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---
license: mit
tags:
- graphs
pipeline_tag: graph-ml
---
# Model Card for pcqm4mv2_graphormer_base
The Graphormer is a graph classification model.
# Model Details
## Model Description
The Graphormer is a graph Transformer model, pretrained on PCQM4M-LSCv2.
- **Developed by:** Microsoft
- **Model type:** Graphormer
- **License:** MIT
## Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [Github](https://github.com/microsoft/Graphormer)
- **Paper:** [Paper](https://arxiv.org/abs/2106.05234)
- **Documentation:** [Link](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}
}
``` |