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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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You can use the raw model for simulating neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table. However, the model is mostly meant to be fine-tuned on a supervised dataset. Currently TAPEX can be fine-tuned to tackle table question answering tasks and table fact verification tasks. See the [model hub](https://huggingface.co/models?search=tapex) to look for fine-tuned versions on a task that interests you.
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### How to Use
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Here is how to use this model in transformers:
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```python
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from transformers import TapexTokenizer, BartForConditionalGeneration
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import pandas as pd
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tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large")
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model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large")
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data = {
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"year": [1896, 1900, 1904, 2004, 2008, 2012],
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"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
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}
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table = pd.DataFrame.from_dict(data)
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# tapex accepts uncased input since it is pre-trained on the uncased corpus
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query = "select year where city = beijing"
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encoding = tokenizer(table=table, query=query, return_tensors="pt")
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outputs = model.generate(**encoding)
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print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
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# ['2008']
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```
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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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``` |
