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76 lines
3.3 KiB
Markdown
76 lines
3.3 KiB
Markdown
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was published in HF papers on 2021-10-15 and contributed to Hugging Face Transformers on 2021-12-07.*
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# mLUKE
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## Overview
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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
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of the [LUKE model](https://huggingface.co/papers/2010.01057) trained on the basis of XLM-RoBERTa.
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It is based on XLM-RoBERTa and adds entity embeddings, which helps improve performance on various downstream tasks
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involving reasoning about entities such as named entity recognition, extractive question answering, relation
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classification, cloze-style knowledge completion.
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The abstract from the paper is the following:
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*Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual
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alignment information from Wikipedia entities. However, existing methods only exploit entity information in pretraining
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and do not explicitly use entities in downstream tasks. In this study, we explore the effectiveness of leveraging
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entity representations for downstream cross-lingual tasks. We train a multilingual language model with 24 languages
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with entity representations and show the model consistently outperforms word-based pretrained models in various
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cross-lingual transfer tasks. We also analyze the model and the key insight is that incorporating entity
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representations into the input allows us to extract more language-agnostic features. We also evaluate the model with a
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multilingual cloze prompt task with the mLAMA dataset. We show that entity-based prompt elicits correct factual
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knowledge more likely than using only word representations.*
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This model was contributed by [ryo0634](https://huggingface.co/ryo0634). The original code can be found [here](https://github.com/studio-ousia/luke).
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## Usage tips
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One can directly plug in the weights of mLUKE into a LUKE model, like so:
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```python
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from transformers import LukeModel
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model = LukeModel.from_pretrained("studio-ousia/mluke-base", device_map="auto")
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```
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Note that mLUKE has its own tokenizer, [`MLukeTokenizer`]. You can initialize it as follows:
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```python
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from transformers import MLukeTokenizer
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tokenizer = MLukeTokenizer.from_pretrained("studio-ousia/mluke-base")
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```
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<Tip>
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As mLUKE's architecture is equivalent to that of LUKE, one can refer to [LUKE's documentation page](luke) for all
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tips, code examples and notebooks.
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</Tip>
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## MLukeTokenizer
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[[autodoc]] MLukeTokenizer
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- __call__
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- save_vocabulary
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