zachnorton03/bart-midtrain

BART Midtrain

The midtraining corpus for BART — pre-1930 mathematics, science, technology, and medicine — plus the full pipeline that built it and every training mixture it was blended into.

Built by Unbounded Labs.

Corpus documents	11,409
Corpus characters	2,543,809,124
Corpus tokens	~604M
Removed by cleaning	24% of documents (15,075 → 11,409)
Subject focus	math, science, technology, medicine
Cutoff	1930
Schema	single string column text

What midtraining is

Midtraining sits between pretraining and downstream use. Instead of continuing on the same broad corpus, we shift the data mixture toward a smaller, higher-quality set of documents while decaying the learning rate. The goal is to spend the model's final optimization steps on the text we most want it to internalize — which for us meant pre-1930s math, science, technology, and medicine.

Repository layout

corpus/                      ← the midtrain corpus. Start here.
pipeline/
  stage2/   Internet Archive harvest          15,075 docs
  stage3/   OCRoscope second-opinion filter   13,552 docs
  stage4/   boilerplate clean + log-prior     12,791 docs
  stage5/   footer strip + anachronism        11,409 docs → corpus/
mixtures/
  v1-by-docs/     SUPERSEDED — ratios computed by document count
    ratio_00actual/  ratio_12actual/  ratio_25actual/
  v2-by-tokens/   CURRENT — ratios computed by token count
    ratio_21/  ratio_45/

corpus/ is stage 5's output, promoted to the top so the deliverable isn't buried. pipeline/stage5/ keeps that stage's audit artifacts (stripped/, hits/, stats/, _banned/).

from datasets import load_dataset

ds = load_dataset("zachnorton03/bart-midtrain", data_dir="corpus", split="train")

How the corpus was built

We focused on finding high-quality math, science, technology and medicine documents from before the 1930s, and broke each major step into stages.

Stage 1 — subject extraction (external, lives in bart-dataset-v1). We extracted STEM documents by their Library of Congress subject tags from the original corpus, targeting underrepresented Science, Technology, and Medicine subjects.

Stage 2 — Internet Archive harvest. We extracted vintage documents from the Internet Archive, filtering by topic for Math, Science, Medicine, and Technology, and by published date. We then de-duplicated and applied an OCR filter of 0.85. → 15,075 documents, 1.62B tokens

Stage 3 — second opinion. To get an independent read on OCR quality, we filtered with Pleias/OCRoscope (0.85) and an alphanumeric ratio of 0.65. Because math documents carry many unusual symbols, we deliberately kept the alphanumeric ratio relaxed. This removed ~10% of documents. → 13,552 kept

Stage 4 — structural clean + log-prior. We filtered OCR artifacts and stripped boilerplate (headers, library stamps, Google Books and HathiTrust furniture, Project Gutenberg headers, hyphenation, front matter), then mirrored the main corpus with a GPT-2 token log-prior filter at p2.5–p97.5 (−11.062 to −9.928, calibrated on 10,424 sampled documents). → 12,791 kept

Stage 5 — vintage enforcement. Finally we made sure the corpus was as vintage as possible, running it through the footer filter and the tiered banned-words list — the same 463-term, four-tier anachronism filter used on bart-dataset-v3. → 11,409 kept

As we cleaned the original corpus we learned a great deal about dataset cleaning methodology, and we applied that knowledge to midtraining extensively. After all the cleaning, we removed 24% of the original midtraining data, leaving ~604M tokens of high-quality midtraining data.

Per-stage filter detail

Stage	In	Out	Kept	Chars kept
2 — IA harvest	—	15,075	—	—
3 — OCRoscope	15,075	13,552	89.9%	—
4 — clean + prior	13,552	12,791	94.4%	81.7%
5 — footer + anachronism	12,791	11,409	89.2%	84.5%

Stage 4 removals: prior_high 306, prior_low 300, ocr_artifacts 155. Stage 5 footer strip: 1,117 of 12,791 documents changed, 1,466 lines removed.

Every stage is resumable at shard granularity — a rerun skips output shards already present and continues with the remainder. Stage 4's log-prior table and stage 5's banned-term list are each built once, cached under _prior/ and _banned/, and reused.

Training mixtures

Midtraining ran in three stages of decay, following the approach HuggingFace used for SmolLM. Because midtrain documents are much shorter than pretraining documents, we measure the mixture in tokens, not documents. We wanted the midtrain data to see fewer than 3 epochs, and used that as the guide for the ratio at each stage.

mixtures/v2-by-tokens/ — current

Folder	Target	Achieved	Train tokens	Shards
ratio_21	21%	20.96%	4,124,391,390	66
ratio_45	45%	45.00%	2,074,444,767	34

The ratio is enforced by a ratio-tracking interleave, so it holds over every prefix of the stream, not just the total. Document boundaries are preserved — nothing is concatenated or split to force the ratio.

mixtures/v1-by-docs/ — superseded

Named for what they actually delivered, not what they targeted:

Folder	Target	Achieved (docs)	Shards
ratio_00actual	0%	0.1%	99
ratio_12actual	30%	11.5%	1,263
ratio_25actual	60%	25.1%	525

Kept for reproducibility. Don't train on these.

Why there are two generations

One of the biggest pitfalls on this journey came during our final model run. We assumed midtrain documents would be roughly smaller than the original corpus. We were right — but we didn't realize how drastically smaller they were. Because we had built the mixtures by document count rather than token count, the blends under-delivered midtrain content badly: a folder targeting 60% midtrain actually supplied 25%, and one targeting 30% supplied 11.5%.

Our final model was already running when we found the bug. That gave us 36 hours to rebuild the mixtures by token before the run would need them. We got them ready in time — and took away a lesson worth the scare: verify your intuition.

Learning rate schedule

We used nanochat's Warmup-Stable-Decay (WSD), beginning decay at roughly 35% of training. SmolLM, by contrast, starts decaying at around 75%.

Lineage

This corpus is the midtraining counterpart to the BART pretraining series:

bart-dataset-v1 → v2 → v3 → bart-midtrain

Stage 1 of this pipeline draws from v1 by subject tag; all mixtures blend corpus/ against v3.

Citation

The stage-1 subject extraction derives from Institutional Books 1.0:

@misc{institutionalbooks2025,
  title  = {Institutional Books 1.0: A 242B Token Dataset from Harvard Library's
            Collections, Refined for Accuracy and Usability},
  year   = {2025},
  eprint = {2506.08300},
  archivePrefix = {arXiv}
}

---

Built by Unbounded Labs.