metadata
license: mit
tags:
- protein
- structure
- pretrain
- tokens
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