GoLLeM Corpus v4 PL — SlayerLab/gollem-corpus-16b-pl

The exact pretraining corpus of the Polish base model GoLLeM v4 (250M, trained from scratch) — released before training, so the published bytes are byte-identical (sha-tied) to what the model will see. Successor of SlayerLab/gollem-corpus-2b-pl (the v2/v3 corpus), scaled ~7.5x with per-record provenance this time.

• 16.58B unique tokens (GoLLeM V32k tokenizer, measured) = ~1.96B curated + 14.62B cleaned Polish web.
• Per-record license, is_share_alike, register, source_id columns — no blanket license.
• Three partitions (Dynaword-style): default (permissive), cc-by-sa (share-alike sources), web (HPLT v3 PL, TDM basis — see License).
• PII-scrubbed (two-stage pipeline + independent gate), deduplicated (license-aware MinHash+LSH), decontaminated against our evaluation sets.

Composition

Register sizes (V32k tokens, measured this build; sha256 = train .bin per register, full set in _manifest.json):

register	V32k tokens	docs	train-bin sha256	main sources
encyclopedic	984,163,312	1,707,741	98a3da0e979c	Wikipedia PL, Wikisource (CC-BY-SA)
science	522,045,834	41,484	a5449de3307f	Biblioteka Nauki (per-record license)
news	210,859,436	355,740	cf156a47cc20	polish_news, elka, Wikinews, Global Voices
legal/gov	176,224,723	99,152	25a5fe007936	Dziennik Ustaw, gov.pl (PD; 15% register cap)
literary	60,405,659	4,228	b91f051fc579	Wolne Lektury, ELTeC-pol (PD/CC-BY-SA)
mixed	1,621,359	18,348	992d76483f8d	NKJP-1M
web	14,624,198,792	17,092,459	5045b38564b5	HPLT v3 PL, cleaned + scrubbed
total	16,579,519,115	19,319,152		train 16,495,148,611 + heldout 84,467,087

Held-out (val split, NEVER trained): 84,467,087 V32k tokens / 96,583 docs (sha256 c4b4990fef60, deterministic sha1(document_id) % 200). 18 parquet shards across 3 partitions.

Design decisions (documented in the build experiment, DS25B-EXP1):

• QA sources excluded from base: PoQuAD is a published Polish QA benchmark — its documents in pretraining data would leak evaluation (held out as eval-only). PolQA is deferred to the SFT stage (Q/A format skews base-model style).
• 1000 Novels excluded — residual-author-rights concern despite the CC-BY label (mirrors upstream Dynaword's own handling).
• Legal register capped at 15% — the dominant failure mode of open Polish corpora is legalese skew (71.8% in polish-dynaword v0.2.1); this corpus caps it by construction.
• Token counts are reported in the GoLLeM V32k tokenizer (vocab 32,000). Document counts are tokenizer-agnostic; re-tokenize with your own tokenizer as needed.

Quality benchmarks (this corpus)

All numbers from full-corpus scans of the published artifact (not samples), pipeline scripts in the build repo:

check	method	result
exact duplicates	blake2b/sha8, normalized	575 docs (0.003%) removed
near duplicates	MinHash K=32, 8 bands, word 5-grams (J≈0.6)	4.80% removed (974,277 of 20,296,635 docs; total removal incl. exact+decontam 4.82%, full-web scan; a 15k sample had estimated 0.03–0.14% — sampling underestimates long-tail web duplication, which is why we only report full scans)
eval decontamination	exact + word 8-gram fingerprints vs d3_eval, llmzszl, MultiBLiMP, eval_admission	2,632 docs (0.013%) removed
retention		95.18% kept (19,319,152 / 20,296,635; falsification threshold pre-registered at >15% removal — not met)
license-aware dedup	duplicate survivor chosen by permissiveness (CC0 > CC-BY > CC-BY-SA)	implemented; effect small here (cross-license dups pre-excluded), method contributed for general corpora
PII residual (post-gate)	independent full scan, check ⊋ scrub	0.0018% (≤351 / 19,319,152 docs; sha-tie 8/8 verified)
trainability smoke	300 docs/register schema+content audit	schema OK, 0 empty docs, scrub markers present, registers consistent

Reference downstream results (predecessor models)

The v4 model trained on this corpus will publish its own benchmarks. For calibration, models trained on this pipeline's predecessor corpus (gollem-corpus-2b-pl, same source family and cleaning approach):

• GoLLeM-110M-PL-v3 (2 epochs / ~2B V32k): PL sentiment polemo2 56.5 acc / 8tags 37.2 (bench_pl harness; +9.3/+5.7 over an independently built 45M PL model on the same harness). Cross-lingual control (lm-eval 0.4.12 zero-shot): BLiMP .546, SciQ .620, LAMBADA-EN ppl 10508 — i.e. a genuinely Polish model, near-random on English, as expected.
• Honest framing: at 110M/2B tokens these are fluency/completion models, ~random on zero-shot classification (8tags .11 vs random .125). v4 (250M / 24B seen tokens, DataDecide-style 100:1 overtraining) is the scale-up this corpus exists for.

