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README.md

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+---
+language: en
+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: Deep Contextualized Entity Representations with Entity-aware Self-attention
+
+**LUKE** (**L**anguage **U**nderstanding with **K**nowledge-based
+**E**mbeddings) is a new pre-trained contextualized representation of words and
+entities based on transformer. LUKE treats words and entities in a given text as
+independent tokens, and outputs contextualized representations of them. LUKE
+adopts an entity-aware self-attention mechanism that is an extension of the
+self-attention mechanism of the transformer, and considers the types of tokens
+(words or entities) when computing attention scores.
+
+LUKE achieves state-of-the-art results on five popular NLP benchmarks including
+**[SQuAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/)** (extractive
+question answering),
+**[CoNLL-2003](https://www.clips.uantwerpen.be/conll2003/ner/)** (named entity
+recognition), **[ReCoRD](https://sheng-z.github.io/ReCoRD-explorer/)**
+(cloze-style question answering),
+**[TACRED](https://nlp.stanford.edu/projects/tacred/)** (relation
+classification), and
+**[Open Entity](https://www.cs.utexas.edu/~eunsol/html_pages/open_entity.html)**
+(entity typing).
+
+Please check the [official repository](https://github.com/studio-ousia/luke) for
+more details and updates.
+
+This is the LUKE base model with 12 hidden layers, 768 hidden size. The total number
+of parameters in this model is 253M. It is trained using December 2018 version of
+Wikipedia.
+
+### Experimental results
+
+The experimental results are provided as follows:
+
+| Task                           | Dataset                                                                      | Metric | LUKE-large        | luke-base | Previous SOTA                                                             |
+| ------------------------------ | ---------------------------------------------------------------------------- | ------ | ----------------- | --------- | ------------------------------------------------------------------------- |
+| Extractive Question Answering  | [SQuAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/)                    | EM/F1  | **90.2**/**95.4** | 86.1/92.3 | 89.9/95.1 ([Yang et al., 2019](https://arxiv.org/abs/1906.08237))         |
+| Named Entity Recognition       | [CoNLL-2003](https://www.clips.uantwerpen.be/conll2003/ner/)                 | F1     | **94.3**          | 93.3      | 93.5 ([Baevski et al., 2019](https://arxiv.org/abs/1903.07785))           |
+| Cloze-style Question Answering | [ReCoRD](https://sheng-z.github.io/ReCoRD-explorer/)                         | EM/F1  | **90.6**/**91.2** | -         | 83.1/83.7 ([Li et al., 2019](https://www.aclweb.org/anthology/D19-6011/)) |
+| Relation Classification        | [TACRED](https://nlp.stanford.edu/projects/tacred/)                          | F1     | **72.7**          | -         | 72.0 ([Wang et al. , 2020](https://arxiv.org/abs/2002.01808))             |
+| Fine-grained Entity Typing     | [Open Entity](https://www.cs.utexas.edu/~eunsol/html_pages/open_entity.html) | F1     | **78.2**          | -         | 77.6 ([Wang et al. , 2020](https://arxiv.org/abs/2002.01808))             |
+
+### Citation
+
+If you find LUKE useful for your work, please cite the following paper:
+
+```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}
+}
+```