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transformers/docs/source/en/model_doc/mluke.md

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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