TAPAS is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained with a causal language modeling (CLM) objective are better in that regard. Note that TAPAS can be used as an encoder in the EncoderDecoderModel framework, to combine it with an autoregressive text decoder such as GPT-2.
Usage: fine-tuning
Here we explain how you can fine-tune [TapasForQuestionAnswering] on your own dataset.
STEP 1: Choose one of the 3 ways in which you can use TAPAS - or experiment
Basically, there are 3 different ways in which one can fine-tune [TapasForQuestionAnswering], corresponding to the different datasets on which Tapas was fine-tuned:
SQA: if you're interested in asking follow-up questions related to a table, in a conversational set-up. For example if you first ask "what's the name of the first actor?" then you can ask a follow-up question such as "how old is he?". Here, questions do not involve any aggregation (all questions are cell selection questions).
