k-fold-structure/triprorep-pretrain

K-Fold Structure Pretrain Corpus

ELECTRA pretraining corpus: 83.6M ATLAS + PDB structures, tokenized with our backbone (aminoaseed VQ-VAE, from StructTokenBench) + full-atom (CHI VQ-VAE) tokenizers.

Format

Single LMDB. Per-record value (pickle):

{
    "seq": int64 [L],   # AA sequence tokens
    "bb":  int64 [L],   # backbone tokens
    "fa":  int64 [L],   # full-atom tokens
}

Keyed by structure ID.

Why sharded

The native data.mdb is ~654 GB, which exceeds HF's 50 GB per-file hard limit. We byte-split into 17 parts of ~40 GB each. Reassemble before opening:

hf download k-fold-structure/triprorep-pretrain --local-dir ./pretrain.lmdb
cd ./pretrain.lmdb
cat data.mdb.part_* > data.mdb && rm data.mdb.part_*
# Now ./pretrain.lmdb/ is a valid LMDB.

Quickstart

import lmdb, pickle
env = lmdb.open("./pretrain.lmdb", readonly=True, lock=False, readahead=False)
with env.begin() as txn:
    rec = pickle.loads(txn.get(b"<your-key>"))
    print(rec["seq"].shape, rec["bb"].shape, rec["fa"].shape)

Train/valid/test split

This LMDB contains all 83.6M structures pooled together, with no per-split sub-LMDBs. For ELECTRA training, point the dataloader's lmdb_dir at the reassembled directory and set train_split / val_split to your own filter logic (or symlink train.lmdb → this dir if you want to skip a validation pass).

License

MIT