index) and batch encode each table with its questions. This will make sure that the prev_labels token types (see docs of [TapasTokenizer]) are set correctly. See this notebook for more info. See this notebook for more info regarding using the TensorFlow model.
STEP 4: Train (fine-tune) TapasForQuestionAnswering/TFTapasForQuestionAnswering
You can then fine-tune [TapasForQuestionAnswering] or [TFTapasForQuestionAnswering] as follows (shown here for the weak supervision for aggregation case):
```py
from transformers import TapasConfig, TapasForQuestionAnswering, AdamW
this is the default WTQ configuration
config = TapasConfig(
...     num_aggregation_labels=4,
...     use_answer_as_supervision=True,
...     answer_loss_cutoff=0.664694,
...     cell_selection_preference=0.207951,
...     huber_loss_delta=0.121194,
