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OmniTab is a table-based QA model proposed in OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering. The original Github repository is https://github.com/jzbjyb/OmniTab.


neulab/omnitab-large-128shot-finetuned-wtq-128shot (based on BART architecture) is initialized with neulab/omnitab-large-128shot and fine-tuned on WikiTableQuestions in the 128-shot setting.


from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import pandas as pd

tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-128shot-finetuned-wtq-128shot")
model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-128shot-finetuned-wtq-128shot")

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']


  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",
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Table Question Answering
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Dataset used to train neulab/omnitab-large-128shot-finetuned-wtq-128shot