| | --- |
| | language: en |
| | tags: |
| | - tapex |
| | - table-question-answering |
| | license: mit |
| | --- |
| | |
| | # TAPEX (large-sized model) |
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| | TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraining). |
| |
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| | ## Model description |
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| | TAPEX (**Ta**ble **P**re-training via **Ex**ecution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with *table reasoning* skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries. |
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| | TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. |
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| | ## Intended Uses |
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| | ⚠️ This model checkpoint is **ONLY** used for fine-tuining on downstream tasks, and you **CANNOT** use this model for simulating neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table. The one that can neurally execute SQL queries is at [here](https://huggingface.co/microsoft/tapex-large-sql-execution). |
| | > This separation of two models for two kinds of intention is because of a known issue in BART large, and we recommend readers to see [this comment](https://github.com/huggingface/transformers/issues/15559#issuecomment-1062880564) for more details. |
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| | ### How to Fine-tuning |
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| | Please find the fine-tuning script [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/tapex). |
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|
| | ### BibTeX entry and citation info |
| |
|
| | ```bibtex |
| | @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} |
| | } |
| | ``` |
