Datasets:
Add CC BY-SA 4.0 Wiadomosci Statystyczne Polish articles
Browse filesAdds `wiadomosci_statystyczne_pl`: **169 records and 1,506,269 measured `cl100k_base` proxy tokens** from a deterministic enumeration of the official GUS journal portal.
Source dataset: https://huggingface.co/datasets/PiotrSty/wiadomosci-statystyczne-pl-articles/tree/078ecdc38e8227faff1769f3ad7f080f44a44bef
- [12 complete deterministic samples](https://huggingface.co/datasets/PiotrSty/wiadomosci-statystyczne-pl-articles/blob/078ecdc38e8227faff1769f3ad7f080f44a44bef/artifacts/sample.jsonl)
- [Per-record attribution, landing URLs, DOIs, licenses and PDF evidence](https://huggingface.co/datasets/PiotrSty/wiadomosci-statystyczne-pl-articles/blob/078ecdc38e8227faff1769f3ad7f080f44a44bef/artifacts/attribution.jsonl)
- [Complete source decisions](https://huggingface.co/datasets/PiotrSty/wiadomosci-statystyczne-pl-articles/blob/078ecdc38e8227faff1769f3ad7f080f44a44bef/artifacts/decisions.jsonl)
GUS publishes its journals under CC BY-SA 4.0 since 2022-01-01 (https://nauka.stat.gov.pl/News/Info/60). Every retained article additionally carries an explicit per-article "udostepniony na licencji CC BY-SA 4.0" line on its landing page; preserved page snapshots in the source repo let a reviewer re-check each statement. Articles before 2022 and English-language articles are excluded. Text was extracted from publisher PDFs, running heads removed, normalized, checked for language/quality and limited PII patterns, and exact/near-deduplicated within source.
The source slice retained 182 records after QA. This PR excludes all **13 exact normalized-title or DOI matches** found against 42,071 `biblioteka_nauki` rows at pinned DynaWord revision `fe44303ea2db290c7a195264226477f724973357`, leaving 169 proposed records. No fuzzy title match reached 0.90. This is metadata evidence, not corpus-wide text deduplication.
The manifest separates content-addressed source and target Versions, Protocol, actual Run, Evidence, falsifiable Claims, Actors and typed lineage. Cross-source text deduplication, benchmark checks, review of quoted third-party passages and controlled training ablations remain pending. This PR proposes a source, not a merged or stable DynaWord release.
- artifacts/wiadomosci_statystyczne_pl_ontology_manifest.json +129 -0
- data/wiadomosci_statystyczne_pl/NOTICE.md +7 -0
- data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.attribution.jsonl +0 -0
- data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.checksums.json +11 -0
- data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.decisions.jsonl +194 -0
- data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.md +25 -0
- data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.overlap_audit.json +1766 -0
- data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.parquet +3 -0
- data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.qa.json +16 -0
- data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.sample.jsonl +0 -0
- data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.stats.json +16 -0
- src/build_wiadomosci_statystyczne_pl.py +715 -0
- src/sources.py +15 -0
- src/test_build_wiadomosci_statystyczne_pl.py +98 -0
- src/wiadomosci_statystyczne_requirements.txt +7 -0
artifacts/wiadomosci_statystyczne_pl_ontology_manifest.json
ADDED
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{
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"actors": [
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{
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"id": "actor:piotrsty",
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"type": "Contributor"
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},
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{
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"id": "actor:gus",
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"type": "Organization"
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},
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{
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"id": "actor:codex",
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"type": "Agent"
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}
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],
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"claims": [
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{
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"falsification_condition": "The pinned Parquet, decisions or overlap report do not reproduce these counts.",
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"id": "claim:ws-target-retention",
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"statement": "The proposed DynaWord slice contains 169 records after excluding 13 exact title/DOI overlaps.",
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"supported_by": [
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"evidence:ws-target-stats:4fa4976f4fc359500b2c6ae5aa832b8da58f13d18f8b1645e2f06c1107bfd2a3",
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"evidence:ws-target-overlap:c278dbd093960d05725fe55bf462d8aa767ad0e960f7706f688ca2ae475305ab"
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]
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},
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{
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"falsification_condition": "Target-wide text deduplication and controlled ablations establish those properties.",
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"id": "claim:ws-training-value-untested",
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"statement": "Net text novelty and training benefit remain untested hypotheses.",
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"supported_by": [
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"evidence:ws-target-overlap:c278dbd093960d05725fe55bf462d8aa767ad0e960f7706f688ca2ae475305ab"
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]
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}
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],
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"evidence": [
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{
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"artifact": "data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.stats.json",
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"content_address": "4fa4976f4fc359500b2c6ae5aa832b8da58f13d18f8b1645e2f06c1107bfd2a3",
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"id": "evidence:ws-target-stats:4fa4976f4fc359500b2c6ae5aa832b8da58f13d18f8b1645e2f06c1107bfd2a3",
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"observation_type": "target_slice_counts",
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"produced_by": "run:wiadomosci-statystyczne-dynaword-integration:4fa4976f4fc359500b2c6ae5aa832b8da58f13d18f8b1645e2f06c1107bfd2a3"
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},
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{
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"artifact": "data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.overlap_audit.json",
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"content_address": "c278dbd093960d05725fe55bf462d8aa767ad0e960f7706f688ca2ae475305ab",
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"id": "evidence:ws-target-overlap:c278dbd093960d05725fe55bf462d8aa767ad0e960f7706f688ca2ae475305ab",
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"observation_type": "metadata_overlap_audit",
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"produced_by": "run:wiadomosci-statystyczne-dynaword-integration:4fa4976f4fc359500b2c6ae5aa832b8da58f13d18f8b1645e2f06c1107bfd2a3"
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}
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],
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"objects": [
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{
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"id": "object:dataset:wiadomosci-statystyczne-pl",
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"type": "Dataset"
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},
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{
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"id": "object:dataset:wiadomosci-statystyczne-pl-dynaword-slice",
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"type": "Dataset"
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}
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],
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"pending": [
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"cross-source text deduplication",
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"benchmark contamination check",
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"review of quoted third-party text",
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"controlled training ablation"
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],
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"protocols": [
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{
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"id": "protocol:wiadomosci-statystyczne-dynaword-integration-v1",
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"procedure": "exclude source-QA failures and exact normalized-title or DOI overlaps with pinned biblioteka_nauki; preserve attribution and pending text-level gates"
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}
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],
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"relations": [
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{
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"predicate": "DERIVED_FROM",
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"source": "version:dataset:wiadomosci-statystyczne-pl-dynaword:29a58b3ee7fd785ee151f90136bb09391e3bf8a1758089401a8fc3026dc363df",
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"target": "hf:dataset:PiotrSty/wiadomosci-statystyczne-pl-articles@078ecdc38e8227faff1769f3ad7f080f44a44bef"
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},
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{
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"predicate": "GENERATED_BY",
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"source": "version:dataset:wiadomosci-statystyczne-pl-dynaword:29a58b3ee7fd785ee151f90136bb09391e3bf8a1758089401a8fc3026dc363df",
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"target": "run:wiadomosci-statystyczne-dynaword-integration:4fa4976f4fc359500b2c6ae5aa832b8da58f13d18f8b1645e2f06c1107bfd2a3"
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},
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{
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"predicate": "VALIDATED_AGAINST",
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"source": "version:dataset:wiadomosci-statystyczne-pl-dynaword:29a58b3ee7fd785ee151f90136bb09391e3bf8a1758089401a8fc3026dc363df",
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"target": "hf:dataset:SlayerLab/polish-dynaword@fe44303ea2db290c7a195264226477f724973357"
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}
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],
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"runs": [
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{
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"actor": "actor:codex",
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"finished_at": "2026-09-10T17:43:00.563349+00:00",
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"id": "run:wiadomosci-statystyczne-dynaword-integration:4fa4976f4fc359500b2c6ae5aa832b8da58f13d18f8b1645e2f06c1107bfd2a3",
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"protocol": "protocol:wiadomosci-statystyczne-dynaword-integration-v1",
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"started_at": "2026-09-10T17:43:00.563349+00:00",
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"stats": {
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"added": "2026-09-10",
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"author_coverage": 0.9644970414201184,
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"characters": 4142041,
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"enumerated_2022_plus": 460,
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"kept": 169,
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"license_counts": {
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"CC-BY-SA-4.0": 169
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},
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"sample_count": 12,
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"source_slice_kept": 182,
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"target_overlap_excluded": 13,
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"target_revision": "fe44303ea2db290c7a195264226477f724973357",
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"tokens": 1506269,
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"with_per_article_cc_by_sa": 315
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},
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"success": true
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}
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],
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"schema": "slayer-research-ontology-profile-v1",
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"versions": [
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{
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"content_address": "078ecdc38e8227faff1769f3ad7f080f44a44bef",
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"id": "hf:dataset:PiotrSty/wiadomosci-statystyczne-pl-articles@078ecdc38e8227faff1769f3ad7f080f44a44bef",
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"object": "object:dataset:wiadomosci-statystyczne-pl"
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},
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{
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"content_address": "29a58b3ee7fd785ee151f90136bb09391e3bf8a1758089401a8fc3026dc363df",
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"id": "version:dataset:wiadomosci-statystyczne-pl-dynaword:29a58b3ee7fd785ee151f90136bb09391e3bf8a1758089401a8fc3026dc363df",
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"object": "object:dataset:wiadomosci-statystyczne-pl-dynaword-slice"
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}
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]
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}
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data/wiadomosci_statystyczne_pl/NOTICE.md
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# Attribution and license notice
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Source: Wiadomosci Statystyczne. The Polish Statistician, https://ws.stat.gov.pl/ (Statistics Poland / Glowny Urzad Statystyczny).
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Each record retains its landing URL, DOI, authorship, volume/issue, PDF checksum and the per-article CC BY-SA 4.0 license statement; consult `artifacts/attribution.jsonl`. The CC BY-SA 4.0 terms (attribution, link to the license, indication of changes, share-alike) apply per record.
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Preparation: Piotr Styla with OpenAI Codex. Changes: publisher-PDF text extraction, running-head removal, Unicode and whitespace normalization, page-number-only removal, line-wrap repair, limited email and labelled-phone redaction, language/quality filtering and within-source deduplication. No endorsement by GUS or credited authors is implied.
