data/document_structures/contacts_v1/README.md

contacts-v1 protein-document dataset

Training documents in the contacts-v1 format (single-chain protein → sequence section + side-chain contact statements), generated from timodonnell/afdb-24M by calling MarinFold's contacts_v1 generator (no re-implementation). See the format spec: marinfold/.../document_structures/contacts_v1.

Produced by experiment exp53 (marinfold @ dd025aab, which includes the min_seq_separation=6 contact filter).

Structure (rounds)

Up to 5 examples per structural cluster, organised into pLDDT rounds: round = 0 is the highest-pLDDT representative of each struct_cluster_id, round = 1 the next, etc. Structural clusters with fewer than 3 usable members are dropped (so round-0 == round-1 == round-2 in size). Shards are laid out round-descending (round-4 first … round-0 last) so a sequential reader trains on the highest-quality data last. The round column is authoritative regardless of file order.

split (train/val/test) is inherited from afdb-24M and is cluster-consistent (a whole structural cluster stays in one split → no train/test leakage).

Layout

<split>/contacts_v1-{shard:05d}-of-{total:05d}.parquet   # 2000 rows/shard, round-descending
tokenizer/                                               # contacts-v1 tokenizer (2845 tokens)

Counts

4,213,203 documents over 960,054 structural clusters (0 generation drops).

split	round 0	round 1	round 2	round 3	round 4	total
train	941,028	941,028	941,028	719,519	587,079	4,129,682
val	9,558	9,558	9,558	7,316	5,964	41,954
test	9,468	9,468	9,468	7,248	5,915	41,567

~4.67 B tokens in train (4.77 B total; mean ~1,131 num_tokens/doc, max 8,192; 245 docs truncated at the budget). Mean ~200 contacts/doc.

Columns

document (token string) · structure ("contacts-v1") · entry_id · round · struct_cluster_id · seq_cluster_id · split · global_plddt · seq_len · start_index / n_term_index / c_term_index · contacts_pre_filter / contacts_passing_min_degree / contacts_emitted / contacts_excluded · truncated · highest_contact_degree / lowest_nonzero_contact_degree / lowest_included_contact_degree · num_tokens · sha1 · uniprot_accession / tax_id / organism_name.

sha1 = sha1 of document, so byte-equality with the MarinFold generator is a single column compare.