
LUKE
Overview
The LUKE model was proposed in LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda and Yuji Matsumoto.
It is based on RoBERTa and adds entity embeddings as well as an entity-aware self-attention mechanism, which helps
improve performance on various downstream tasks involving reasoning about entities such as named entity recognition,
extractive and cloze-style question answering, entity typing, and relation classification.
The abstract from the paper is the following:
Entity representations are useful in natural language tasks involving entities. In this paper, we propose new
pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed
model treats words and entities in a given text as independent tokens, and outputs contextualized representations of
them. Our model is trained using a new pretraining task based on the masked language model of BERT. The task involves
predicting randomly masked words and entities in a large entity-annotated corpus retrieved from Wikipedia. We also
propose an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the
transformer, and considers the types of tokens (words or entities) when computing attention scores. The proposed model
achieves impressive empirical performance on a wide range of entity-related tasks. In particular, it obtains
state-of-the-art results on five well-known datasets: Open Entity (entity typing), TACRED (relation classification),
CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), and SQuAD 1.1 (extractive question
answering).
This model was contributed by ikuyamada and nielsr. The original code can be found here.
Usage tips

This implementation is the same as [RobertaModel] with the addition of entity embeddings as well
  as an entity-aware self-attention mechanism, which improves performance on tasks involving reasoning about entities.
LUKE treats entities as input tokens; therefore, it takes entity_ids, entity_attention_mask,
  entity_token_type_ids and entity_position_ids as extra input. You can obtain those using
  [LukeTokenizer].

[LukeTokenizer] takes entities and entity_spans (character-based start and end
  positions of the entities in the input text) as extra input. entities typically consist of [MASK] entities or
  Wikipedia entities. The brief description when inputting these entities are as follows:

Inputting [MASK] entities to compute entity representations: The [MASK] entity is used to mask entities to be
    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
    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.

[LukeTokenizer] has a task argument, which enables you to easily create an input to these
  head models by specifying task="entity_classification", task="entity_pair_classification", or
  task="entity_span_classification". Please refer to the example code of each head models.
Usage example:
thon

from transformers import LukeTokenizer, LukeModel, LukeForEntityPairClassification
model = LukeModel.from_pretrained("studio-ousia/luke-base")
tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-base")

Example 1: Computing the contextualized entity representation corresponding to the entity mention "Beyoncé"

text = "Beyoncé lives in Los Angeles."
entity_spans = [(0, 7)]  # character-based entity span corresponding to "Beyoncé"
inputs = tokenizer(text, 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 2: Inputting Wikipedia entities to obtain enriched contextualized representations

entities = [
     "Beyoncé",
     "Los Angeles",
 ]  # Wikipedia entity titles corresponding to the entity mentions "Beyoncé" and "Los Angeles"
entity_spans = [(0, 7), (17, 28)]  # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
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())
print("Predicted class:", model.config.id2label[predicted_class_idx])

Resources

A demo notebook on how to fine-tune [LukeForEntityPairClassification] for relation classification
Notebooks showcasing how you to reproduce the results as reported in the paper with the HuggingFace implementation of LUKE
Text classification task guide
Token classification task guide
Question answering task guide
Masked language modeling task guide
Multiple choice task guide

LukeConfig
[[autodoc]] LukeConfig
LukeTokenizer
[[autodoc]] LukeTokenizer
    - call
    - save_vocabulary
LukeModel
[[autodoc]] LukeModel
    - forward
LukeForMaskedLM
[[autodoc]] LukeForMaskedLM
    - forward
LukeForEntityClassification
[[autodoc]] LukeForEntityClassification
    - forward
LukeForEntityPairClassification
[[autodoc]] LukeForEntityPairClassification
    - forward
LukeForEntitySpanClassification
[[autodoc]] LukeForEntitySpanClassification
    - forward
LukeForSequenceClassification
[[autodoc]] LukeForSequenceClassification
    - forward
LukeForMultipleChoice
[[autodoc]] LukeForMultipleChoice
    - forward
LukeForTokenClassification
[[autodoc]] LukeForTokenClassification
    - forward
LukeForQuestionAnswering
[[autodoc]] LukeForQuestionAnswering
    - forward