model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
===PT-TF-SPLIT===
from transformers import TapasConfig, TFTapasForQuestionAnswering
for example, the base sized model with default SQA configuration
model = TFTapasForQuestionAnswering.from_pretrained("google/tapas-base")
or, the base sized model with WTQ configuration
config = TapasConfig.from_pretrained("google/tapas-base-finetuned-wtq")
model = TFTapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
or, the base sized model with WikiSQL configuration
config = TapasConfig("google-base-finetuned-wikisql-supervised")
model = TFTapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
```
Of course, you don't necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also experiment by defining any hyperparameters you want when initializing [TapasConfig], and then create a [TapasForQuestionAnswering] based on that configuration. For example, if you have a dataset that has both conversational questions and questions that might involve aggregation, then you can do it this way. Here's an example:
