question: string
table_file: string, name of a csv file containing the tabular data
answer_coordinates: list of one or more tuples (each tuple being a cell coordinate, i.e. row, column pair that is part of the answer)
answer_text: list of one or more strings (each string being a cell value that is part of the answer)
aggregation_label: index of the aggregation operator. Only required in case of strong supervision for aggregation (the WikiSQL-supervised case)
float_answer: the float answer to the question, if there is one (np.nan if there isn't). Only required in case of weak supervision for aggregation (such as WTQ and WikiSQL)
The tables themselves should be present in a folder, each table being a separate csv file. Note that the authors of the TAPAS algorithm used conversion scripts with some automated logic to convert the other datasets (WTQ, WikiSQL) into the SQA format. The author explains this here. A conversion of this script that works with HuggingFace's implementation can be found here. Interestingly, these conversion scripts are not perfect (the answer_coordinates and float_answer fields are populated based on the answer_text), meaning that WTQ and WikiSQL results could actually be improved.
