--- license: cc-by-4.0 tags: - protein-nmr - chemical-shifts - torsion-angles --- # FACET model weights Model parameters and reference data for [FACET](https://github.com/maxzinke/facet-nmr), which predicts protein backbone phi/psi torsion angles from NMR chemical shifts. These files are **downloaded automatically on first use** — you do not need to fetch them by hand. `facet/assets.py` resolves them into `~/.facet/` and verifies each against a pinned SHA-256. They live here rather than in the Python wheel because `facet_retrieval_index.npz` is 133 MB, past PyPI's 100 MB per-file limit; a bundled wheel could not be uploaded at all. Hosting them separately also means a corrected BMRB entry can reach users without a new package release. ## Contents | File | Size | What it is | |---|---|---| | `facet_v3.pt` | 5.2 MB | Encoder weights (PyTorch) | | `facet_v3.onnx` | 5.2 MB | The same encoder, ONNX | | `facet_retrieval_index.npz` | 132.6 MB | 254K residue embeddings with phi/psi and labels | | `facet_retrieval_index.entries.json` | 2.0 MB | Per-row source identifiers | | `facet_shift_reference.npz` | 9.6 MB | Mask-safe retrieval reference (optional; absence degrades to the parametric head) | ## Provenance and licence These files are licensed **CC BY 4.0**: use them for anything, including commercially, provided you credit the project (see `CITATION.cff` in the source repository). Structural data (phi/psi, secondary structure) come from the **Protein Data Bank** and chemical-shift data from the **BMRB** — both released under **CC0 1.0**, a public-domain dedication with no conditions. Trained parameters are the work of this project. See `DATA_PROVENANCE.md` in the source repository. No deposition from the 745-entry benchmark test set is present in the retrieval index or the shift reference (`benchmarks/check_leakage.py` in the source repository). ## Citation Zinke, M. ([ORCID 0000-0002-0541-5139](https://orcid.org/0000-0002-0541-5139)). *FACET: backbone torsion angle prediction from NMR chemical shifts* (2026). Software: — DOI [10.5281/zenodo.22190034](https://doi.org/10.5281/zenodo.22190034). Citing BMRB is appreciated: Hoch *et al.*, *Nucleic Acids Research* **51**, D368 (2023), doi:10.1093/nar/gkac1050.