studio-ousia/luke-japanese-large-lite

luke-japanese-large-lite

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 is a lightweight version which does not contain Wikipedia entity embeddings. Please use the full version for tasks that 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、Wikipedia エンティティのエンベディングを含まない軽量版のモデルです。Wikipedia エンティティを入力として使うタスクには、full 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

@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}
}