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---
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language: en
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tags:
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- tapex
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license: mit
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---
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# 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).
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> 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/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex).
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### BibTeX entry and citation info
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```bibtex
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@inproceedings{
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liu2022tapex,
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title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor},
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author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou},
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booktitle={International Conference on Learning Representations},
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year={2022},
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url={https://openreview.net/forum?id=O50443AsCP}
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}
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``` |
