the main library soon!
• [2022-02-20]: Our paper is accepted by ICLR 2022! We also provided a fine-tuning script based on 🤗 transformers, which is not merged now. You can see the preview version here. ⚠️ It is worth noting that tapex-large is not well-prepared now. We found there is a strange bug in bart-large, which also affects tapex-large. Hope it is solved in the near future.
• [2021-10-25]: We released the code for Table Pre-training. You can check it out and try pre-training on your data!
• [2021-10-01]: We released the code for TableFT and the fine-tuned model weights on TabFact!
• [2021-08-28]: We released the fine-tuned model weights on WikiSQL, SQA and WikiTableQuestions!
• [2021-08-27]: We released the code, the pre-training corpus, and the pre-trained TAPEX model weights. Thanks for your patience!
• [2021-07-16]: We released our paper and home page. Check it out!

🏴󠁶󠁵󠁭󠁡󠁰󠁿 Overview

In the paper, we present T<span class="span-small">A</span>PE<span class="span-small">X</span> (for Table Pre-training via Execution), a conceptually simple and empirically powerful pre-training approach to empower existing generative pre-trained models (e.g., BART in our paper) with table reasoning skills. T<span class="span-small">A</span>PE<span class="span-small">X</span> realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries.

The central point of T<span class="span-small">A</span>PE<span class="span-small">X</span> is to train a model to mimic the SQL query execution process over a table. We believe that if a model can be trained to faithfully execute SQL queries, then it must have a deep understanding of table structures and possess an inductive bias towards table structures.

Meanwhile, since the diversity of SQL queries can be guaranteed systemically, and thus a diverse and high-quality pre-training corpus can be automatically synthesized for T<span class="span-small">A</span>PE<span class="span-small">X</span>.

Project

This project contains two parts, tapex library and examples to employ it on different table-related applications (e.g., Table Question Answering).

• For tapex, there is an overview:

|-- common
    |-- dbengine.py # the database engine to return answer for a SQL query
    |-- download.py # download helper for automatic resource
|-- data_utils
    |-- wikisql
        |-- executor.py # the re-implementation of WikiSQL style SQL execution to obtain ground-truth answers in the dataset
    |-- format_converter.py # convert dataset formats into HuggingFace style
    |-- preprocess_binary.py # wrapper for the fairseq preprocess script
    |-- preprocess_bpe.py # wrapper for the BPE preprocess
|-- processor
    |-- table_linearize.py # the class to flatten a table into a linearized form, which should keep consistent during pre-training, fine-tuning and evaluating
    |-- table_truncate.py # the class to truncate a long table into a shorter version to satisfy model's input length limit (e.g., BART can accept at most 1024 tokens)
    |-- table_processor.py # the wrapper for the above two table utility function classes
|-- model_eval.py # evaluate the denotation accuracy of model
|-- model_interface.py # wrap a model interface for interaction based on HubInterface

• For examples, please refer to here for more details.

⚡️ Quickstart

Prepare Environment

First, you should set up a python environment. This code base has been tested under python 3.x, and we officially support python 3.8.

After installing python 3.8, we strongly recommend you to use virtualenv (a tool to create isolated Python environments) to manage the python environment. You could use following commands to create an environment venv and activate it.

$ python3.8 -m venv venv
$ source venv/bin/activate

Install TAPEX

The main requirements of our code base is fairseq, which may be difficult for beginners to get started in an hour.

However, do not worry, we already wrap all necessary commands for developers. In other words, you do not need to study fairseq to start your journey about TAPEX! You can simply run the following command (in the virtual environment) to use TAPEX:

$ pip install --editable ./

The argument --editable is important for your potential follow-up modification on the tapex library. The command will not only install dependencies, but also install tapex as a library, which can be imported easily.

Use TAPEX

Once tapex is successfully installed, you could go into examples to enjoy fine-tuning TAPEX models and using them on different applications!

🏰 Resource

Pre-training Corpus

Our synthetic pre-training corpus which includes nearly 5,000,000 tuples of (SQL queries, flattened tables, SQL execution results) can be downloaded from here. You can use it for research purpose, but you should be careful about the data license.