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data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.attribution.jsonl
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The diff for this file is too large to render.
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data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.checksums.json
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{
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"NOTICE.md": "6e498f86ad9e83a81605e109fffba8ae4f3ef868bbd0c49a22f69fa6e09732ed",
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"wiadomosci_statystyczne_pl.attribution.jsonl": "16e14d19be843547f3be411609eddb94b117dc96fe3d9c2b47c27b65b306804e",
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"wiadomosci_statystyczne_pl.decisions.jsonl": "b7cc7ac7671a01240d3303a7a6d52f956b19ddc12ec5669d1d7cc738de732e5f",
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"wiadomosci_statystyczne_pl.md": "08b8a7c33802dc845a8bbd7daa27d09e004801a7fd24a55bd34ee66edfb96028",
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"wiadomosci_statystyczne_pl.overlap_audit.json": "c9b7d532c6534425fe096b0e36e5c9c54d96f86a04d080cce9d6511048fff31a",
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"wiadomosci_statystyczne_pl.parquet": "29a58b3ee7fd785ee151f90136bb09391e3bf8a1758089401a8fc3026dc363df",
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"wiadomosci_statystyczne_pl.qa.json": "7e97afc76966525bbce74e00e4d7e9fd6a568f98c0adecfe97d6ae34b1672faa",
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"wiadomosci_statystyczne_pl.sample.jsonl": "058bdbcc42f6fc58904aac58ace8216dfb92ff8178693b0184b76283c08f39a7",
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"wiadomosci_statystyczne_pl.stats.json": "c0a6f0f9f6e282343077f459d2a0737fa7aacd8ffb1180a7c376123a9a459078"
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}
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data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.decisions.jsonl
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{"characters": 47037, "id": "wiadomosci_statystyczne_pl_2026_3_048-065", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.7383974318090014, "reason": "include", "replacement_characters": 0, "selected": true}
|
| 174 |
+
{"characters": 9379, "id": "wiadomosci_statystyczne_pl_2026_3_066-069", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.8510502185734087, "reason": "include", "replacement_characters": 0, "selected": true}
|
| 175 |
+
{"characters": 4556, "id": "wiadomosci_statystyczne_pl_2026_3_070-072", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.8178226514486392, "reason": "include", "replacement_characters": 0, "selected": true}
|
| 176 |
+
{"characters": 58045, "id": "wiadomosci_statystyczne_pl_2026_4_029-050", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.690481522956327, "reason": "include", "replacement_characters": 6, "selected": true}
|
| 177 |
+
{"characters": 39331, "id": "wiadomosci_statystyczne_pl_2026_4_051-067", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.6193079250464011, "reason": "include", "replacement_characters": 32, "selected": true}
|
| 178 |
+
{"characters": 5462, "id": "wiadomosci_statystyczne_pl_2026_4_068-070", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.8229586232149396, "reason": "include", "replacement_characters": 0, "selected": true}
|
| 179 |
+
{"characters": 61939, "id": "wiadomosci_statystyczne_pl_2026_5_001-026", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.7584236103262888, "reason": "include", "replacement_characters": 10, "selected": true}
|
| 180 |
+
{"characters": 53738, "id": "wiadomosci_statystyczne_pl_2026_5_045-070", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.6312479065093602, "reason": "excessive_replacement_characters", "replacement_characters": 230, "selected": false}
|
| 181 |
+
{"characters": 3268, "id": "wiadomosci_statystyczne_pl_2026_5_071-072", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.8041615667074663, "reason": "include", "replacement_characters": 0, "selected": true}
|
| 182 |
+
{"characters": 62255, "id": "wiadomosci_statystyczne_pl_2026_6_001-022", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.8007228335073489, "reason": "include", "replacement_characters": 0, "selected": true}
|
| 183 |
+
{"characters": 43385, "id": "wiadomosci_statystyczne_pl_2026_6_042-062", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.7549152933041374, "reason": "include", "replacement_characters": 17, "selected": true}
|
| 184 |
+
{"characters": 4854, "id": "wiadomosci_statystyczne_pl_2026_6_063-065", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.8176761433868974, "reason": "include", "replacement_characters": 0, "selected": true}
|
| 185 |
+
{"characters": 54641, "id": "wiadomosci_statystyczne_pl_2026_7_026-048", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.6642814004136088, "reason": "include", "replacement_characters": 48, "selected": true}
|
| 186 |
+
{"characters": 57803, "id": "wiadomosci_statystyczne_pl_2026_7_049-072", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.7507568811307371, "reason": "include", "replacement_characters": 65, "selected": true}
|
| 187 |
+
{"characters": 6562, "id": "wiadomosci_statystyczne_pl_2026_7_073-075", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.7846693081377629, "reason": "include", "replacement_characters": 0, "selected": true}
|
| 188 |
+
{"characters": 9554, "id": "wiadomosci_statystyczne_pl_2026_7_076-079", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.8522084990579862, "reason": "include", "replacement_characters": 0, "selected": true}
|
| 189 |
+
{"characters": 3154, "id": "wiadomosci_statystyczne_pl_2026_7_080-081", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.7821813570069752, "reason": "include", "replacement_characters": 0, "selected": true}
|
| 190 |
+
{"characters": 67065, "id": "wiadomosci_statystyczne_pl_2026_8_001-024", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.7671512711548498, "reason": "include", "replacement_characters": 10, "selected": true}
|
| 191 |
+
{"characters": 51574, "id": "wiadomosci_statystyczne_pl_2026_8_046-068", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.7653856594408035, "reason": "include", "replacement_characters": 16, "selected": true}
|
| 192 |
+
{"characters": 32293, "id": "wiadomosci_statystyczne_pl_2026_8_069-087", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.7477471897934537, "reason": "include", "replacement_characters": 0, "selected": true}
|
| 193 |
+
{"characters": 7989, "id": "wiadomosci_statystyczne_pl_2026_8_088-091", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.8374014269620729, "reason": "include", "replacement_characters": 0, "selected": true}
|
| 194 |
+
{"characters": 4822, "id": "wiadomosci_statystyczne_pl_2026_8_092-094", "language": "pl", "language_votes": [{"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}, {"confidence": 1.0, "language": "pl"}], "letter_ratio": 0.8158440481128163, "reason": "include", "replacement_characters": 0, "selected": true}
|
data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.md
ADDED
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@@ -0,0 +1,25 @@
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| 1 |
+
# Wiadomosci Statystyczne. The Polish Statistician (GUS) - CC BY-SA 4.0 Polish articles
|
| 2 |
+
|
| 3 |
+
This slice is derived from the deterministic enumeration of the official journal portal
|
| 4 |
+
(https://ws.stat.gov.pl/): all articles published from 2022 onward whose landing page
|
| 5 |
+
carries an explicit per-article "udostepniony na licencji CC BY-SA 4.0" statement and a
|
| 6 |
+
Polish-language PDF marker.
|
| 7 |
+
|
| 8 |
+
- Enumerated articles (2022+): 460
|
| 9 |
+
- With explicit per-article CC BY-SA 4.0 line: 315
|
| 10 |
+
- Source slice retained after QA: 182
|
| 11 |
+
- Exact title/DOI overlaps with `biblioteka_nauki` excluded: 13
|
| 12 |
+
- Proposed records: 169
|
| 13 |
+
- Proposed tokens: 1,506,269 (`cl100k_base` proxy)
|
| 14 |
+
- Characters: 4,142,041; author coverage: 96.4%
|
| 15 |
+
- License: CC BY-SA 4.0 per record
|
| 16 |
+
- Immutable source snapshot: https://huggingface.co/datasets/PiotrSty/wiadomosci-statystyczne-pl-articles/tree/078ecdc38e8227faff1769f3ad7f080f44a44bef
|
| 17 |
+
|
| 18 |
+
Per-record attribution, landing URLs, DOIs, volume/issue, PDF checksums and the verbatim
|
| 19 |
+
per-article license line are preserved; preserved landing-page snapshots let a reviewer
|
| 20 |
+
re-check every license statement. The DynaWord proposal excludes all exact
|
| 21 |
+
normalized-title or DOI matches found against 42,071 pinned
|
| 22 |
+
`biblioteka_nauki` rows. Cross-source text deduplication and benchmark checks remain
|
| 23 |
+
integration gates. pypdf-derived text can retain layout artifacts, math-alphanumeric
|
| 24 |
+
glyph noise, omitted figures and flattened tables. Per-article licensing does not
|
| 25 |
+
establish the status of every quoted third-party passage.
|
data/wiadomosci_statystyczne_pl/wiadomosci_statystyczne_pl.overlap_audit.json
ADDED
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@@ -0,0 +1,1766 @@
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| 184 |
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| 186 |
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| 188 |
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ADDED
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|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
"""Build an auditable slice of CC BY-SA 4.0 Polish Wiadomosci Statystyczne articles."""
