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Model description

We propose a task that aims to enable data-efficient and fast updates to KG embeddings without damaging the performance of the rest. We provide four experimental edit object models of the PT-KGE in the paper experiments used.

How to use

Here is how to use this model:

>>> from transformers import BertForMaskedLM
>>> model = BertForMaskedLM.from_pretrained(pretrained_model_name_or_path="zjunlp/KGEditor", subfolder="E-FB15k237")

BibTeX entry and citation info

@article{DBLP:journals/corr/abs-2301-10405,
  author    = {Siyuan Cheng and
               Ningyu Zhang and
               Bozhong Tian and
               Zelin Dai and
               Feiyu Xiong and
               Wei Guo and
               Huajun Chen},
  title     = {Editing Language Model-based Knowledge Graph Embeddings},
  journal   = {CoRR},
  volume    = {abs/2301.10405},
  year      = {2023},
  url       = {https://doi.org/10.48550/arXiv.2301.10405},
  doi       = {10.48550/arXiv.2301.10405},
  eprinttype = {arXiv},
  eprint    = {2301.10405},
  timestamp = {Thu, 26 Jan 2023 17:49:16 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2301-10405.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
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Dataset used to train zjunlp/KGEditor

Space using zjunlp/KGEditor 1