luke-japanese is the Japanese version of LUKE (Language Understanding with Knowledge-based Embeddings), 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 for more details and updates.

This model contains Wikipedia entity embeddings which are not used in general NLP tasks. Please use the lite version 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を使用してください。

Experimental results on JGLUE

The experimental results evaluated on the dev set of JGLUE is shown as follows:

Model	MARC-ja	JSTS	JNLI	JCommonsenseQA
	acc	Pearson/Spearman	acc	acc
LUKE Japanese large	0.965	0.932/0.902	0.927	0.893
Baselines:
Tohoku BERT large	0.955	0.913/0.872	0.900	0.816
Waseda RoBERTa large (seq128)	0.954	0.930/0.896	0.924	0.907
Waseda RoBERTa large (seq512)	0.961	0.926/0.892	0.926	0.891
XLM RoBERTa large	0.964	0.918/0.884	0.919	0.840

The baseline scores are obtained from here.

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}
}

studio-ousia/luke-japanese-large

luke-japanese-large

Experimental results on JGLUE

Citation

studio-ousia/
luke-japanese-large