readme: ---
  language: en
  tags:
  - tapex
  - table-question-answering
  datasets:
  - wikitablequestions
  license: mit
  ---
  
  # TAPEX (base-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.
  
  This model is the `tapex-base` model fine-tuned on the [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) dataset.
  
  ## Intended Uses
  
  You can use the model for table question answering on *complex* questions. Some **solveable** questions are shown below (corresponding tables now shown):
  
  | Question | Answer |
  |:---: |:---:|
  | according to the table, what is the last title that spicy horse produced? | Akaneiro: Demon Hunters |
  | what is the difference in runners-up from coleraine academical institution and royal school dungannon? | 20 |
  | what were the first and last movies greenstreet acted in? | The Maltese Falcon, Malaya |
  | in which olympic games did arasay thondike not finish in the top 20? | 2012 |
  | which broadcaster hosted 3 titles but they had only 1 episode? | Channel 4 |
  
  
  ### 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-base-finetuned-wtq")
  model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-base-finetuned-wtq")
  
  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 = "In which year did beijing host the Olympic Games?"
  encoding = tokenizer(table=table, query=query, return_tensors="pt")
  
  outputs = model.generate(**encoding)
  
  print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
  # [' 2008.0']
  ```
  
  ### How to Eval
  
  Please find the eval script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex).
  
  ### 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}
  }
  ```