    question answering.
There are three head models for the former use case:
[LukeForEntityClassification], for tasks to classify a single entity in an input text such as
    entity typing, e.g. the Open Entity dataset.
    This model places a linear head on top of the output entity representation.
[LukeForEntityPairClassification], for tasks to classify the relationship between two entities
    such as relation classification, e.g. the TACRED dataset. This
    model places a linear head on top of the concatenated output representation of the pair of given entities.
[LukeForEntitySpanClassification], for tasks to classify the sequence of entity spans, such as
    named entity recognition (NER). This model places a linear head on top of the output entity representations. You
    can address NER using this model by inputting all possible entity spans in the text to the model.
