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luke-base

studio-ousia

**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** (extractive question answering), **CoNLL-2003** (named entity recognition), **ReCoRD** (cloze-style question answering), **TACRED** (relation classification), and **Open Entity** (entity typing). 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.

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mluke-base

studio-ousia

Please check the official repository for more details and updates. This is the mLUKE base model with 12 hidden layers, 768 hidden size. The total number of parameters in this model is 585M (278M for the word embeddings and encoder, 307M for the entity embeddings). The model was initialized with the weights of XLM-RoBERTa(base) and trained using December 2020 version of Wikipedia in 24 languages. When you load the model from `AutoModel.from_pretrained` with the default configuration, you will see the following warning:

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