pretty_name: FoldingCorpus
license: mit
configs:
- config_name: default
default: true
data_files:
- split: train
path: corpus/train.jsonl
- split: validation
path: corpus/dev.jsonl
- split: test
path: corpus/frozen_test.jsonl
- config_name: protein_training
data_files:
- split: train
path: protein_training/train.jsonl
- split: validation
path: protein_training/dev.jsonl
- split: test
path: protein_training/test.jsonl
FoldingCorpus
Protein-derived supervised question-answer records for Fold2Reason. The default
configuration contains 12,000 train, 1,200 validation, and 1,200 frozen-test
question records from 1,000 / 100 / 100 proteins. The protein_training
configuration contains the associated BB4Q10 input/target records (1,000 / 100 / 100).
Audit and manifest JSON files are retained under corpus/ but excluded from the
load_dataset split definitions.
from datasets import load_dataset
corpus = load_dataset("GENTEL-Lab/FoldingCorpus")
proteins = load_dataset("GENTEL-Lab/FoldingCorpus", "protein_training")
Paper: https://huggingface.co/papers/2609.38879
Code and reproduction instructions: https://github.com/GENTEL-lab/Fold2Reason
Source: project dataset.zip, SHA-256 97ef38130fec0b7caaa323022d5b7bef620e7b26dfa42a1da4713066806dd90b. The original release files
are unchanged. Exact per-file hashes and source paths are in SOURCE_PROVENANCE.json.
The protein records derive from a selected OpenFold high-confidence distillation
snapshot. The project owner confirmed redistribution for this public release;
the original acquisition record is documented as pending in the project release checklist.