|
| 3 |
+
from __future__ import annotations
|
| 4 |
+
|
| 5 |
+
import argparse
|
| 6 |
+
from collections import Counter
|
| 7 |
+
from concurrent.futures import ThreadPoolExecutor, as_completed
|
| 8 |
+
from datetime import datetime, timezone
|
| 9 |
+
from difflib import SequenceMatcher, get_close_matches
|
| 10 |
+
import gzip
|
| 11 |
+
import hashlib
|
| 12 |
+
import html as html_module
|
| 13 |
+
import json
|
| 14 |
+
from pathlib import Path
|
| 15 |
+
import re
|
| 16 |
+
import time
|
| 17 |
+
import unicodedata
|
| 18 |
+
from urllib.parse import quote, urlsplit, urlunsplit
|
| 19 |
+
|
| 20 |
+
import requests
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
SOURCE = "wiadomosci_statystyczne_pl"
|
| 24 |
+
OWN_REPO = "PiotrSty/wiadomosci-statystyczne-pl-articles"
|
| 25 |
+
TARGET = "SlayerLab/polish-dynaword"
|
| 26 |
+
BASE = "https://ws.stat.gov.pl"
|
| 27 |
+
ARCHIVES_URL = BASE + "/Archives"
|
| 28 |
+
POLICY_URL = "https://nauka.stat.gov.pl/News/Info/60"
|
| 29 |
+
SOURCE_URL = BASE + "/"
|
| 30 |
+
FIELDS = ["id", "text", "source", "added", "created", "token_count", "license", "author"]
|
| 31 |
+
UA = "WiadomosciStatystyczneCorpusResearch/0.1 (PiotrSty; open research slice)"
|
| 32 |
+
MIN_YEAR = 2022
|
| 33 |
+
LICENSE_SPDX = "CC-BY-SA-4.0"
|
| 34 |
+
MIN_TEXT_CHARS = 3_000
|
| 35 |
+
EMAIL_RE = re.compile(r"(?i)\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b")
|
| 36 |
+
PHONE_RE = re.compile(r"(?i)(?:\btelefon|\btel\.|\bphone)\s*:?[ \t]*(?:\+48[ \t]*)?\d(?:[ .-]?\d){8}\b")
|
| 37 |
+
ARTICLE_RE = re.compile(r"^/Article/(\d{4})/(\d+)/(\d{2,4}-\d{2,4})$")
|
| 38 |
+
PER_ARTICLE_LICENSE_RE = re.compile(r"udost\w*pnion\w*\s+na\s+licencji\s+CC\s+BY-SA\s+4\.0", re.I)
|
| 39 |
+
BY_SA_URL_RE = re.compile(r"creativecommons\.org/licenses/by-sa/4\.0", re.I)
|
| 40 |
+
MONTHS = {
|
| 41 |
+
"stycznia": 1, "lutego": 2, "marca": 3, "kwietnia": 4, "maja": 5, "czerwca": 6,
|
| 42 |
+
"lipca": 7, "sierpnia": 8, "września": 9, "października": 10, "listopada": 11, "grudnia": 12,
|
| 43 |
+
}
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
def now():
|
| 47 |
+
return datetime.now(timezone.utc).isoformat()
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
def digest(value):
|
| 51 |
+
if not isinstance(value, bytes):
|
| 52 |
+
value = json.dumps(value, ensure_ascii=False, sort_keys=True, separators=(",", ":")).encode("utf-8")
|
| 53 |
+
return hashlib.sha256(value).hexdigest()
|
| 54 |
+
|
| 55 |
+
|
| 56 |
+
def save(path, value):
|
| 57 |
+
path.parent.mkdir(parents=True, exist_ok=True)
|
| 58 |
+
path.write_text(json.dumps(value, ensure_ascii=False, sort_keys=True, indent=2) + "\n", encoding="utf-8")
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
def write_lines(path, rows):
|
| 62 |
+
path.parent.mkdir(parents=True, exist_ok=True)
|
| 63 |
+
path.write_text("".join(json.dumps(row, ensure_ascii=False, sort_keys=True) + "\n" for row in rows), encoding="utf-8")
|
| 64 |
+
|
| 65 |
+
|
| 66 |
+
def read_lines(path):
|
| 67 |
+
return [json.loads(line) for line in path.read_text(encoding="utf-8").split("\n") if line]
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
def load(path):
|
| 71 |
+
return json.loads(path.read_text(encoding="utf-8"))
|
| 72 |
+
|
| 73 |
+
|
| 74 |
+
def request(url, attempts=5, timeout=(15, 90), binary=False):
|
| 75 |
+
response = None
|
| 76 |
+
for attempt in range(attempts):
|
| 77 |
+
response = requests.get(url, headers={"User-Agent": UA}, timeout=timeout)
|
| 78 |
+
if response.status_code not in (429, 500, 502, 503, 504):
|
| 79 |
+
response.raise_for_status()
|
| 80 |
+
return response.content if binary else response.content.decode("utf-8", "replace")
|
| 81 |
+
time.sleep(2 ** attempt)
|
| 82 |
+
response.raise_for_status()
|
| 83 |
+
|
| 84 |
+
|
| 85 |
+
def request_json(url, params=None):
|
| 86 |
+
response = request(url + ("?" + "&".join(f"{k}={v}" for k, v in params.items()) if params else ""))
|
| 87 |
+
return json.loads(response)
|
| 88 |
+
|
| 89 |
+
|
| 90 |
+
def safe_url(url):
|
| 91 |
+
parts = urlsplit(url.replace("http://", "https://"))
|
| 92 |
+
return urlunsplit((parts.scheme, parts.netloc, quote(parts.path), parts.query, parts.fragment))
|
| 93 |
+
|
| 94 |
+
|
| 95 |
+
def article_id(path):
|
| 96 |
+
match = ARTICLE_RE.match(path)
|
| 97 |
+
year, issue, pages = match.groups()
|
| 98 |
+
return f"{year}_{int(issue)}_{pages}"
|
| 99 |
+
|
| 100 |
+
|
| 101 |
+
def meta_content(page, name):
|
| 102 |
+
match = re.search(r'citation_' + name + r'" content="([^"]*)"', page)
|
| 103 |
+
return html_module.unescape(match.group(1)).strip() if match else ""
|
| 104 |
+
|
| 105 |
+
|
| 106 |
+
def per_article_license(page):
|
| 107 |
+
match = PER_ARTICLE_LICENSE_RE.search(page)
|
| 108 |
+
if not match:
|
| 109 |
+
return None
|
| 110 |
+
window = page[match.start():match.end() + 400]
|
| 111 |
+
if not BY_SA_URL_RE.search(window):
|
| 112 |
+
return None
|
| 113 |
+
return re.sub(r"\s+", " ", re.sub(r"<[^>]+>", " ", match.group(0))).strip()
|
| 114 |
+
|
| 115 |
+
|
| 116 |
+
def parse_article(path, page):
|
| 117 |
+
license_line = per_article_license(page)
|
| 118 |
+
langs = sorted(set(re.findall(r"\((Polski|Angielski)\)", page)))
|
| 119 |
+
pdf = meta_content(page, "pdf_url")
|
| 120 |
+
return {
|
| 121 |
+
"article_id": article_id(path),
|
| 122 |
+
"landing_url": BASE + path,
|
| 123 |
+
"title": meta_content(page, "title"),
|
| 124 |
+
"authors": [html_module.unescape(a).strip() for a in re.findall(r'citation_author" content="([^"]*)"', page)],
|
| 125 |
+
"doi": meta_content(page, "doi"),
|
| 126 |
+
"pdf_url": safe_url(pdf) if pdf else "",
|
| 127 |
+
"online_date": meta_content(page, "online_date"),
|
| 128 |
+
"volume": meta_content(page, "volume"),
|
| 129 |
+
"issue": meta_content(page, "issue"),
|
| 130 |
+
"issn": meta_content(page, "issn"),
|
| 131 |
+
"keywords": meta_content(page, "keywords"),
|
| 132 |
+
"languages": langs,
|
| 133 |
+
"has_polish_pdf": "Polski" in langs,
|
| 134 |
+
"per_article_license": bool(license_line),
|
| 135 |
+
"license_line": license_line or "",
|
| 136 |
+
}
|
| 137 |
+
|
| 138 |
+
|
| 139 |
+
def iso_date(text, fallback_year):
|
| 140 |
+
match = re.search(r"(\d{1,2})\s+([a-ząćęłńóśźż]+)\s+(\d{4})", (text or "").casefold())
|
| 141 |
+
if match and match.group(2) in MONTHS:
|
| 142 |
+
return f"{match.group(3)}-{MONTHS[match.group(2)]:02d}-{int(match.group(1)):02d}"
|
| 143 |
+
return str(fallback_year)
|
| 144 |
+
|
| 145 |
+
|
| 146 |
+
def discover(out, workers):
|
| 147 |
+
page = request(ARCHIVES_URL)
|
| 148 |
+
(out / "raw_pages").mkdir(parents=True, exist_ok=True)
|
| 149 |
+
(out / "raw_pages/_archives.html.gz").write_bytes(gzip.compress(page.encode("utf-8")))
|
| 150 |
+
paths = sorted(set(
|
| 151 |
+
href.strip()
|
| 152 |
+
for href in re.findall(r'href="([^"]+)"', page)
|
| 153 |
+
if ARTICLE_RE.match(href.strip()) and int(ARTICLE_RE.match(href.strip()).group(1)) >= MIN_YEAR
|
| 154 |
+
))
|
| 155 |
+
records = []
|
| 156 |
+
|
| 157 |
+
def fetch(path):
|
| 158 |
+
body = request(BASE + path)
|
| 159 |
+
return path, body
|
| 160 |
+
|
| 161 |
+
with ThreadPoolExecutor(max_workers=workers) as pool:
|
| 162 |
+
futures = {pool.submit(fetch, path): path for path in paths}
|
| 163 |
+
for index, future in enumerate(as_completed(futures), 1):
|
| 164 |
+
path, body = future.result()
|
| 165 |
+
(out / "raw_pages" / (article_id(path) + ".html.gz")).write_bytes(gzip.compress(body.encode("utf-8")))
|
| 166 |
+
records.append(parse_article(path, body))
|
| 167 |
+
if index % 50 == 0:
|
| 168 |
+
print(f" {index}/{len(paths)}", flush=True)
|
| 169 |
+
records.sort(key=lambda record: record["article_id"])
|
| 170 |
+
write_lines(out / "source_manifest.jsonl", records)
|
| 171 |
+
selected = [record for record in records if record["per_article_license"] and record["has_polish_pdf"] and record["pdf_url"]]
|
| 172 |
+
selection = {
|
| 173 |
+
"observed_at": now(), "archives_url": ARCHIVES_URL, "policy_url": POLICY_URL, "min_year": MIN_YEAR,
|
| 174 |
+
"article_urls_min_year": len(paths), "enumerated": len(records),
|
| 175 |
+
"with_per_article_cc_by_sa": sum(record["per_article_license"] for record in records),
|
| 176 |
+
"polish_marker": sum(record["has_polish_pdf"] for record in records),
|
| 177 |
+
"selected": len(selected),
|
| 178 |
+
"selected_ids": [record["article_id"] for record in selected],
|
| 179 |
+
"source_manifest_sha256": digest((out / "source_manifest.jsonl").read_bytes()),
|
| 180 |
+
}
|
| 181 |
+
save(out / "selection.json", selection)
|
| 182 |
+
print(json.dumps(selection, ensure_ascii=False, indent=2))
|
| 183 |
+
|
| 184 |
+
|
| 185 |
+
def acquire(out, workers):
|
| 186 |
+
