studio-ousia/luke-japanese-base-lite

luke-japanese

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 are shown as follows:

Model	MARC-ja	JSTS	JNLI	JCommonsenseQA
	acc	Pearson/Spearman	acc	acc
LUKE Japanese base	0.965	0.916/0.877	0.912	0.842
Baselines:
Tohoku BERT base	0.958	0.909/0.868	0.899	0.808
NICT BERT base	0.958	0.910/0.871	0.902	0.823
Waseda RoBERTa base	0.962	0.913/0.873	0.895	0.840
XLM RoBERTa base	0.961	0.877/0.831	0.893	0.687

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}
}