Fix YAML: valid front-matter (configs register, drop invalid license_link anchor)
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README.md
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--
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```
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`
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**Author:** Arkadiusz Słota / SlayerLab.
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## Podsumowanie (PL)
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Dokładny korpus pretreningowy polskiego modelu bazowego **GoLLeM v4 (250M, od zera)** —
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publikowany PRZED treningiem, bajt-w-bajt ten sam zbiór (sha-tied), na którym model będzie
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uczony. 16.58B unikatowych tokenów V32k: ~1.96B kuratorowanych (encyklopedia, nauka, news,
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legal z capem 15%, literatura) + 14.62B oczyszczonego polskiego weba (HPLT v3). Licencje
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per-rekord w trzech partycjach (permissive / CC-BY-SA / web-TDM), PII po dwustopniowym
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scrubie z niezależną bramką (residual 0.0018%), dedup license-aware z pełnego skanu (retencja 95.18%),
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dekontaminacja względem naszych zbiorów ewaluacyjnych.
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---
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license: other
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license_name: per-source
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language:
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- pl
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task_categories:
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- text-generation
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pretty_name: GoLLeM Corpus v4 PL (16B tokens)
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size_categories:
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- 10M<n<100M
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configs:
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- config_name: default
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data_files: "data/default/*.parquet"
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- config_name: cc-by-sa
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data_files: "data/cc-by-sa/*.parquet"
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- config_name: web
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data_files: "data/web/*.parquet"
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tags:
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- pretraining
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- polish
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- gollem
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- from-scratch
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- pii-scrubbed
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- deduplicated
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---
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# GoLLeM Corpus v4 PL — SlayerLab/gollem-corpus-16b-pl
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**The exact pretraining corpus of the Polish base model GoLLeM v4 (250M, trained from
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scratch)** — released *before* training, so the published bytes are byte-identical
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(sha-tied) to what the model will see. Successor of
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[`SlayerLab/gollem-corpus-2b-pl`](https://huggingface.co/datasets/SlayerLab/gollem-corpus-2b-pl)
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(the v2/v3 corpus), scaled ~7.5x with per-record provenance this time.
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- **16.58B unique tokens** (GoLLeM V32k tokenizer, measured) =
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~1.96B curated + 14.62B cleaned Polish web.
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- **Per-record `license`, `is_share_alike`, `register`, `source_id` columns** — no blanket license.
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- **Three partitions** (Dynaword-style): `default` (permissive), `cc-by-sa` (share-alike
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sources), `web` (HPLT v3 PL, TDM basis — see [License](#license)).
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- PII-scrubbed (two-stage pipeline + independent gate), deduplicated (license-aware
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MinHash+LSH), decontaminated against our evaluation sets.
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## Composition
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Register sizes (V32k tokens, measured this build; sha256 = train `.bin` per register, full set in
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[`_manifest.json`](./_manifest.json)):
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| register | V32k tokens | docs | train-bin sha256 | main sources |
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|---|---:|---:|---|---|
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| encyclopedic | 984,163,312 | 1,707,741 | `98a3da0e979c` | Wikipedia PL, Wikisource (CC-BY-SA) |
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| science | 522,045,834 | 41,484 | `a5449de3307f` | Biblioteka Nauki (per-record license) |
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| news | 210,859,436 | 355,740 | `cf156a47cc20` | polish_news, elka, Wikinews, Global Voices |
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| legal/gov | 176,224,723 | 99,152 | `25a5fe007936` | Dziennik Ustaw, gov.pl (PD; 15% register cap) |
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| literary | 60,405,659 | 4,228 | `b91f051fc579` | Wolne Lektury, ELTeC-pol (PD/CC-BY-SA) |
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| mixed | 1,621,359 | 18,348 | `992d76483f8d` | NKJP-1M |
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| **web** | **14,624,198,792** | 17,092,459 | `5045b38564b5` | HPLT v3 PL, cleaned + scrubbed |
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| **total** | **16,579,519,115** | 19,319,152 | | train 16,495,148,611 + heldout 84,467,087 |
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Held-out (val split, NEVER trained): 84,467,087 V32k tokens / 96,583 docs (sha256 `c4b4990fef60`,
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deterministic `sha1(document_id) % 200`). 18 parquet shards across 3 partitions.