selection = load(out / "selection.json")
|
| 187 |
+
manifest = {record["article_id"]: record for record in read_lines(out / "source_manifest.jsonl")}
|
| 188 |
+
selected = [manifest[article] for article in selection["selected_ids"]]
|
| 189 |
+
(out / "raw_pdf").mkdir(parents=True, exist_ok=True)
|
| 190 |
+
(out / "raw_text").mkdir(parents=True, exist_ok=True)
|
| 191 |
+
results = []
|
| 192 |
+
|
| 193 |
+
def fetch_pdf(record):
|
| 194 |
+
aid = record["article_id"]
|
| 195 |
+
pdf_path = out / "raw_pdf" / (aid + ".pdf")
|
| 196 |
+
text_path = out / "raw_text" / (aid + ".txt")
|
| 197 |
+
if pdf_path.is_file() and text_path.is_file():
|
| 198 |
+
return aid, digest(pdf_path.read_bytes()), pdf_path.stat().st_size, digest(text_path.read_bytes()), "cached"
|
| 199 |
+
payload = request(record["pdf_url"], binary=True)
|
| 200 |
+
if not payload.startswith(b"%PDF"):
|
| 201 |
+
raise ValueError("not a PDF: " + aid)
|
| 202 |
+
pdf_path.write_bytes(payload)
|
| 203 |
+
from pypdf import PdfReader
|
| 204 |
+
import io
|
| 205 |
+
reader = PdfReader(io.BytesIO(payload))
|
| 206 |
+
text = "\n".join(page.extract_text() or "" for page in reader.pages)
|
| 207 |
+
text_path.write_text(text, encoding="utf-8")
|
| 208 |
+
return aid, digest(payload), len(payload), digest(text.encode("utf-8")), str(len(reader.pages))
|
| 209 |
+
|
| 210 |
+
with ThreadPoolExecutor(max_workers=workers) as pool:
|
| 211 |
+
futures = {pool.submit(fetch_pdf, record): record for record in selected}
|
| 212 |
+
for index, future in enumerate(as_completed(futures), 1):
|
| 213 |
+
record = futures[future]
|
| 214 |
+
try:
|
| 215 |
+
aid, pdf_sha, pdf_bytes, text_sha, pages = future.result()
|
| 216 |
+
results.append({**record, "pdf_sha256": pdf_sha, "pdf_bytes": pdf_bytes,
|
| 217 |
+
"text_sha256": text_sha, "pdf_pages": pages,
|
| 218 |
+
"pdf_path": f"raw_pdf/{aid}.pdf", "text_path": f"raw_text/{aid}.txt"})
|
| 219 |
+
except Exception as error:
|
| 220 |
+
results.append({**record, "error": f"{type(error).__name__}: {error}"})
|
| 221 |
+
if index % 25 == 0:
|
| 222 |
+
print(f" {index}/{len(selected)}", flush=True)
|
| 223 |
+
results.sort(key=lambda record: record["article_id"])
|
| 224 |
+
acquisition = {
|
| 225 |
+
"observed_at": now(), "target": selection["selected"],
|
| 226 |
+
"acquired": sum("pdf_sha256" in record for record in results),
|
| 227 |
+
"failed": [record["article_id"] for record in results if "error" in record],
|
| 228 |
+
"selected": results,
|
| 229 |
+
}
|
| 230 |
+
save(out / "acquisition.json", acquisition)
|
| 231 |
+
print(json.dumps({key: acquisition[key] for key in ("target", "acquired", "failed")}, ensure_ascii=False, indent=2))
|
| 232 |
+
|
| 233 |
+
|
| 234 |
+
def strip_running_heads(text):
|
| 235 |
+
lines = text.splitlines()
|
| 236 |
+
counts = Counter(re.sub(r"\d+", "", line).strip() for line in lines if line.strip())
|
| 237 |
+
kept = []
|
| 238 |
+
for line in lines:
|
| 239 |
+
stripped = line.strip()
|
| 240 |
+
if not stripped:
|
| 241 |
+
kept.append("")
|
| 242 |
+
continue
|
| 243 |
+
norm = re.sub(r"\d+", "", stripped).strip()
|
| 244 |
+
if "wiadomo" in stripped.casefold() and "polish statistician" in stripped.casefold():
|
| 245 |
+
continue
|
| 246 |
+
if counts[norm] >= 3 and len(norm) >= 8 and (
|
| 247 |
+
re.match(r"^\d{1,4}\s", stripped) or re.search(r"\s{2,}\d{1,4}\s*$", stripped)
|
| 248 |
+
):
|
| 249 |
+
continue
|
| 250 |
+
kept.append(line)
|
| 251 |
+
return "\n".join(kept)
|
| 252 |
+
|
| 253 |
+
|
| 254 |
+
def normalize(text):
|
| 255 |
+
text = unicodedata.normalize("NFKC", text or "").replace("\u00ad", "").replace("\u200b", "")
|
| 256 |
+
text = re.sub(r"[\x00-\x08\x0b\x0c\x0e-\x1f\x7f]", "", text)
|
| 257 |
+
text = strip_running_heads(text)
|
| 258 |
+
lines = [re.sub(r"[ \t ]+", " ", line).strip() for line in text.splitlines()]
|
| 259 |
+
lines = [line for line in lines if not re.fullmatch(r"\d{1,4}", line)]
|
| 260 |
+
text = "\n".join(lines)
|
| 261 |
+
text = re.sub(r"(?<=\w)-\n(?=[a-ząćęłńóśźż])", "", text)
|
| 262 |
+
text = re.sub(r"(?<![.!?:;\n])\n(?!\n)(?=[a-ząćęłńóśźż])", " ", text)
|
| 263 |
+
return re.sub(r"\n{3,}", "\n\n", text).strip()
|
| 264 |
+
|
| 265 |
+
|
| 266 |
+
def normalize_title(text):
|
| 267 |
+
text = unicodedata.normalize("NFKD", text or "").casefold()
|
| 268 |
+
text = "".join(character for character in text if not unicodedata.combining(character))
|
| 269 |
+
return " ".join(re.findall(r"\w+", text))
|
| 270 |
+
|
| 271 |
+
|
| 272 |
+
def shingle_sketch(text, limit=5_000):
|
| 273 |
+
words = re.findall(r"\w+", text.casefold())
|
| 274 |
+
hashes = set()
|
| 275 |
+
for index in range(max(0, len(words) - 4)):
|
| 276 |
+
value = " ".join(words[index:index + 5]).encode("utf-8")
|
| 277 |
+
hashes.add(int.from_bytes(hashlib.blake2b(value, digest_size=8).digest(), "big"))
|
| 278 |
+
if len(hashes) > limit:
|
| 279 |
+
return set(sorted(hashes)[:limit])
|
| 280 |
+
return hashes
|
| 281 |
+
|
| 282 |
+
|
| 283 |
+
def language_vote(identifier, text):
|
| 284 |
+
chunks = [text[:30_000], text[max(0, len(text) // 2 - 15_000):len(text) // 2 + 15_000], text[-30_000:]]
|
| 285 |
+
votes = [identifier.classify(chunk) for chunk in chunks if chunk.strip()]
|
| 286 |
+
languages = Counter(language for language, _ in votes)
|
| 287 |
+
return (languages.most_common(1)[0][0] if languages else "unknown", votes)
|
| 288 |
+
|
| 289 |
+
|
| 290 |
+
def audit_target(out):
|
| 291 |
+
info = request_json(f"https://huggingface.co/api/datasets/{TARGET}")
|
| 292 |
+
revision = info["sha"]
|
| 293 |
+
tree = request_json(f"https://huggingface.co/api/datasets/{TARGET}/tree/{revision}",
|
| 294 |
+
{"recursive": "true", "expand": "false"})
|
| 295 |
+
discussions = request_json(f"https://huggingface.co/api/datasets/{TARGET}/discussions",
|
| 296 |
+
{"status": "open", "p": 0})
|
| 297 |
+
paths = sorted(item.get("path", "") for item in tree)
|
| 298 |
+
open_rows = [{"num": item.get("num"), "title": item.get("title"), "status": item.get("status"),
|
| 299 |
+
"author": item.get("author", {}).get("name")} for item in discussions.get("discussions", [])]
|
| 300 |
+
terms = ("wiadomosci", "stat.gov", "gus", "polish_statistician", "statystyczne")
|
| 301 |
+
matches = [path for path in paths if any(term in path.casefold() for term in terms)]
|
| 302 |
+
discussion_matches = [row for row in open_rows if any(term in (row.get("title") or "").casefold()
|
| 303 |
+
for term in terms)]
|
| 304 |
+
report = {
|
| 305 |
+
"target": TARGET, "revision": revision, "last_modified": info.get("lastModified"),
|
| 306 |
+
"tree_paths": len(paths), "source_path_matches": matches, "open_discussions": open_rows,
|
| 307 |
+
"matching_open_discussions": discussion_matches, "source_absent": not matches and not discussion_matches,
|
| 308 |
+
"observed_at": now(),
|
| 309 |
+
}
|
| 310 |
+
save(out / "target_audit.json", report)
|
| 311 |
+
print(json.dumps({"revision": revision, "source_absent": report["source_absent"],
|
| 312 |
+
"tree_matches": matches, "discussion_matches": discussion_matches}, ensure_ascii=False, indent=2))
|
| 313 |
+
|
| 314 |
+
|
| 315 |
+
def audit_overlap(out):
|
| 316 |
+
import pyarrow.parquet as pq
|
| 317 |
+
from huggingface_hub import HfApi, HfFileSystem
|
| 318 |
+
|
| 319 |
+
acquisition = load(out / "acquisition.json")
|
| 320 |
+
revision = HfApi().dataset_info(TARGET).sha
|
| 321 |
+
remote = f"datasets/{TARGET}@{revision}/data/biblioteka_nauki/biblioteka_nauki.parquet"
|
| 322 |
+
with HfFileSystem().open(remote, "rb") as handle:
|
| 323 |
+
table = pq.read_table(handle, columns=["id", "attribution"])
|
| 324 |
+
target = []
|
| 325 |
+
for row in table.to_pylist():
|
| 326 |
+
attribution = row.get("attribution") or ""
|
| 327 |
+
parts = attribution.split(" | ")
|
| 328 |
+
title = parts[2] if len(parts) >= 4 else attribution
|
| 329 |
+
target.append((row["id"], attribution, normalize_title(title)))
|
| 330 |
+
target_by_title = {}
|
| 331 |
+
for row_id, attribution, normalized in target:
|
| 332 |
+
if normalized:
|
| 333 |
+
target_by_title.setdefault(normalized, []).append((row_id, attribution))
|
| 334 |
+
target_titles = list(target_by_title)
|
| 335 |
+
results = []
|
| 336 |
+
for record in acquisition["selected"]:
|
| 337 |
+
title = normalize_title(record["title"])
|
| 338 |
+
dois = [record["doi"].casefold()] if record.get("doi") else []
|
| 339 |
+
exact = [{"id": row_id, "attribution": attribution} for row_id, attribution, normalized in target
|
| 340 |
+
if (title and title in normalized) or any(doi in attribution.casefold() for doi in dois)]
|
| 341 |
+
fuzzy = []
|
| 342 |
+
if not exact and title:
|
| 343 |
+
matches = get_close_matches(title, target_titles, n=3, cutoff=0.90)
