readme: ---
  language: en
  tags:
  - tapex
  - table-question-answering
  datasets:
  - wikitablequestions
  ---
  
  # OmniTab
  
  OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab).
  
  ## Description
  
  `neulab/omnitab-large-finetuned-wtq` (based on BART architecture) is initialized with `neulab/omnitab-large` and fine-tuned on [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions).
  
  ## Usage
  
  ```python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  import pandas as pd
  
  tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-finetuned-wtq")
  model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-finetuned-wtq")
  
  data = {
      "year": [1896, 1900, 1904, 2004, 2008, 2012],
      "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
  }
  table = pd.DataFrame.from_dict(data)
  
  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']
  ```
  
  ## Reference
  
  ```bibtex
  @inproceedings{jiang-etal-2022-omnitab,
    title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering",
    author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
  }
  ```
  