PII Statement

Personal Data / PII Processing

All text passed a two-stage PII pipeline before inclusion:

1. Scrubbing (scrub_pii.py, Paweł, PR#28 + international-format extension): emails → [PII], phone numbers (Polish and international formats, label-anchored and self-labelling +CC) → [Telefon], PESEL/NIP/REGON/KRS and identity-document numbers (checksum-validated, label-anchored) → [PII], bank accounts / IBAN (MOD-97 validated) → [PII]. Placeholders are semantic tags, not fake values, so no synthetic high-frequency numbers poison the corpus.
2. Independent gate (second reviewer, lens strictly wider than the scrubber — check ⊋ scrub): full-corpus residual scan on the exact artifact being published (sha-tied, verify == publish). Residual after independent gate on the published artifacts: ≤351 documents with unredacted contact data out of 19,319,152 (0.0018%), all triaged as institutional/business contacts (university department listings, ministry switchboards, travel-guide venues) in edge formats (+0-trunk, legacy 8-digit); no private-person data found in any triaged sample. Verified sha-tied: all 8 manifest checksums independently recomputed and matched.

Out of scope by explicit decision (ADR): public-institution donation account numbers, court registry numbers (KRS as bare references), emergency numbers (112/997/998/999), timestamps, coordinates, ISBNs.

Names of public officials in official/government documents are left intact by design (public-interest information, consistent with the source datasets' practice).

Known limitations

• Numbers damaged at the source (truncated 8-digit forms) may survive scrubbing; measured share is included in the residual figure above.
• The scrubber is regex-based; free-form descriptions of persons are not redacted. The corpus is web/official text — downstream users training generative models should apply their own output filters.

License

This dataset is a compilation of sources with per-record license tracking (license and is_share_alike columns). It is NOT distributed under a single blanket license.

Partition	Sources	License basis
default	Wikisource, Wikinews, Wikivoyage (PD/CC0 parts), gov.pl documents, Dziennik Ustaw, Wolne Lektury (PD), NKJP-1M, PoQuAD, ELTeC-pol, Global Voices, news (per-source)	CC0 / public domain / permissive per-source — see per-record license column
cc-by-sa	Wikipedia (994M tok), Wikibooks, Wikiquote, Biblioteka Nauki (SA parts)	CC-BY-SA-4.0 — share-alike applies to derivatives of the TEXT
web	HPLT v3 PL (cleaned web crawl)	see below

HPLT web partition — legal basis statement (important): the HPLT project releases its compilation under CC0, but explicitly does not own the underlying texts ("we do not own any of the text"). We therefore do NOT claim CC0 on the web texts themselves. Our redistribution relies on the text-and-data-mining framework (Directive (EU) 2019/790, art. 4 — lawful access, opt-out respected at crawl level by HPLT); training use falls under the same TDM basis. Users redistributing or re-publishing the raw texts are responsible for their own legal review. This mirrors the approach of the Danish Dynaword project.

Attribution for curated sources is provided per-source in the table above and per-record in the provenance column.

How to use

from datasets import load_dataset

# permissive partition only
ds = load_dataset("SlayerLab/gollem-corpus-16b-pl", "default", split="train")

# full corpus (all partitions; mind per-partition licenses)
for cfg in ("default", "cc-by-sa", "web"):
    part = load_dataset("SlayerLab/gollem-corpus-16b-pl", cfg, split="train")

Columns (curated partitions default, cc-by-sa): document_id, source_id, text, register, license, is_share_alike, provenance, n_tokens_v32k. The web partition carries the same fields except n_tokens_v32k (its per-record V32k count is not materialized — web tokens were counted at corpus level; see _manifest.json for the web token total and per-shard sha). Load each partition via its own config (load_dataset("SlayerLab/gollem-corpus-16b-pl", "<default|cc-by-sa|web>")), so the schema difference never surfaces in a single load.

Provenance & reproducibility

• Build pipeline (scrub → uniform web subsample → license-aware dedup → decontam → assembly) is documented step-by-step with parameters in the build experiment log; every shard ships a sha256 in the manifest. Published bytes ARE the verified bytes (verify == publish, checked post-upload by independent re-download).
• Curated layer provenance per-source: SlayerLab/polish-dynaword.
• Predecessor corpus (v2/v3, 2B): SlayerLab/gollem-corpus-2b-pl.
• Design predecessor / scaffold: SlayerLab/slayer-pl-8x3b — same design and source selection (8-pack, ~24B-token plan); this corpus is the materialized + cleaned realization of that plan (adds full-scan near-dedup and the PII gate the scaffold did not have).
• Model line: SlayerLab/GoLLeM-110M-PL-v3 → GoLLeM v4 (250M, this corpus).

Acknowledgements

• PII scrubbing: Paweł Puzio (ppuzio, PR#28 in polish-dynaword)
• Independent PII gate + international-format fix: Wartownik (N-04, Slayer Kolektyw)
• License-aware dedup + per-license partitions: Monter (N-03, Slayer Kolektyw)
• Card integration & training: Hart (N-02, Slayer Kolektyw)

Author: Arkadiusz Słota / SlayerLab.

Podsumowanie (PL)

Dokładny korpus pretreningowy polskiego modelu bazowego GoLLeM v4 (250M, od zera) — publikowany PRZED treningiem, bajt-w-bajt ten sam zbiór (sha-tied), na którym model będzie uczony. 16.58B unikatowych tokenów V32k: ~1.96B kuratorowanych (encyklopedia, nauka, news, legal z capem 15%, literatura) + 14.62B oczyszczonego polskiego weba (HPLT v3). Licencje per-rekord w trzech partycjach (permissive / CC-BY-SA / web-TDM), PII po dwustopniowym scrubie z niezależną bramką (residual 0.0018%), dedup license-aware z pełnego skanu (retencja 95.18%), dekontaminacja względem naszych zbiorów ewaluacyjnych.