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Design decisions (documented in the build experiment, DS25B-EXP1):
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- **QA sources excluded from base:** PoQuAD is a published Polish QA benchmark — its
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documents in pretraining data would leak evaluation (held out as eval-only). PolQA is
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deferred to the SFT stage (Q/A format skews base-model style).
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- **1000 Novels excluded** — residual-author-rights concern despite the CC-BY label
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(mirrors upstream Dynaword's own handling).
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- **Legal register capped at 15%** — the dominant failure mode of open Polish corpora is
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legalese skew (71.8% in polish-dynaword v0.2.1); this corpus caps it by construction.
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- **Token counts** are reported in the GoLLeM V32k tokenizer (vocab 32,000). Document
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counts are tokenizer-agnostic; re-tokenize with your own tokenizer as needed.
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## Quality benchmarks (this corpus)
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All numbers from **full-corpus scans of the published artifact** (not samples), pipeline scripts in the build repo:
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| check | method | result |
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|---|---|---|
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| exact duplicates | blake2b/sha8, normalized | 575 docs (0.003%) removed |
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| 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) |
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| eval decontamination | exact + word 8-gram fingerprints vs d3_eval, llmzszl, MultiBLiMP, eval_admission | 2,632 docs (0.013%) removed |
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| retention | | 95.18% kept (19,319,152 / 20,296,635; falsification threshold pre-registered at >15% removal — not met) |
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| 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 |
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| PII residual (post-gate) | independent full scan, `check ⊋ scrub` | 0.0018% (≤351 / 19,319,152 docs; sha-tie 8/8 verified) |
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| trainability smoke | 300 docs/register schema+content audit | schema OK, 0 empty docs, scrub markers present, registers consistent |
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## Reference downstream results (predecessor models)
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The v4 model trained on this corpus will publish its own benchmarks. For calibration,
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models trained on this pipeline's predecessor corpus (gollem-corpus-2b-pl, same source
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family and cleaning approach):
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- **GoLLeM-110M-PL-v3** (2 epochs / ~2B V32k): PL sentiment polemo2 56.5 acc /
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8tags 37.2 (bench_pl harness; +9.3/+5.7 over an independently built 45M PL model on the
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same harness). Cross-lingual control (lm-eval 0.4.12 zero-shot): BLiMP .546,
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SciQ .620, LAMBADA-EN ppl 10508 — i.e. a genuinely Polish model, near-random on
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English, as expected.
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- Honest framing: at 110M/2B tokens these are fluency/completion models, ~random on
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zero-shot classification (8tags .11 vs random .125). v4 (250M / 24B seen tokens,
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DataDecide-style 100:1 overtraining) is the scale-up this corpus exists for.
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## PII Statement
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<!-- === SEKCJA WARTOWNIKA (verbatim) === -->
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### Personal Data / PII Processing
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All text passed a two-stage PII pipeline before inclusion:
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1. **Scrubbing** (`scrub_pii.py`, Paweł, PR#28 + international-format extension): emails → `[PII]`,
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phone numbers (Polish and international formats, label-anchored and self-labelling `+CC`) → `[Telefon]`,
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PESEL/NIP/REGON/KRS and identity-document numbers (checksum-validated, label-anchored) → `[PII]`,
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bank accounts / IBAN (MOD-97 validated) → `[PII]`. Placeholders are semantic tags, not fake values,
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so no synthetic high-frequency numbers poison the corpus.
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2. **Independent gate** (second reviewer, lens strictly wider than the scrubber — `check ⊋ scrub`):
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full-corpus residual scan on the exact artifact being published (sha-tied, `verify == publish`).
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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.
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Out of scope by explicit decision (ADR): public-institution donation account numbers, court registry
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numbers (KRS as bare references), emergency numbers (112/997/998/999), timestamps, coordinates, ISBNs.
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Names of public officials in official/government documents are left intact by design (public-interest
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information, consistent with the source datasets' practice).
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### Known limitations
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- Numbers damaged at the source (truncated 8-digit forms) may survive scrubbing; measured share is
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included in the residual figure above.
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- The scrubber is regex-based; free-form descriptions of persons are not redacted. The corpus is
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web/official text — downstream users training generative models should apply their own output filters.
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## License
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This dataset is a compilation of sources with **per-record license tracking** (`license` and
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`is_share_alike` columns). It is NOT distributed under a single blanket license.