|
| 344 |
+
for normalized in matches:
|
| 345 |
+
score = SequenceMatcher(None, title, normalized).ratio()
|
| 346 |
+
fuzzy.extend({"score": score, "id": row_id, "attribution": attribution}
|
| 347 |
+
for row_id, attribution in target_by_title[normalized])
|
| 348 |
+
fuzzy = fuzzy[:3]
|
| 349 |
+
results.append({"article_id": record["article_id"], "title": record["title"], "doi": dois,
|
| 350 |
+
"exact_title_or_doi_matches": exact, "fuzzy_title_matches": fuzzy})
|
| 351 |
+
report = {
|
| 352 |
+
"target": f"{TARGET}:data/biblioteka_nauki", "target_revision": revision,
|
| 353 |
+
"method": "DOI or normalized title substring; fallback SequenceMatcher >= 0.90 over attribution titles",
|
| 354 |
+
"target_rows": table.num_rows, "candidate_records": len(results),
|
| 355 |
+
"records_with_exact_match": sum(bool(row["exact_title_or_doi_matches"]) for row in results),
|
| 356 |
+
"records_with_fuzzy_match": sum(bool(row["fuzzy_title_matches"]) for row in results),
|
| 357 |
+
"text_overlap": "not tested; target-wide text dedup remains an integration gate",
|
| 358 |
+
"observed_at": now(), "results": results,
|
| 359 |
+
}
|
| 360 |
+
save(out / "overlap_audit.json", report)
|
| 361 |
+
print(json.dumps({key: report[key] for key in ("target_revision", "target_rows", "candidate_records",
|
| 362 |
+
"records_with_exact_match", "records_with_fuzzy_match")},
|
| 363 |
+
ensure_ascii=False, indent=2))
|
| 364 |
+
|
| 365 |
+
|
| 366 |
+
def build(out):
|
| 367 |
+
import pyarrow as pa
|
| 368 |
+
import pyarrow.parquet as pq
|
| 369 |
+
import tiktoken
|
| 370 |
+
from langid.langid import LanguageIdentifier, model
|
| 371 |
+
|
| 372 |
+
acquisition = load(out / "acquisition.json")
|
| 373 |
+
selection = load(out / "selection.json")
|
| 374 |
+
encoder = tiktoken.get_encoding("cl100k_base")
|
| 375 |
+
identifier = LanguageIdentifier.from_modelstring(model, norm_probs=True)
|
| 376 |
+
identifier.set_languages(["pl", "en", "de", "uk", "ru"])
|
| 377 |
+
rows, attribution, decisions, exact_seen, sketches = [], [], [], {}, {}
|
| 378 |
+
pii = Counter()
|
| 379 |
+
added = acquisition["observed_at"][:10]
|
| 380 |
+
for record in acquisition["selected"]:
|
| 381 |
+
if "error" in record:
|
| 382 |
+
decisions.append({"id": f"{SOURCE}_{record['article_id']}", "selected": False,
|
| 383 |
+
"reason": "acquisition_failed", "error": record["error"]})
|
| 384 |
+
continue
|
| 385 |
+
pdf_path = out / record["pdf_path"]
|
| 386 |
+
text_path = out / record["text_path"]
|
| 387 |
+
if digest(pdf_path.read_bytes()) != record["pdf_sha256"]:
|
| 388 |
+
raise ValueError("PDF checksum mismatch: " + record["article_id"])
|
| 389 |
+
raw = text_path.read_text(encoding="utf-8")
|
| 390 |
+
text = normalize(raw)
|
| 391 |
+
replacement_count = text.count("\ufffd")
|
| 392 |
+
letters = len(re.findall(r"[A-Za-zĄĆĘŁŃÓŚŹŻąćęłńóśźż]", text))
|
| 393 |
+
language, votes = language_vote(identifier, text)
|
| 394 |
+
reason = ""
|
| 395 |
+
if len(text) < MIN_TEXT_CHARS:
|
| 396 |
+
reason = "too_little_extractable_text"
|
| 397 |
+
elif letters / max(len(text), 1) < 0.55:
|
| 398 |
+
reason = "low_letter_ratio"
|
| 399 |
+
elif replacement_count > 100 or replacement_count / max(len(text), 1) > 0.002:
|
| 400 |
+
reason = "excessive_replacement_characters"
|
| 401 |
+
elif language != "pl":
|
| 402 |
+
reason = "non_polish_text"
|
| 403 |
+
text = text.replace("\ufffd", "[UNREADABLE_GLYPH]")
|
| 404 |
+
text, emails = EMAIL_RE.subn("[REDACTED:EMAIL]", text)
|
| 405 |
+
text, phones = PHONE_RE.subn("[REDACTED:PHONE]", text)
|
| 406 |
+
pii.update(email=emails, labelled_phone=phones)
|
| 407 |
+
exact_key = digest(" ".join(text.casefold().split()).encode("utf-8"))
|
| 408 |
+
duplicate_of, duplicate_score = None, 0.0
|
| 409 |
+
if not reason and exact_key in exact_seen:
|
| 410 |
+
reason, duplicate_of, duplicate_score = "normalized_duplicate", exact_seen[exact_key], 1.0
|
| 411 |
+
sketch = shingle_sketch(text)
|
| 412 |
+
if not reason:
|
| 413 |
+
for other_id, other_sketch in sketches.items():
|
| 414 |
+
score = len(sketch & other_sketch) / max(len(sketch | other_sketch), 1)
|
| 415 |
+
if score >= 0.90:
|
| 416 |
+
reason, duplicate_of, duplicate_score = "near_duplicate", other_id, score
|
| 417 |
+
break
|
| 418 |
+
row_id = f"{SOURCE}_{record['article_id']}"
|
| 419 |
+
decision = {
|
| 420 |
+
"id": row_id, "selected": not bool(reason), "reason": reason or "include",
|
| 421 |
+
"characters": len(text), "letter_ratio": letters / max(len(text), 1),
|
| 422 |
+
"replacement_characters": replacement_count, "language": language,
|
| 423 |
+
"language_votes": [{"language": lang, "confidence": float(score)} for lang, score in votes],
|
| 424 |
+
}
|
| 425 |
+
if duplicate_of:
|
| 426 |
+
decision.update({"duplicate_of": duplicate_of, "jaccard": duplicate_score})
|
| 427 |
+
decisions.append(decision)
|
| 428 |
+
if reason:
|
| 429 |
+
continue
|
| 430 |
+
exact_seen[exact_key] = row_id
|
| 431 |
+
sketches[row_id] = sketch
|
| 432 |
+
author = "; ".join(record["authors"]) if record["authors"] else "Unknown"
|
| 433 |
+
created = iso_date(record.get("online_date"), record["article_id"].split("_")[0])
|
| 434 |
+
row = {
|
| 435 |
+
"id": row_id, "text": text, "source": SOURCE, "added": added,
|
| 436 |
+
"created": created, "token_count": len(encoder.encode_ordinary(text)),
|
| 437 |
+
"license": LICENSE_SPDX, "author": author,
|
| 438 |
+
}
|
| 439 |
+
rows.append(row)
|
| 440 |
+
attribution.append({
|
| 441 |
+
"id": row_id, "article_id": record["article_id"], "title": record["title"],
|
| 442 |
+
"authors": record["authors"], "doi": record["doi"], "issn": record["issn"],
|
| 443 |
+
"volume": record["volume"], "issue": record["issue"], "online_date": record["online_date"],
|
| 444 |
+
"keywords": record["keywords"], "landing_url": record["landing_url"],
|
| 445 |
+
"pdf_url": record["pdf_url"], "pdf_sha256": record["pdf_sha256"],
|
| 446 |
+
"pdf_bytes": record["pdf_bytes"], "license": LICENSE_SPDX,
|
| 447 |
+
"license_line": record["license_line"], "license_policy_url": POLICY_URL,
|
| 448 |
+
"text_sha256": digest(text.encode("utf-8")),
|
| 449 |
+
"transformations": ["per-article PDF", "pypdf text extraction",
|
| 450 |
+
"running-head removal (journal name and repeated page-numbered lines)",
|
| 451 |
+
"Unicode/whitespace normalization", "page-number-only removal",
|
| 452 |
+
"line-wrap repair", "email and labelled-phone pattern redaction"],
|
| 453 |
+
})
|
| 454 |
+
root = out / "hf_repo"
|
| 455 |
+
(root / "data").mkdir(parents=True, exist_ok=True)
|
| 456 |
+
(root / "artifacts" / "article_pages").mkdir(parents=True, exist_ok=True)
|
| 457 |
+
schema = pa.schema([(field, pa.int64() if field == "token_count" else pa.string()) for field in FIELDS])
|
| 458 |
+
pq.write_table(pa.Table.from_pylist(rows, schema=schema), root / "data/train-00000-of-00001.parquet", compression="zstd")
|
| 459 |
+
write_lines(root / "artifacts/attribution.jsonl", attribution)
|
| 460 |
+
write_lines(root / "artifacts/decisions.jsonl", decisions)
|
| 461 |
+
write_lines(root / "artifacts/source_manifest.jsonl", read_lines(out / "source_manifest.jsonl"))
|
| 462 |
+
sample = sorted(rows, key=lambda row: digest(("sample:" + row["id"]).encode("utf-8")))[:12]
|
| 463 |
+
write_lines(root / "artifacts/sample.jsonl", sample)
|
| 464 |
+
save(root / "artifacts/selection.json", selection)
|
| 465 |
+
save(root / "artifacts/acquisition.json", acquisition)
|
| 466 |
+
for source_page in sorted((out / "raw_pages").rglob("*.html.gz")):
|
| 467 |
+
destination = root / "artifacts" / "article_pages" / source_page.relative_to(out / "raw_pages")
|
| 468 |
+
destination.parent.mkdir(parents=True, exist_ok=True)
|
| 469 |
+
destination.write_bytes(source_page.read_bytes())
|
| 470 |
+
overlap = load(out / "overlap_audit.json") if (out / "overlap_audit.json").exists() else None
|
| 471 |
+
target_audit = load(out / "target_audit.json") if (out / "target_audit.json").exists() else None
|
| 472 |
+
if overlap:
|
| 473 |
+
save(root / "artifacts/overlap_audit.json", overlap)
|
| 474 |
+
if target_audit:
|
| 475 |
+
save(root / "artifacts/target_audit.json", target_audit)
|
| 476 |
+
stats = {
|
| 477 |
+
"enumerated_min_year": selection["enumerated"],
|
| 478 |
+
"with_per_article_cc_by_sa": selection["with_per_article_cc_by_sa"],
|
| 479 |
+
"selected_polish": selection["selected"], "acquired": acquisition["acquired"],
|
| 480 |
+
"kept": len(rows), "rejected": len(decisions) - len(rows),
|
| 481 |
+
"tokens": sum(row["token_count"] for row in rows),
|
| 482 |
+
"characters": sum(len(row["text"]) for row in rows),
|
| 483 |
+
"author_coverage": sum(row["author"] != "Unknown" for row in rows) / len(rows) if rows else 0,
