

Third, given that you've prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular data), you can then use [TapasTokenizer] to convert table-question pairs into input_ids, attention_mask, token_type_ids and so on. Again, based on which of the three cases you picked above, [TapasForQuestionAnswering] requires different
inputs to be fine-tuned:
| Task                           | Required inputs                                                                                                 |
|------------------------------------|---------------------------------------------------------------------------------------------------------------------|
| Conversational                     | input_ids, attention_mask, token_type_ids, labels                                                           |
|  Weak supervision for aggregation  | input_ids, attention_mask, token_type_ids, labels, numeric_values, numeric_values_scale, float_answer |
| Strong supervision for aggregation | input ids, attention mask, token type ids, labels, aggregation_labels                                     |
[TapasTokenizer] creates the labels, numeric_values and numeric_values_scale based on the answer_coordinates and answer_text columns of the TSV file. The float_answer and aggregation_labels are already in the TSV file of step 2. Here's an example: