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Initial 2,000-protein dataset
This is the canonical local root for the first complete router dataset: 1,000
single-dominant structured-state proteins (label 0) and 1,000 dynamic or
heterogeneous-state proteins (label 1). The fixed split is 1,400 train, 300
validation, and 300 test proteins.
Place Colab's completed ESMFold result files (<sequence_sha256>.npz) in
esmfold_results/. Then import them with:
uv run python scripts/esmfold_dataset.py import
The importer validates every NPZ against esmfold_input_manifest.csv and
writes the reusable package-native bundles into embeddings/esmfold_v1/. It
also writes embeddings/esmfold_v1/manifest.parquet, the index consumed by the
training commands. Do not train directly from esmfold_results/; it is only a
transfer/staging location and can be deleted after a successful import.
Key files:
catalog.parquetandcatalog.csv: canonical labels, sequences, metadata, and fixed split assignments.catalog_esmfold_eligible.parquetand.csv: the 1,988 rows with verified ESMFold bundles; use these for embedding-only or combined model training.esmfold_input_manifest.csv: exact sequence hashes and model input contract.embeddings/esmfold_v1/manifest.parquet: verified embedding-bundle index.embeddings/esmfold_v1/excluded.csv: the 12 sequences outside ESMFold's 1,022-residue limit.
For CPU/MPS training, pass --dataset data/initial_2000_dataset/catalog.parquet
and --bundle-manifest data/initial_2000_dataset/embeddings/esmfold_v1/manifest.parquet.
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