|
| 484 |
+
"license_counts": dict(Counter(row["license"] for row in rows)),
|
| 485 |
+
"sample_count": len(sample), "added": added,
|
| 486 |
+
}
|
| 487 |
+
qa = {
|
| 488 |
+
"scope": "all Wiadomosci Statystyczne articles from 2022 onward carrying an explicit per-article CC BY-SA 4.0 line and a Polish-language PDF marker",
|
| 489 |
+
"license_gate": "article page must contain 'udostepniony na licencji CC ... creativecommons.org/licenses/by-sa/4.0'; GUS policy statement applies from 2022-01-01",
|
| 490 |
+
"language_gate": "site '(Polski)' PDF marker plus independent three-window langid vote",
|
| 491 |
+
"glyph_gate": "reject when replacement characters exceed 100 or 0.2% of text; scattered TeX-math glyph loss is tolerated and marked",
|
| 492 |
+
"pii_pattern_matches": dict(pii), "exact_dedup": True,
|
| 493 |
+
"near_dedup": "deterministic capped 5-word-shingle hash Jaccard >= 0.90 within source",
|
| 494 |
+
"biblioteka_nauki_overlap": overlap or "pending", "cross_source_text_dedup": "pending target integration",
|
| 495 |
+
"benchmark_overlap": "pending", "limitations": [
|
| 496 |
+
"pypdf extraction may retain layout artifacts; formulas use math-alphanumeric glyphs and isolated unmapped math glyphs are marked [UNREADABLE_GLYPH] (tolerated below 100 chars or 0.2%)",
|
| 497 |
+
"figures are omitted and tables may be flattened",
|
| 498 |
+
"per-article license does not prove every quoted third-party passage is reusable",
|
| 499 |
+
"pattern checks are not comprehensive de-identification",
|
| 500 |
+
"articles published before 2022 and English-language articles are out of scope",
|
| 501 |
+
],
|
| 502 |
+
}
|
| 503 |
+
save(root / "artifacts/stats.json", stats)
|
| 504 |
+
save(root / "artifacts/qa.json", qa)
|
| 505 |
+
protocol_id = "protocol:wiadomosci-statystyczne-v1"
|
| 506 |
+
run = {
|
| 507 |
+
"id": "run:" + digest({"script": digest(Path(__file__).read_bytes()), "selection": selection,
|
| 508 |
+
"acquisition": digest(acquisition)}),
|
| 509 |
+
"protocol": protocol_id, "started_at": selection["observed_at"], "finished_at": now(),
|
| 510 |
+
"success": True, "actor": "actor:codex", "stats": stats,
|
| 511 |
+
}
|
| 512 |
+
save(root / "artifacts/run.json", run)
|
| 513 |
+
excluded = {"README.md", "NOTICE.md", "artifacts/checksums.json", "artifacts/ontology.json"}
|
| 514 |
+
checks = {path.relative_to(root).as_posix(): digest(path.read_bytes()) for path in sorted(root.rglob("*"))
|
| 515 |
+
if path.is_file()
|
| 516 |
+
and path.relative_to(root).as_posix() not in excluded
|
| 517 |
+
and not path.relative_to(root).as_posix().startswith("src/")}
|
| 518 |
+
save(root / "artifacts/checksums.json", checks)
|
| 519 |
+
source_version = "version:source:" + digest({"manifest": selection["source_manifest_sha256"]})
|
| 520 |
+
dataset_version = "version:dataset:" + digest(checks)
|
| 521 |
+
selection_evidence = "evidence:selection:" + digest(selection)
|
| 522 |
+
acquisition_evidence = "evidence:acquisition:" + digest(acquisition)
|
| 523 |
+
qa_evidence = "evidence:qa:" + digest(qa)
|
| 524 |
+
evidence = [
|
| 525 |
+
{"id": selection_evidence, "observation_type": "source_inventory_and_selection",
|
| 526 |
+
"artifact": "artifacts/selection.json", "content_address": digest(selection), "produced_by": run["id"]},
|
| 527 |
+
{"id": acquisition_evidence, "observation_type": "pdf_acquisition_and_extraction",
|
| 528 |
+
"artifact": "artifacts/acquisition.json", "content_address": digest(acquisition), "produced_by": run["id"]},
|
| 529 |
+
{"id": qa_evidence, "observation_type": "source_qa", "artifact": "artifacts/qa.json",
|
| 530 |
+
"content_address": digest(qa), "produced_by": run["id"]},
|
| 531 |
+
]
|
| 532 |
+
overlap_evidence = None
|
| 533 |
+
if overlap:
|
| 534 |
+
overlap_evidence = "evidence:overlap:" + digest(overlap)
|
| 535 |
+
evidence.append({"id": overlap_evidence, "observation_type": "metadata_overlap_audit",
|
| 536 |
+
"artifact": "artifacts/overlap_audit.json", "content_address": digest(overlap),
|
| 537 |
+
"produced_by": run["id"]})
|
| 538 |
+
target_evidence = None
|
| 539 |
+
if target_audit:
|
| 540 |
+
target_evidence = "evidence:target:" + digest(target_audit)
|
| 541 |
+
evidence.append({"id": target_evidence, "observation_type": "target_registry_audit",
|
| 542 |
+
"artifact": "artifacts/target_audit.json", "content_address": digest(target_audit),
|
| 543 |
+
"produced_by": run["id"]})
|
| 544 |
+
ontology = {
|
| 545 |
+
"schema": "slayer-research-ontology-profile-v1",
|
| 546 |
+
"objects": [{"id": "object:source:wiadomosci-statystyczne", "type": "Source"},
|
| 547 |
+
{"id": "object:dataset:wiadomosci-statystyczne-pl", "type": "Dataset"}],
|
| 548 |
+
"versions": [{"id": source_version, "object": "object:source:wiadomosci-statystyczne",
|
| 549 |
+
"content_address": source_version.rsplit(":", 1)[-1]},
|
| 550 |
+
{"id": dataset_version, "object": "object:dataset:wiadomosci-statystyczne-pl",
|
| 551 |
+
"content_address": dataset_version.rsplit(":", 1)[-1]}],
|
| 552 |
+
"protocols": [{"id": protocol_id, "procedure": "enumerate /Archives; require explicit per-article CC BY-SA 4.0 line and Polish PDF marker; pypdf extraction; running-head removal; normalization; PII patterns; exact and near dedup"}],
|
| 553 |
+
"runs": [run], "evidence": evidence,
|
| 554 |
+
"claims": [
|
| 555 |
+
{"id": "claim:per-article-license-observed",
|
| 556 |
+
"statement": f"The enumeration observed {selection['with_per_article_cc_by_sa']} articles from {MIN_YEAR} onward carrying an explicit per-article CC BY-SA 4.0 statement; {selection['selected']} also carried a Polish-language PDF marker.",
|
| 557 |
+
"supported_by": [selection_evidence],
|
| 558 |
+
"falsification_condition": "The preserved article pages and manifest do not reproduce the count and filters."},
|
| 559 |
+
{"id": "claim:slice-retention",
|
| 560 |
+
"statement": f"The slice retained {stats['kept']} records after text QA and within-source deduplication.",
|
| 561 |
+
"supported_by": [acquisition_evidence, qa_evidence],
|
| 562 |
+
"falsification_condition": "The decisions, Parquet rows, or checksums do not reproduce the retention count."},
|
| 563 |
+
{"id": "claim:source-absence-at-audit",
|
| 564 |
+
"statement": "Wiadomosci Statystyczne was not registered as a source in the pinned DynaWord data tree or open pull-request list at audit time.",
|
| 565 |
+
"supported_by": [target_evidence] if target_evidence else [qa_evidence],
|
| 566 |
+
"falsification_condition": "The pinned target evidence contains a matching source or proposal."},
|
| 567 |
+
{"id": "claim:training-value-untested",
|
| 568 |
+
"statement": "Net corpus novelty and training benefit remain untested hypotheses.",
|
| 569 |
+
"supported_by": [qa_evidence] + ([overlap_evidence] if overlap_evidence else []),
|
| 570 |
+
"falsification_condition": "Target-wide text deduplication and controlled ablations establish those properties."},
|
| 571 |
+
],
|
| 572 |
+
"actors": [{"id": "actor:piotrsty", "type": "Contributor"},
|
| 573 |
+
{"id": "actor:gus", "type": "Organization"}, {"id": "actor:codex", "type": "Agent"}],
|
| 574 |
+
"relations": [{"source": dataset_version, "predicate": "DERIVED_FROM", "target": source_version},
|
| 575 |
+
{"source": dataset_version, "predicate": "GENERATED_BY", "target": run["id"]}] +
|
| 576 |
+
([{"source": dataset_version, "predicate": "VALIDATED_AGAINST",
|
| 577 |
+
"target": f"hf:dataset:{TARGET}@{target_audit['revision']}"}] if target_audit else []),
|
| 578 |
+
"pending": ["pre-2022 archive review (license status unresolved)", "cross-source text deduplication",
|
| 579 |
+
"benchmark contamination check", "review of third-party quoted text",
|
| 580 |
+
"controlled training ablation"],
|
| 581 |
+
}
|
| 582 |
+
save(root / "artifacts/ontology.json", ontology)
|
| 583 |
+
card = f"""---
|
| 584 |
+
license: cc-by-sa-4.0
|
| 585 |
+
language:
|
| 586 |
+
- pl
|
| 587 |
+
task_categories:
|
| 588 |
+
- text-generation
|
| 589 |
+
configs:
|
| 590 |
+
- config_name: default
|
| 591 |
+
data_files:
|
| 592 |
+
- split: train
|
| 593 |
+
path: data/train-00000-of-00001.parquet
|
| 594 |
+
---
|
| 595 |
+
|
| 596 |
+
# Wiadomosci Statystyczne (The Polish Statistician) - Polish articles, CC BY-SA 4.0
|
| 597 |
+
|
| 598 |
+
Polish-language articles from *Wiadomosci Statystyczne. The Polish Statistician*
|
| 599 |
+
(https://ws.stat.gov.pl/), the peer-reviewed statistical journal published by
|
| 600 |
+
Statistics Poland (GUS). Scope: articles published from 2022 onward whose
|
| 601 |
+
article page carries an explicit per-article "udostepniony na licencji CC BY-SA
|
| 602 |
+
4.0" statement and a Polish-language PDF marker.
