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}
}
Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Providers NEW
This model is not currently available via any of the supported Inference Providers.
The model cannot be deployed to the HF Inference API: The model has no library tag.

Dataset used to train zjunlp/KGEditor

Space using zjunlp/KGEditor 1