File size: 2,971 Bytes
8895503 29167de 8895503 29167de a1e1514 29167de a1e1514 29167de 08a09af 29167de a1e1514 29167de |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
---
language: ja
thumbnail: https://github.com/studio-ousia/luke/raw/master/resources/luke_logo.png
tags:
- luke
- named entity recognition
- entity typing
- relation classification
- question answering
license: apache-2.0
---
## luke-japanese
**luke-japanese** is the Japanese version of **LUKE** (**L**anguage **U**nderstanding with **K**nowledge-based **E**mbeddings), a pre-trained _knowledge-enhanced_ contextualized representation of words and entities. LUKE treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. Please refer to our [GitHub repository](https://github.com/studio-ousia/luke) for more details and updates.
This model contains Wikipedia entity embeddings which are not used in general NLP tasks. Please use the [lite version](https://huggingface.co/studio-ousia/luke-japanese-base-lite/) for tasks that do not use Wikipedia entities as inputs.
**luke-japanese**は、単語とエンティティの知識拡張型訓練済みTransformerモデル**LUKE**の日本語版です。LUKEは単語とエンティティを独立したトークンとして扱い、これらの文脈を考慮した表現を出力します。詳細については、[GitHub リポジトリ](https://github.com/studio-ousia/luke)を参照してください。
このモデルは、通常のNLPタスクでは使われないWikipediaエンティティのエンベディングを含んでいます。単語の入力のみを使うタスクには、[lite version](https://huggingface.co/studio-ousia/luke-japanese-base-lite/)を使用してください。
### Experimental results on JGLUE
The experimental results evaluated on the dev set of
[JGLUE](https://github.com/yahoojapan/JGLUE) is shown as follows:
| Model | MARC-ja | JSTS | JNLI | JCommonsenseQA |
| ---------------------- | --------- | ------------------- | --------- | -------------- |
| | acc | Pearson/Spearman | acc | acc |
| **LUKE Japanese base** | **0.963** | **0.912**/**0.875** | **0.912** | **0.842** |
| _Baselines:_ | |
| Tohoku BERT base | 0.958 | 0.899/0.859 | 0.899 | 0.808 |
| NICT BERT base | 0.958 | 0.903/0.867 | 0.902 | 0.823 |
| Waseda RoBERTa base | 0.962 | 0.901/0.865 | 0.895 | 0.840 |
| XLM RoBERTa base | 0.961 | 0.870/0.825 | 0.893 | 0.687 |
The baseline scores are obtained from [here](https://github.com/yahoojapan/JGLUE/tree/9f650417195ec54a080411f44e2395012979d42e#baseline-scores).
### Citation
```latex
@inproceedings{yamada2020luke,
title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention},
author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto},
booktitle={EMNLP},
year={2020}
}
```
|