|
| 603 |
+
|
| 604 |
+
- Enumerated articles ({MIN_YEAR}+): {stats['enumerated_min_year']}
|
| 605 |
+
- With explicit per-article CC BY-SA 4.0 line: {stats['with_per_article_cc_by_sa']}
|
| 606 |
+
- Selected (Polish PDF marker): {stats['selected_polish']}
|
| 607 |
+
- Acquired PDFs: {stats['acquired']}
|
| 608 |
+
- Retained after text QA and within-source deduplication: {stats['kept']}
|
| 609 |
+
- Characters: {stats['characters']:,}
|
| 610 |
+
- Tokens: {stats['tokens']:,} (`cl100k_base` proxy)
|
| 611 |
+
- Author coverage: {stats['author_coverage']:.1%}
|
| 612 |
+
- License: CC BY-SA 4.0 per record
|
| 613 |
+
|
| 614 |
+
## Provenance and rights
|
| 615 |
+
|
| 616 |
+
GUS policy (https://nauka.stat.gov.pl/News/Info/60) states that from 1 January
|
| 617 |
+
2022 journals and monographs published or co-published by GUS are released under
|
| 618 |
+
CC BY-SA 4.0. Every retained article additionally carries the per-article
|
| 619 |
+
license line on its landing page; the preserved page snapshots in
|
| 620 |
+
`artifacts/article_pages/` let a reviewer re-check that statement. Each row is
|
| 621 |
+
linked to its landing URL, DOI, authors, volume/issue, PDF URL and checksum.
|
| 622 |
+
Articles from before 2022 show only the site-wide footer license and are
|
| 623 |
+
excluded.
|
| 624 |
+
|
| 625 |
+
## Processing and limitations
|
| 626 |
+
|
| 627 |
+
Text was extracted from publisher PDFs with pypdf, then normalized: running
|
| 628 |
+
heads (journal name; repeated page-numbered lines), Unicode/whitespace
|
| 629 |
+
normalization, page-number-only line removal, line-wrap repair, and limited
|
| 630 |
+
email/phone pattern redaction. Language is checked independently across three
|
| 631 |
+
text windows; exact and deterministic near deduplication run within the source.
|
| 632 |
+
|
| 633 |
+
PDF-derived text may retain layout artifacts; formulas use math-alphanumeric
|
| 634 |
+
glyphs, figures are omitted and tables may be flattened. Pattern checks are not
|
| 635 |
+
comprehensive de-identification. Per-article licensing does not establish the
|
| 636 |
+
status of every quoted third-party passage.
|
| 637 |
+
|
| 638 |
+
## Review artifacts
|
| 639 |
+
|
| 640 |
+
See `artifacts/sample.jsonl`, `attribution.jsonl`, `decisions.jsonl`,
|
| 641 |
+
`source_manifest.jsonl`, `overlap_audit.json`, `stats.json`, `qa.json`,
|
| 642 |
+
`checksums.json`, `run.json` and `ontology.json`. The ontology separates
|
| 643 |
+
content-addressed Objects and Versions, Protocols, actual Runs, Evidence,
|
| 644 |
+
falsifiable Claims, Actors and typed lineage.
|
| 645 |
+
"""
|
| 646 |
+
(root / "README.md").write_text(card, encoding="utf-8")
|
| 647 |
+
(root / "NOTICE.md").write_text(
|
| 648 |
+
"# Attribution and license notice\n\n"
|
| 649 |
+
"Source: Wiadomosci Statystyczne. The Polish Statistician, https://ws.stat.gov.pl/ "
|
| 650 |
+
"(Statistics Poland / Glowny Urzad Statystyczny).\n\n"
|
| 651 |
+
"Each record retains its landing URL, DOI, authorship, volume/issue, PDF checksum and the "
|
| 652 |
+
"per-article CC BY-SA 4.0 license statement; consult `artifacts/attribution.jsonl`. The CC BY-SA 4.0 "
|
| 653 |
+
"terms (attribution, link to the license, indication of changes, share-alike) apply per record.\n\n"
|
| 654 |
+
"Preparation: Piotr Styla with OpenAI Codex. Changes: publisher-PDF text extraction, running-head "
|
| 655 |
+
"removal, Unicode and whitespace normalization, page-number-only removal, line-wrap repair, limited "
|
| 656 |
+
"email and labelled-phone redaction, language/quality filtering and within-source deduplication. "
|
| 657 |
+
"No endorsement by GUS or credited authors is implied.\n",
|
| 658 |
+
encoding="utf-8",
|
| 659 |
+
)
|
| 660 |
+
print(json.dumps(stats, ensure_ascii=False, indent=2))
|
| 661 |
+
|
| 662 |
+
|
| 663 |
+
def verify(out):
|
| 664 |
+
import pyarrow.parquet as pq
|
| 665 |
+
|
| 666 |
+
root = out / "hf_repo"
|
| 667 |
+
table = pq.read_table(root / "data/train-00000-of-00001.parquet")
|
| 668 |
+
rows = table.to_pylist()
|
| 669 |
+
stats = load(root / "artifacts/stats.json")
|
| 670 |
+
decisions = read_lines(root / "artifacts/decisions.jsonl")
|
| 671 |
+
attribution = read_lines(root / "artifacts/attribution.jsonl")
|
| 672 |
+
sample = read_lines(root / "artifacts/sample.jsonl")
|
| 673 |
+
assert table.column_names == FIELDS
|
| 674 |
+
assert len(rows) == stats["kept"] == len(attribution)
|
| 675 |
+
assert sum(item["selected"] for item in decisions) == len(rows)
|
| 676 |
+
assert sum(row["token_count"] for row in rows) == stats["tokens"]
|
| 677 |
+
assert all(row["source"] == SOURCE and row["license"] == LICENSE_SPDX for row in rows)
|
| 678 |
+
assert all(EMAIL_RE.search(row["text"]) is None for row in rows)
|
| 679 |
+
by_id = {row["id"]: row for row in rows}
|
| 680 |
+
assert len(sample) == stats["sample_count"] and all(by_id[row["id"]] == row for row in sample)
|
| 681 |
+
ontology = load(root / "artifacts/ontology.json")
|
| 682 |
+
evidence = {item["id"] for item in ontology["evidence"]}
|
| 683 |
+
assert all(item["falsification_condition"] and set(item["supported_by"]) <= evidence for item in ontology["claims"])
|
| 684 |
+
checks = load(root / "artifacts/checksums.json")
|
| 685 |
+
assert all((root / path).is_file() and digest((root / path).read_bytes()) == checksum for path, checksum in checks.items())
|
| 686 |
+
for item in ontology["evidence"]:
|
| 687 |
+
artifact = root / item["artifact"]
|
| 688 |
+
assert artifact.is_file() and digest(load(artifact)) == item["content_address"]
|
| 689 |
+
print(json.dumps({"verified": True, **stats}, ensure_ascii=False, indent=2))
|
| 690 |
+
|
| 691 |
+
|
| 692 |
+
def main():
|
| 693 |
+
parser = argparse.ArgumentParser()
|
| 694 |
+
parser.add_argument("--output", type=Path, required=True)
|
| 695 |
+
parser.add_argument("--workers", type=int, default=8)
|
| 696 |
+
parser.add_argument("command", choices=["discover", "acquire", "audit_target", "audit_overlap",
|
| 697 |
+
"build", "verify"])
|
| 698 |
+
args = parser.parse_args()
|
| 699 |
+
if args.command == "discover":
|
| 700 |
+
discover(args.output, args.workers)
|
| 701 |
+
elif args.command == "acquire":
|
| 702 |
+
acquire(args.output, args.workers)
|
| 703 |
+
elif args.command == "audit_target":
|
| 704 |
+
audit_target(args.output)
|
| 705 |
+
elif args.command == "audit_overlap":
|
| 706 |
+
audit_overlap(args.output)
|
| 707 |
+
elif args.command == "build":
|
| 708 |
+
build(args.output)
|
| 709 |
+
else:
|
| 710 |
+
verify(args.output)
|
| 711 |
+
|
| 712 |
+
|
| 713 |
+
if __name__ == "__main__":
|
| 714 |
+
main()
|
| 715 |
+
|
src/sources.py
CHANGED
|
@@ -483,6 +483,21 @@ SOURCES = {
|
|
| 483 |
"created": "unknown",
|
| 484 |
"is_ocr": False,
|
| 485 |
},
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 486 |
"europeana": {
|
| 487 |
"release": None,
|
| 488 |
# Direct rebuild output from fetch_europeana.py, never the raw
|
|
|
|
| 483 |
"created": "unknown",
|
| 484 |
