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  - graphs
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  ---
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  The Graphormer is a graph classification model.
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- The checkpoint here has been pretrained on PCQM4Mv2.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - graphs
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  ---
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+ # Model Card for pcqm4mv1_graphormer_base
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+
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  The Graphormer is a graph classification model.
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+
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+ # Model Details
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+
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+ ## Model Description
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+ The Graphormer is a graph Transformer model, pretrained on PCQM4M-LSCv2.
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+ - **Developed by:** Microsoft
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+ - **Model type:** Graphormer
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+ - **License:** MIT
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+
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+ ## Model Sources
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [Github](https://github.com/microsoft/Graphormer)
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+ - **Paper:** [Paper](https://arxiv.org/abs/2106.05234)
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+ - **Documentation:** [Link](https://graphormer.readthedocs.io/en/latest/)
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+
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+ # Uses
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+
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+ ## Direct Use
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+ 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.
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+ # Bias, Risks, and Limitations
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+ The Graphormer model is ressource intensive for large graphs, and might lead to OOM errors.
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+ ## How to Get Started with the Model
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+ See the Graph Classification with Transformers tutorial.
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+
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+ # Citation [optional]
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+
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+
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+ **BibTeX:**
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+ ```
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+ @article{DBLP:journals/corr/abs-2106-05234,
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+ author = {Chengxuan Ying and
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+ Tianle Cai and
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+ Shengjie Luo and
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+ Shuxin Zheng and
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+ Guolin Ke and
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+ Di He and
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+ Yanming Shen and
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+ Tie{-}Yan Liu},
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+ title = {Do Transformers Really Perform Bad for Graph Representation?},
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+ journal = {CoRR},
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+ volume = {abs/2106.05234},
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+ year = {2021},
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+ url = {https://arxiv.org/abs/2106.05234},
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+ eprinttype = {arXiv},
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+ eprint = {2106.05234},
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+ timestamp = {Tue, 15 Jun 2021 16:35:15 +0200},
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+ biburl = {https://dblp.org/rec/journals/corr/abs-2106-05234.bib},
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+ bibsource = {dblp computer science bibliography, https://dblp.org}
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+ }
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+ ```