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# Model Card for pcqm4mv1_graphormer_base

The Graphormer is a graph classification model.

# Model Details

## Model Description

The Graphormer is a graph Transformer model, pretrained on PCQM4M-LSC, and which got 1st place on the KDD CUP 2021 (quantum prediction track).


- **Developed by:** [Microsoft]
- **Model type:** [Graphormer]
- **License:** [MIT]

## Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [https://github.com/microsoft/Graphormer]
- **Paper:** [https://arxiv.org/abs/2106.05234]
- **Documentation:** [https://graphormer.readthedocs.io/en/latest/]

# Uses

## Direct Use

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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**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}
}