Run on custom data about tapas HOT 1 CLOSED

Hello @PunitShah1988, this is the current data preparation pipeline:

• Each dataset is converted into a TSV file format for the questions and answers, and each table on it's own TSV as well. This is the format that SQA is obtained from the original authors. You can check tapas/utils/task_utils.py for reference.
• We read the TSVs and convert into an intermediate format we call Interactions, which contains the questions, answers and table information. From there we convert into tf_examples, which are the numeric features (after text is tokenized and mapped to indexes) that are fed into the model and used for training. This happens in the module tapas/utils/tf_example_utils.py

The colab we added to the repo shows how to create an Interaction, and save it directly as a tf_record. If you do that for all of your data then you can run the train job directly.

Alternatively you can try to get your data into the same format as SQA TSVs or add your own conversion code to task_utils. You may also want to think about which of the three datasets your problem is more similar to, is it conversational (SQA), does it require aggregations (WTQ, WikiSQL), do you have supervision for which cells to aggregate (WIkiSQL), etc...

Hope this helps, good luck!

from tapas.