readme: ---
  language: en
  tags:
  - tapex
  - table-question-answering
  license: mit
  ---
  
  # TAPEX (large-sized model) 
  
  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).
  
  ## Model description
  
  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.
  
  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.
  
  ## Intended Uses
  
  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.
  
  ### How to Use
  
  Here is how to use this model in transformers:
  
  ```python
  from transformers import TapexTokenizer, BartForConditionalGeneration
  import pandas as pd
  
  tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-sql-execution")
  model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-sql-execution")
  
  data = {
      "year": [1896, 1900, 1904, 2004, 2008, 2012],
      "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
  }
  table = pd.DataFrame.from_dict(data)
  
  # tapex accepts uncased input since it is pre-trained on the uncased corpus
  query = "select year where city = beijing"
  encoding = tokenizer(table=table, query=query, return_tensors="pt")
  
  outputs = model.generate(**encoding)
  
  print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
  # ['2008']
  ```
  
  ### How to Fine-tuning
  
  ⚠️ This model checkpoint is **ONLY** used for simulating neural SQL execution (i.e., employ TAPEX to execute a SQL query on a given table), and you **CANNOT** use this model for fine-tuning on downstream tasks. The one that can be used for fine-tuning is at [here](https://huggingface.co/microsoft/tapex-large).
  
  > 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.
  
  ### 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}
  }
  ```