| --- |
| 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. |
|
|
| ```python |
| 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. |
|
|
