Initializing a model with a pre-trained base and randomly initialized classification heads from the hub can be done as shown below. Be sure to have installed the
torch-scatter dependency for your environment in case you're using PyTorch, or the tensorflow_probability
dependency in case you're using Tensorflow:
```py
from transformers import TapasConfig, TapasForQuestionAnswering
for example, the base sized model with default SQA configuration
model = TapasForQuestionAnswering.from_pretrained("google/tapas-base")
or, the base sized model with WTQ configuration
config = TapasConfig.from_pretrained("google/tapas-base-finetuned-wtq")
model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
or, the base sized model with WikiSQL configuration
config = TapasConfig("google-base-finetuned-wikisql-supervised")
