Model overview
**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.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。