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--- |
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license: mit |
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tags: |
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- graphs |
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pipeline_tag: graph-ml |
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--- |
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# Model Card for pcqm4mv1_graphormer_base |
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The Graphormer is a graph classification model. |
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# Model Details |
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## Model Description |
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The Graphormer is a graph Transformer model, pretrained on PCQM4M-LSC, and which got 1st place on the KDD CUP 2021 (quantum prediction track). |
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- **Developed by:** Microsoft |
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- **Model type:** Graphormer |
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- **License:** MIT |
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## Model Sources |
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<!-- Provide the basic links for the model. --> |
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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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# Uses |
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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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# Citation [optional] |
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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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**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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``` |
