 
 TAPAS architecture. Taken from the original blog post.
This model was contributed by nielsr. The Tensorflow version of this model was contributed by kamalkraj. The original code can be found here.
Tips:
TAPAS is a model that uses relative position embeddings by default (restarting the position embeddings at every cell of the table). Note that this is something that was added after the publication of the original TAPAS paper. According to the authors, this usually results in a slightly better performance, and allows you to encode longer sequences without running out of embeddings. This is reflected in the reset_position_index_per_cell parameter of [TapasConfig], which is set to True by default. The default versions of the models available on the hub all use relative position embeddings. You can still use the ones with absolute position embeddings by passing in an additional argument revision="no_reset" when calling the from_pretrained() method. Note that it's usually advised to pad the inputs on the right rather than the left.
