DEFault++ benchmark v1

DEFault++ benchmark v1: Hierarchical fault diagnosis of fine-tuned HuggingFace transformers Contents- encoder_merged.csv (6,042 rows × 2,121 cols): trainer-ready feature vectors for 4 encoder models × 6 GLUE tasks (BERT, DistilBERT, RoBERTa, DistilRoBERTa on MRPC, QNLI, QQP, RTE, SST-2, STS-B).- decoder_merged.csv (2,535 rows × 3,130 cols): same shape for 4 decoder models × 5 LM tasks (GPT-2, DistilGPT-2, GPT-Neo-125M, OPT-125M on Lambada, OpenWebText, PTB, WikiText).- 35 per-task source CSVs and feature dictionary for full reproducibility.- Pre-computed deduplication, NaN handling, log-transforms, and CV filtering applied; downstream layer aggregation runs at training time via FeatureProcessor. Companion code: https://github.com/SigmaJahan/DEFaultplusplus-Transformer-Debugging Built from fine-tuning runs of 11,251 encoder + 4,282 decoder configurations covering ~13 fault categories and ~44 root causes (see feature_dictionary.csv for the complete catalog). Citation: see the DOI on this page.