Graph classification with Transformers: Graphormer on ogbg-molhiv with Hugging Face

AI Impact Summary

This post shows an end-to-end graph classification workflow using Graphormer in the Hugging Face Transformers ecosystem, including loading the ogbg-molhiv dataset from Open Graph Benchmark and using GraphormerDataCollator with preprocess_item. It demonstrates fine-tuning a pretrained GraphormerForGraphClassification model (clefourrier/pcqm4mv2_graphormer_base) by wiring TrainingArguments and Trainer for a binary task. The approach is computationally heavy and memory-sensitive, so teams must provision GPUs and tune batch sizes and gradient accumulation to avoid OOM errors, affecting project timelines.