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*This model was released on 2021-10-15 and added to Hugging Face Transformers on 2021-12-07.*

# mLUKE

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

The mLUKE model was proposed in [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://huggingface.co/papers/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka. It's a multilingual extension
of the [LUKE model](https://huggingface.co/papers/2010.01057) trained on the basis of XLM-RoBERTa.

It is based on XLM-RoBERTa and adds entity embeddings, which helps improve performance on various downstream tasks
involving reasoning about entities such as named entity recognition, extractive question answering, relation
classification, cloze-style knowledge completion.

The abstract from the paper is the following:

*Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual
alignment information from Wikipedia entities. However, existing methods only exploit entity information in pretraining
and do not explicitly use entities in downstream tasks. In this study, we explore the effectiveness of leveraging
entity representations for downstream cross-lingual tasks. We train a multilingual language model with 24 languages
with entity representations and show the model consistently outperforms word-based pretrained models in various
cross-lingual transfer tasks. We also analyze the model and the key insight is that incorporating entity
representations into the input allows us to extract more language-agnostic features. We also evaluate the model with a
multilingual cloze prompt task with the mLAMA dataset. We show that entity-based prompt elicits correct factual
knowledge more likely than using only word representations.*

This model was contributed by [ryo0634](https://huggingface.co/ryo0634). The original code can be found [here](https://github.com/studio-ousia/luke).

## Usage tips

One can directly plug in the weights of mLUKE into a LUKE model, like so:

```python
from transformers import LukeModel

model = LukeModel.from_pretrained("studio-ousia/mluke-base")
```

Note that mLUKE has its own tokenizer, [`MLukeTokenizer`]. You can initialize it as follows:

```python
from transformers import MLukeTokenizer

tokenizer = MLukeTokenizer.from_pretrained("studio-ousia/mluke-base")
```

<Tip>

As mLUKE's architecture is equivalent to that of LUKE, one can refer to [LUKE's documentation page](luke) for all
tips, code examples and notebooks.

</Tip>

## MLukeTokenizer

[[autodoc]] MLukeTokenizer
    - __call__
    - save_vocabulary
