This model should be used for graph classification tasks or graph representation tasks; the most likely associated task is molecule modeling. It can either be used as such, or finetuned on downstream tasks.

Bias, Risks, and Limitations

The Graphormer model is ressource intensive for large graphs, and might lead to OOM errors.

How to Get Started with the Model

See the Graph Classification with Transformers tutorial.

Citation [optional]

BibTeX:

@article{DBLP:journals/corr/abs-2106-05234,
  author    = {Chengxuan Ying and
               Tianle Cai and
               Shengjie Luo and
               Shuxin Zheng and
               Guolin Ke and
               Di He and
               Yanming Shen and
               Tie{-}Yan Liu},
  title     = {Do Transformers Really Perform Bad for Graph Representation?},
  journal   = {CoRR},
  volume    = {abs/2106.05234},
  year      = {2021},
  url       = {https://arxiv.org/abs/2106.05234},
  eprinttype = {arXiv},
  eprint    = {2106.05234},
  timestamp = {Tue, 15 Jun 2021 16:35:15 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2106-05234.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

clefourrier/graphormer-base-pcqm4mv2

Model Card for pcqm4mv2_graphormer_base

Model Details

Model Description

Model Sources

Uses

Direct Use

Bias, Risks, and Limitations

How to Get Started with the Model

Citation [optional]

clefourrier/
graphormer-base-pcqm4mv2