dlab-spp/corpus-1T-manifest

SPP Corpus 1T Manifest

The selection manifest for the ~1.0T-token pretraining corpus used in Synthetic Persona Pretraining (SPP): Alignment from Token Zero.

The corpus is a seeded subsample of allenai/dolma3_mix-6T. Rather than redistribute ~2.6 TB of text that is already public, this dataset publishes the selection decisions keyed by upstream document id, so the corpus can be reconstructed exactly by replaying against upstream.

📄 Reflections + text for the annotated half: dlab-spp/reflection-50m

🧾 Safety classifier outputs: dlab-spp/safety-classifications

Configs

config	rows	what
annotated	102,772,028	rows selected for reflection annotation (safety_score >= 3 plus a matched sample)
unannotated	925,065,551	the rest of the 1T corpus
text_rescue	27,904,353	text for rows with no upstream id (see below)

Columns in annotated / unannotated: id, safety_score, has_annotation, is_bad. Total 1,027,837,579 rows = 1.000T tokens.

Reconstruction

1. Fetch text from allenai/dolma3_mix-6T at revision 689a3ea2d8217e64d73a5058913fa43ad15e81aa (pin this — the shard manifest is a seeded shuffle over the shard count, so it changes if upstream does).
2. Join by id.
3. Splice in text_rescue rows at their recorded global_row positions.
4. Keep the row order exactly as published — the Megatron .bin layout, and therefore batch composition, depends on it.

file_boundaries.json maps each of the original 40,000 subsample part files to [global_start_row, n_rows], so the original partitioning is reconstructible without publishing 40,000 files. subsample_metadata.json carries the seeded subsample parameters verbatim (seed=42, annotation_threshold=3, scale=0.871747139193521).

Full procedure, including tokenization and the reflection insertion, is in REPRODUCTION.md in the code release.

Why text_rescue exists

stack_edu-Python is the only 1 of the 163 subsets in allenai/dolma3_mix-6T that was never normalized to the Dolma schema. Its 301 shards ship raw Stack-Edu fields flattened at top level — blob_id, repo_name, path, score, … — with no id field at all, unlike its 14 sibling language subsets. Rows drawn from it therefore have a null id and cannot be joined back to upstream.

split	rows without id	share
annotated	1,629,168	1.585%
unannotated	26,275,185	2.840%
total	27,904,353	2.715%

Rather than leave a 2.7% hole, the text_rescue config publishes those rows' text directly, keyed by global_row. Merge them at their recorded positions and reconstruction is complete.

(Upstream does carry usable identifiers for these rows — blob_id, or repo_name + path. They are simply not named id and were not among the columns downloaded.)

License and attribution

Released under the Open Data Commons Attribution License (ODC-BY 1.0), inherited from the upstream source.

Contains information from allenai/dolma3_mix-6T, made available under the Open Data Commons Attribution License (ODC-BY 1.0).

Please cite Olmo 3 (arXiv:2512.13961) and observe AI2's Responsible Use Guidelines. Upstream frames this data as intended for research and educational use; that framing carries over here.