使用 Transformer 進行圖形分類 (Graph Classification with Transformers)
Original: Graph Classification with Transformers
This technical blog post from Hugging Face explores in depth how to apply the Transformer architecture — traditionally used in natural…
Hugging Face 介紹了如何利用 Transformer 架構進行圖形分類（Graph Classification）。文章以微軟開發的 Graphormer 模型為例，展示如何處理非歐幾里得空間的圖形數據，並將其應用於預測分子特性等實際場景。讀者將學習如何利用 Hugging Face transformers 庫載入圖形數據集、進行特徵編碼並訓練圖形 Transformer 模型。
This technical blog post from Hugging Face explores in depth how to apply the Transformer architecture — traditionally used in natural language processing (NLP) — to the task of "graph classification." Graph classification aims to predict properties of an entire graph structure, for example predicting whether a chemical molecule (represented as a graph where nodes are atoms and edges are chemical bonds) is toxic or has a particular biological activity.
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