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| Partition | Sources | License basis |
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|---|---|---|
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| `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 |
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| `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 |
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| `web` | HPLT v3 PL (cleaned web crawl) | see below |
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**HPLT web partition — legal basis statement (important):** the HPLT project releases its *compilation*
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under CC0, but explicitly does not own the underlying texts ("we do not own any of the text"). We therefore
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do NOT claim CC0 on the web texts themselves. Our redistribution relies on the text-and-data-mining
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framework (Directive (EU) 2019/790, art. 4 — lawful access, opt-out respected at crawl level by HPLT);
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training use falls under the same TDM basis. Users redistributing or re-publishing the raw texts are
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responsible for their own legal review. This mirrors the approach of the Danish Dynaword project.
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Attribution for curated sources is provided per-source in the table above and per-record in the
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`provenance` column.
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<!-- === KONIEC SEKCJI WARTOWNIKA === -->
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## How to use
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```python
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from datasets import load_dataset
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# permissive partition only
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ds = load_dataset("SlayerLab/gollem-corpus-16b-pl", "default", split="train")
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# full corpus (all partitions; mind per-partition licenses)
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for cfg in ("default", "cc-by-sa", "web"):
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part = load_dataset("SlayerLab/gollem-corpus-16b-pl", cfg, split="train")
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```
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Columns (curated partitions `default`, `cc-by-sa`): `document_id`, `source_id`, `text`,
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`register`, `license`, `is_share_alike`, `provenance`, `n_tokens_v32k`.
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The `web` partition carries the same fields **except `n_tokens_v32k`** (its per-record V32k
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count is not materialized — web tokens were counted at corpus level; see `_manifest.json`
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for the web token total and per-shard sha). Load each partition via its own config
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(`load_dataset("SlayerLab/gollem-corpus-16b-pl", "<default|cc-by-sa|web>")`), so the schema
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difference never surfaces in a single load.
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## Provenance & reproducibility
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- Build pipeline (scrub → uniform web subsample → license-aware dedup → decontam →
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assembly) is documented step-by-step with parameters in the build experiment log;
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every shard ships a sha256 in the manifest. Published bytes ARE the verified bytes
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(`verify == publish`, checked post-upload by independent re-download).
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- Curated layer provenance per-source: [`SlayerLab/polish-dynaword`](https://huggingface.co/datasets/SlayerLab/polish-dynaword).
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- Predecessor corpus (v2/v3, 2B): [`SlayerLab/gollem-corpus-2b-pl`](https://huggingface.co/datasets/SlayerLab/gollem-corpus-2b-pl).
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- Design predecessor / scaffold: [`SlayerLab/slayer-pl-8x3b`](https://huggingface.co/datasets/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).
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- Model line: [`SlayerLab/GoLLeM-110M-PL-v3`](https://huggingface.co/SlayerLab/GoLLeM-110M-PL-v3) → GoLLeM v4 (250M, this corpus).
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## Acknowledgements
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- PII scrubbing: Paweł Puzio ([ppuzio](https://huggingface.co/ppuzio), PR#28 in polish-dynaword)
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- Independent PII gate + international-format fix: Wartownik (N-04, Slayer Kolektyw)
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- License-aware dedup + per-license partitions: Monter (N-03, Slayer Kolektyw)
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- Card integration & training: Hart (N-02, Slayer Kolektyw)
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**Author:** Arkadiusz Słota / SlayerLab.
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## Podsumowanie (PL)
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Dokładny korpus pretreningowy polskiego modelu bazowego **GoLLeM v4 (250M, od zera)** —
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publikowany PRZED treningiem, bajt-w-bajt ten sam zbiór (sha-tied), na którym model będzie
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uczony. 16.58B unikatowych tokenów V32k: ~1.96B kuratorowanych (encyklopedia, nauka, news,
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legal z capem 15%, literatura) + 14.62B oczyszczonego polskiego weba (HPLT v3). Licencje
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per-rekord w trzech partycjach (permissive / CC-BY-SA / web-TDM), PII po dwustopniowym
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scrubie z niezależną bramką (residual 0.0018%), dedup license-aware z pełnego skanu (retencja 95.18%),
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dekontaminacja względem naszych zbiorów ewaluacyjnych.
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