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The graphormer-base-pcqm4mv2 is a graph classification model developed by Microsoft. It is a Graphormer, a type of graph Transformer model, that was pretrained on the PCQM4M-LSCv2 dataset. The Graphormer is an alternative to traditional graph models and large language models, providing a practical solution for graph-related tasks. Similar models include Geneformer, a foundation transformer model pretrained on 30 million single cell transcriptomes to enable context-aware predictions in network biology tasks. Model inputs and outputs Inputs Graph data**: The model takes graph-structured data as input, such as molecular graphs or other relational data. Outputs Graph classification**: The primary output of the model is a classification of the input graph, such as predicting the property of a molecule. Capabilities The graphormer-base-pcqm4mv2 model can be used for a variety of graph classification tasks, particularly those related to molecule modeling. It can handle large graphs without running into memory issues, making it a practical solution for real-world applications. What can I use it for? The graphormer-base-pcqm4mv2 model can be used directly for graph classification tasks or fine-tuned on downstream tasks. Potential use cases include: Molecular property prediction Chemical reaction prediction Drug discovery Material design Social network analysis Knowledge graph reasoning Things to try One key aspect of the graphormer-base-pcqm4mv2 model is its ability to handle large graphs efficiently. Developers can experiment with using the model on various graph-structured datasets to see how it performs compared to traditional graph models or large language models. Additionally, fine-tuning the model on specific domains or tasks can unlock new capabilities and insights.

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Updated 5/28/2024