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Xu-AI4Science/MARRI-interpretability

MARRI headline model — test-set interpretability export Per-sample interpretability/diagnostics for the MARRI headline configuration (dyn ft-rnafm, seed 42 — RNA-FM fine-tuned, RNet2D frozen, dynamic negative resampling), evaluated on the fixed held-out test split (n=16,058 pairs, seed 42). Code and training/evidence logs: https://github.com/GainGod-Xu/MARRI Files marri_ft-rnafm_dyn_bs4_seed42_test_results_interpretability.h5 — per-sample diagnostics:… See the full description on the dataset page: https://huggingface.co/datasets/Xu-AI4Science/MARRI-interpretability.

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MARRI headline model — test-set interpretability export

Per-sample interpretability/diagnostics for the MARRI headline configuration (dyn ft-rnafm, seed 42 — RNA-FM fine-tuned, RNet2D frozen, dynamic negative resampling), evaluated on the fixed held-out test split (n=16,058 pairs, seed 42).

Code and training/evidence logs: https://github.com/GainGod-Xu/MARRI

Files

—marri_ft-rnafm_dyn_bs4_seed42_test_results_interpretability.h5 — per-sample diagnostics: cross-attention maps (A→B, B→A) at every fusion layer (global and local-window scope), the hybridization-gain (H_ij) matrices, fused logits, opening-penalty terms, and RNet2D per-base structural features, all in fp16 with gzip compression, plus per-sample metadata (sequence IDs, raw sequences, local-window coordinates, gene type/genomic context).
—marri_ft-rnafm_dyn_bs4_seed42_test_results.h5 — the corresponding predictions/probabilities/targets/ROC and PR curves for the same test run (test ROC-AUC 0.7530, PR-AUC 0.7346, Accuracy 0.6830, Precision 0.6663, Recall 0.7255, F1 0.6947, MCC 0.3677).

Generated by re-running the test/export phase against the already-trained checkpoint (no retraining); metrics are identical to the ones reported for this run in the paper.