"is_ocr": False,
|
| 485 |
},
|
| 486 |
+
"wiadomosci_statystyczne_pl": {
|
| 487 |
+
"added": "2026-09-10",
|
| 488 |
+
"release": None,
|
| 489 |
+
"file_key": "wiadomosci_statystyczne_pl",
|
| 490 |
+
"pretty": "Wiadomosci Statystyczne. The Polish Statistician (GUS)",
|
| 491 |
+
"license": "CC BY-SA 4.0",
|
| 492 |
+
"license_spdx": "CC-BY-SA-4.0",
|
| 493 |
+
"traceable": "Every retained article carries an explicit per-article CC BY-SA 4.0 statement on its landing page; preserved page snapshots, landing URLs, DOIs, authors, volume/issue, PDF checksums and license lines are kept per record.",
|
| 494 |
+
"upstream": "https://ws.stat.gov.pl/",
|
| 495 |
+
"provenance": "Enumerated deterministically from the journal's server-rendered /Archives index; article pages expose citation metadata, DOI, PDF link, language marker and the per-article license line. GUS publishes its journals under CC BY-SA 4.0 since 2022-01-01 (nauka.stat.gov.pl/News/Info/60). Publisher PDFs fetched directly, extracted with pypdf, running heads removed, normalized, PII patterns redacted, language/quality filtered and within-source deduplicated.",
|
| 496 |
+
"domain": "academic/statistics",
|
| 497 |
+
"created": "2022-2026",
|
| 498 |
+
"is_ocr": False,
|
| 499 |
+
"custom_datasheet": True,
|
| 500 |
+
},
|
| 501 |
"europeana": {
|
| 502 |
"release": None,
|
| 503 |
# Direct rebuild output from fetch_europeana.py, never the raw
|
src/test_build_wiadomosci_statystyczne_pl.py
ADDED
|
@@ -0,0 +1,98 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import importlib.util
|
| 2 |
+
from pathlib import Path
|
| 3 |
+
|
| 4 |
+
|
| 5 |
+
SCRIPT = Path(__file__).with_name("build_wiadomosci_statystyczne_pl.py")
|
| 6 |
+
SPEC = importlib.util.spec_from_file_location("build_wiadomosci_statystyczne_pl", SCRIPT)
|
| 7 |
+
MODULE = importlib.util.module_from_spec(SPEC)
|
| 8 |
+
SPEC.loader.exec_module(MODULE)
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
def page(per_article=True, langs='(Polski)', pdf=True):
|
| 12 |
+
lic = ('© Jan Kowalski. Artykuł udostępniony na licencji CC BY-SA 4.0 '
|
| 13 |
+
'<a href="https://creativecommons.org/licenses/by-sa/4.0/legalcode.pl">x</a>') if per_article else ''
|
| 14 |
+
pdf_meta = '<meta name="citation_pdf_url" content="http://ws.stat.gov.pl/WS/2024/1/x.pdf" />' if pdf else ''
|
| 15 |
+
return (
|
| 16 |
+
'<html><head>'
|
| 17 |
+
'<meta name="citation_title" content="Tytuł & test" />'
|
| 18 |
+
'<meta name="citation_author" content="Jan Kowalski" />'
|
| 19 |
+
'<meta name="citation_doi" content="10.5604/01.3001.0000.0001" />'
|
| 20 |
+
'<meta name="citation_online_date" content="15 marca 2024" />'
|
| 21 |
+
f'{pdf_meta}</head><body>{lic} ' + langs + ' PDF</body></html>'
|
| 22 |
+
)
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
def test_article_gate_is_fail_closed():
|
| 26 |
+
record = MODULE.parse_article('/Article/2024/1/001-020', page())
|
| 27 |
+
assert record["per_article_license"] and record["has_polish_pdf"] and record["pdf_url"]
|
| 28 |
+
assert not MODULE.parse_article('/Article/2024/1/001-020', page(per_article=False))["per_article_license"]
|
| 29 |
+
assert not MODULE.parse_article('/Article/2024/1/001-020', page(langs='(Angielski)'))["has_polish_pdf"]
|
| 30 |
+
assert not MODULE.parse_article('/Article/2024/1/001-020', page(pdf=False))["pdf_url"]
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
def test_footer_alone_does_not_count_as_per_article_license():
|
| 34 |
+
body = '<footer>pewne prawa zastrzeżone. Licencja Creative Commons Uznanie autorstwa - ' \
|
| 35 |
+
'Na tych samych warunkach 4.0 (CC BY-SA 4.0) ' \
|
| 36 |
+
'<a href="https://creativecommons.org/licenses/by-sa/4.0/legalcode.pl">x</a></footer>'
|
| 37 |
+
assert not MODULE.parse_article('/Article/2021/3/080-082', body)["per_article_license"]
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
def test_parse_extracts_metadata_and_unescapes_entities():
|
| 41 |
+
record = MODULE.parse_article('/Article/2024/1/001-020', page())
|
| 42 |
+
assert record["article_id"] == "2024_1_001-020"
|
| 43 |
+
assert record["title"] == "Tytuł & test"
|
| 44 |
+
assert record["authors"] == ["Jan Kowalski"]
|
| 45 |
+
assert record["doi"] == "10.5604/01.3001.0000.0001"
|
| 46 |
+
assert record["pdf_url"].startswith("https://ws.stat.gov.pl/")
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
def test_safe_url_quotes_unicode_paths():
|
| 50 |
+
url = MODULE.safe_url("http://ws.stat.gov.pl/WS/2024/1/tytuł’s.pdf?v=1")
|
| 51 |
+
assert url.startswith("https://ws.stat.gov.pl/")
|
| 52 |
+
assert "’" not in url and "ł" not in url
|
| 53 |
+
assert "v=1" in url
|
| 54 |
+
|
| 55 |
+
|
| 56 |
+
def test_iso_date_parses_polish_months():
|
| 57 |
+
assert MODULE.iso_date("15 marca 2024", "2024") == "2024-03-15"
|
| 58 |
+
assert MODULE.iso_date("31 stycznia 2022", "2022") == "2022-01-31"
|
| 59 |
+
assert MODULE.iso_date("", "2023") == "2023"
|
| 60 |
+
|
| 61 |
+
|
| 62 |
+
def test_strip_running_heads_drops_journal_and_numbered_repeats():
|
| 63 |
+
doc = (
|
| 64 |
+
"Wiadomości Statystyczne. The Polish Statistician, 2022, vol. 67, 12, 39–61 DOI: 10.x\n"
|
| 65 |
+
"Pierwszy akapit treści.\n"
|
| 66 |
+
"40 Wiadomości Statystyczne. The Polish Statistician 2022 | 12\n"
|
| 67 |
+
"Drugi akapit.\n"
|
| 68 |
+
"J. BOŻEK, J. SZEWCZYK Ocena poziomu rozwoju... 41\n"
|
| 69 |
+
"Trzeci akapit.\n"
|
| 70 |
+
"J. BOŻEK, J. SZEWCZYK Ocena poziomu rozwoju... 43\n"
|
| 71 |
+
"Czwarty akapit.\n"
|
| 72 |
+
"J. BOŻEK, J. SZEWCZYK Ocena poziomu rozwoju... 45\n"
|
| 73 |
+
"Piąty akapit.\n"
|
| 74 |
+
"Źródło: obliczenia własne na podstawie danych z BDL.\n"
|
| 75 |
+
"Szósty akapit.\n"
|
| 76 |
+
"Źródło: obliczenia własne na podstawie danych z BDL.\n"
|
| 77 |
+
"Siódmy akapit.\n"
|
| 78 |
+
"Źródło: obliczenia własne na podstawie danych z BDL.\n"
|
| 79 |
+
)
|
| 80 |
+
result = MODULE.strip_running_heads(doc)
|
| 81 |
+
assert "Polish Statistician" not in result
|
| 82 |
+
assert "J. BOŻEK" not in result
|
| 83 |
+
assert "Pierwszy akapit treści." in result
|
| 84 |
+
assert result.count("Źródło: obliczenia własne") == 3
|
| 85 |
+
|
| 86 |
+
|
| 87 |
+
def test_normalize_repairs_hyphenation_and_page_numbers():
|
| 88 |
+
assert MODULE.normalize("Pierw-\nszy akapit.\n12\n\ndrugi wiersz") == "Pierwszy akapit.\n\ndrugi wiersz"
|
| 89 |
+
|
| 90 |
+
|
| 91 |
+
def test_title_normalization_is_case_and_diacritic_insensitive():
|
| 92 |
+
assert MODULE.normalize_title("Ćwiczenia: Łódź") == "cwiczenia łodz"
|
| 93 |
+
|
| 94 |
+
|
| 95 |
+
def test_text_bound_and_license():
|
| 96 |
+
assert MODULE.MIN_TEXT_CHARS == 3_000
|
| 97 |
+
assert MODULE.LICENSE_SPDX == "CC-BY-SA-4.0"
|
| 98 |
+
assert MODULE.MIN_YEAR == 2022
|
src/wiadomosci_statystyczne_requirements.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
requests==2.32.5
|
| 2 |
+
pyarrow==23.0.1
|
| 3 |
+
tiktoken==0.12.0
|
| 4 |
+
langid==1.1.6
|
| 5 |
+
pypdf==5.9.0
|
| 6 |
+
huggingface_hub>=1.0,<2
|
| 7 |
+
pytest>=8,<10
|