Add retrained PolyEdit checkpoint and audit artifacts

README.md

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+---
+library_name: pytorch
+license: apache-2.0
+tags:
+- polymers
+- molecular-optimization
+- transformer
+- polyedit
+---
+
+# PolyEdit Molecular Optimization Transformer (polymer-retrained)
+
+This checkpoint adapts MolecularAI's released Molecular Optimization Transformer to
+two-anchor PSMILES and the eight PolyEdit DFT properties. It is not directly comparable
+to the authors' original LogD/Solubility/Clint model without this adaptation.
+
+The original transformer body is retained: 6 encoder/decoder layers, hidden size 256,
+8 heads, and feed-forward size 2,048. All 258 shape-compatible upstream tensors were
+loaded. Four vocabulary-dependent embedding/generator tensors were initialized for a
+new vocabulary in which padding and the `*` polymer anchor have distinct IDs. Training
+used 152,036 component-held-out examples; the best validation checkpoint was epoch 7
+of 10.
+
+On the balanced 8,176-request test set, the checkpoint obtains 99.217% RDKit+TDC
+validity, 99.168% two-anchor polymer validity, 99.083% changed outputs, 8.892% strict
+MIPS-retrained full-edit hits, and 10.127% strict DFT full-edit hits at 95.034% DFT
+coverage. Only 383 unique raw outputs were generated, so the strong validity comes with
+substantial mode collapse and must not be reported without the diversity result.
+
+Upstream code: <https://github.com/MolecularAI/deep-molecular-optimization>
+
+Upstream checkpoint: <https://doi.org/10.5281/zenodo.5571965>
+
+PolyEdit implementation and record-level evaluation: <https://github.com/promotion-kim/POLYEDIT/tree/tsyou/balanced-polymer-baseline-eval>