    predicted during pretraining. When LUKE receives the [MASK] entity, it tries to predict the original entity by
    gathering the information about the entity from the input text. Therefore, the [MASK] entity can be used to address
    downstream tasks requiring the information of entities in text such as entity typing, relation classification, and
    named entity recognition.
Inputting Wikipedia entities to compute knowledge-enhanced token representations: LUKE learns rich information
    (or knowledge) about Wikipedia entities during pretraining and stores the information in its entity embedding. By
    using Wikipedia entities as input tokens, LUKE outputs token representations enriched by the information stored in
    the embeddings of these entities. This is particularly effective for tasks requiring real-world knowledge, such as
