PyTorch-native NeMo AutoModel handles transformer pretraining in
@nvidia's end-to-end workflow for building a transaction foundation model.
The workflow combines GPU-accelerated data processing and tokenization, decoder-only model pretraining, embedding extraction, and XGBoost fraud classification.
On the synthetic
@IBM TabFormer dataset, combining raw features with learned embeddings increased Average Precision by 41.76% over the raw-feature baseline.
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