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  1. EFFGATE3/gene_embeddings.npz +3 -0
  2. EFFGATE3/model.pt +3 -0
  3. README.md +41 -0
  4. banks/v0.1.0_fullbank_oldfill.npz +3 -0
  5. fold0/gene_embeddings.npz +3 -0
  6. fold0/model.pt +3 -0
  7. fold1/gene_embeddings.npz +3 -0
  8. fold1/model.pt +3 -0
  9. fold10/gene_embeddings.npz +3 -0
  10. fold10/model.pt +3 -0
  11. fold11/gene_embeddings.npz +3 -0
  12. fold11/model.pt +3 -0
  13. fold2/gene_embeddings.npz +3 -0
  14. fold2/model.pt +3 -0
  15. fold3/gene_embeddings.npz +3 -0
  16. fold3/model.pt +3 -0
  17. fold4/gene_embeddings.npz +3 -0
  18. fold4/model.pt +3 -0
  19. fold5/gene_embeddings.npz +3 -0
  20. fold5/model.pt +3 -0
  21. fold6/gene_embeddings.npz +3 -0
  22. fold6/model.pt +3 -0
  23. fold7/gene_embeddings.npz +3 -0
  24. fold7/model.pt +3 -0
  25. fold8/gene_embeddings.npz +3 -0
  26. fold8/model.pt +3 -0
  27. fold9/gene_embeddings.npz +3 -0
  28. fold9/model.pt +3 -0
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+ ---
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+ license: mit
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+ library_name: expert-model
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+ pipeline_tag: other
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+ tags:
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+ - single-cell
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+ - perturb-seq
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+ - scRNA-seq
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+ - perturbation-prediction
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+ - biology
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+ ---
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+
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+ # ExPert paper checkpoints
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+
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+ The additional checkpoints behind the ExPert paper's benchmark and ablation figures. The production model that the
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+ paper's application figures use, and that you should use for your own data, is
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+ [GoyalLab/ExPert](https://huggingface.co/GoyalLab/ExPert).
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+
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+ Code and the scripts that use these checkpoints: <https://github.com/GoyalLab/ExPert> (`paper/`)
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+
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+ | folder | what | used in |
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+ |---|---|---|
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+ | `fold0` … `fold11` | the 12 cross-validation models: each holds out one context (cell line / donor / condition) from training | benchmark and out-of-distribution figures |
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+ | `EFFGATE3` | the ablation arm with the gated prior pull | prior-pull ablation figures |
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+ | `banks/v0.1.0_fullbank_oldfill.npz` | v0.1.0 prototypes with the earlier fill for genes never perturbed in training (trained rows identical to the model's) | ALD and PMD readouts |
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+
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+ Each checkpoint folder holds `model.pt` and `gene_embeddings.npz` and loads like the production model:
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+
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+ ```python
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+ from huggingface_hub import snapshot_download
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+ from expert import ExPert
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+
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+ path = snapshot_download("GoyalLab/ExPert-paper-models", revision="v0.1.0", allow_patterns="fold4/*")
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+ model = ExPert.load(f"{path}/fold4")
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+ ```
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+
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+ The bank is a NumPy archive: `P` (perturbations × latent dims), `labels`, `trained`.
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+
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+ ## Citation
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+
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+ _Preprint reference to be added._
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