inputs = tokenizer(text, entities=entities, entity_spans=entity_spans, add_prefix_space=True, return_tensors="pt")
outputs = model(**inputs)
word_last_hidden_state = outputs.last_hidden_state
entity_last_hidden_state = outputs.entity_last_hidden_state
Example 3: Classifying the relationship between two entities using LukeForEntityPairClassification head model
model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
entity_spans = [(0, 7), (17, 28)]  # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = int(logits[0].argmax())