Below is an example from the pre-training corpus:

• The SQL plus flattened Table as INPUT:

select ( select number where number = 4 ) - ( select number where number = 3 ) col : number | date | name | age (at execution) | age (at offense) | race | state | method row 1 : 1 | november 2, 1984 | velma margie barfield | 52 | 45 | white | north carolina | lethal injection row 2 : 2 | february 3, 1998 | karla faye tucker | 38 | 23 | white | texas | lethal injection row 3 : 3 | march 30, 1998 | judias v. buenoano | 54 | 28 | white | florida | electrocution row 4 : 4 | february 24, 2000 | betty lou beets | 62 | 46 | white | texas | lethal injection row 5 : 5 | may 2, 2000 | christina marie riggs | 28 | 26 | white | arkansas | lethal injection row 6 : 6 | january 11, 2001 | wanda jean allen | 41 | 29 | black | oklahoma | lethal injection row 7 : 7 | may 1, 2001 | marilyn kay plantz | 40 | 27 | white | oklahoma | lethal injection row 8 : 8 | december 4, 2001 | lois nadean smith | 61 | 41 | white | oklahoma | lethal injection row 9 : 9 | may 10, 2002 | lynda lyon block | 54 | 45 | white | alabama | electrocution row 10 : 10 | october 9, 2002 | aileen carol wuornos | 46 | 33 | white | florida | lethal injection row 11 : 11 | september 14, 2005 | frances elaine newton | 40 | 21 | black | texas | lethal injection row 12 : 12 | september 23, 2010 | teresa wilson bean lewis | 41 | 33 | white | virginia | lethal injection row 13 : 13 | june 26, 2013 | kimberly lagayle mccarthy | 52 | 36 | black | texas | lethal injection row 14 : 14 | february 5, 2014 | suzanne margaret basso | 59 | 44 | white | texas | lethal injection

• The SQL Execution Result as OUTPUT:

1.0

Here we want to acknowledge the huge effort of paper On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries, which provides the rich resources of SQL templates for us to synthesize the pre-training corpus. If you are interested, please give a STAR to their repo.

Pre-trained models

The pre-trained models trained on the above pre-training corpus.

Fine-tuned Models

We provide fine-tuned model weights and their performance on different datasets below. The following Accuracy (Acc) refers to denotation accuracy computed by our script model_eval.py. Meanwhile, it is worth noting that we need truncating long tables during preprocessing with some randomness. Therefore, we also provide preprocessed datasets for reproducing our experimental results.

Given these fine-tuned model weights, you can play with them using the predict mode in examples/tableqa/run_model.py.

For example, you can use the following command and see its log:

$ python examples/tableqa/run_model.py predict --resource-dir ./tapex.large.wtq --checkpoint-name model.pt
2021-08-29 17:39:47 | INFO | __main__ | Receive question as : Greece held its last Summer Olympics in which year?
2021-08-29 17:39:47 | INFO | __main__ | The answer should be : 2004

🤗 Run TAPEX using Huggingface

First, you should run the following commands to install the latest lib developed for TAPEX.

pip install datasets
pip install transformers

Then, you could find the detailed tutorial on how to reproduce our results on benchmarks at here.

🐣 Synthesize your own pre-training data

Go to this folder and install the requirements using the provided file:

$ pip install -r requirements.txt

Then directly run the script main.py! By default, you will obtain a file with nearly 5 million examples consisting of (SQL query, Table, Answer) and the flattened fairseq input and output. More details can be found in the argument parser as below:

  --template_file TEMPLATE_FILE
                        SQL query file which provides the template for synthesizing more SQL queries
  --mode {train,dev}    train or dev for pre-training
  --dev_id_file DEV_ID_FILE
                        the dev id file to avoid potential data leakage
  --instance_number INSTANCE_NUMBER
                        the expected instance number corresponding to each template
  --max_source_length MAX_SOURCE_LENGTH
                        the maximum length for the flattened table plus input SQL query

💬 Citation

If our work is useful for you, please consider citing our paper:

@inproceedings{
    liu2022tapex,
    title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor},
    author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=O50443AsCP}
}

❓ Frequently Asked Questions

1. AttributeError: 'NoneType' object has no attribute 'bpe'

You should firstly check the version of fairseq, which should be equal or greater than 0.12.0 when you use pip list to show it.

👍 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. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

📝 License

Please note that there are TWO LICENSES for code and pre-training corpus. The code and pre-trained models are open-sourced under MIT License, while the pre-training corpus is released under CC BY-SA 4.0.

™️ Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.