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facet-weights
SiXa18/facet-weights
facet-weights is a machine learning model from SiXa18. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-4.0.
Model parameters and reference data for FACET, which predicts protein backbone phi/psi torsion angles from NMR chemical shifts.
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Updated Aug 31, 2026
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279 MB
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.npz142 MB · 92%
At a glance
- License
- cc-by-4.0
- Access
- Public
- Created
- Aug 30, 2026
- Updated
- Aug 31, 2026
- SHA
- d35fe566
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Model card
From the Hugging Face model README
FACET model weights
Model parameters and reference data for FACET, 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). FACET: backbone torsion angle prediction from NMR chemical shifts (2026). Software: https://github.com/maxzinke/facet-nmr — DOI 10.5281/zenodo.22190034.
Citing BMRB is appreciated: Hoch et al., Nucleic Acids Research 51, D368 (2023), doi:10.1093/nar/gkac1050.
- License
- cc-by-4.0
- Created
- Aug 30, 2026
- Updated
- Aug 31, 2026