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README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+---
+
+# 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}
+}
+