rename datasets

README.md

@@ -15,7 +15,7 @@ configs:
     path: data/categories.jsonl.gz
 ---
 
-# Russian Wikipedia quality articles — 20261001
+# RuWiki-Good-2026 — Russian Wikipedia quality articles
 
 A Russian-language corpus of **17,413 Wikipedia articles** with active community
 quality status in the **20261001** Russian Wikipedia dump, accompanied by a
@@ -24,6 +24,10 @@ wikitext, extracted readable text, direct categories, quality status and revisio
 links. The category catalog supports inspection of direct membership and parent
 relationships; it is not a curated subject taxonomy.
 
+The full collection is published as [TopicNet/RuWiki-Good-2026](https://huggingface.co/datasets/TopicNet/RuWiki-Good-2026).
+Its subject-labeled companion is [TopicNet/RuWiki-Good-Thematized-2026](https://huggingface.co/datasets/TopicNet/RuWiki-Good-Thematized-2026),
+called Selected in the preparation commands below.
+
 ## Files at a glance
 
 | File | Purpose | Bytes |
@@ -70,10 +74,10 @@ With Hugging Face Datasets:
 from datasets import load_dataset
 
 articles = load_dataset(
-    "TopicNet/RuWiki-Good-2026-V2", "articles", split="corpus"
+    "TopicNet/RuWiki-Good-2026", "articles", split="corpus"
 )
 categories = load_dataset(
-    "TopicNet/RuWiki-Good-2026-V2", "categories", split="corpus"
+    "TopicNet/RuWiki-Good-2026", "categories", split="corpus"
 )
 print(len(articles))    # 17413
 print(len(categories))  # 831556
@@ -98,12 +102,12 @@ JSON Lines configurations:
 ```python
 noow = load_dataset(
     "csv",
-    data_files={"corpus": "hf://datasets/TopicNet/RuWiki-Good-2026-V2/data/noow.csv"},
+    data_files={"corpus": "hf://datasets/TopicNet/RuWiki-Good-2026/data/noow.csv"},
     split="corpus",
 )
 bow = load_dataset(
     "csv",
-    data_files={"corpus": "hf://datasets/TopicNet/RuWiki-Good-2026-V2/data/bow.csv"},
+    data_files={"corpus": "hf://datasets/TopicNet/RuWiki-Good-2026/data/bow.csv"},
     split="corpus",
 )
 ```

SHA256SUMS

@@ -6,8 +6,8 @@ d07ab1e6c07da28cc162d786853a73c54e300289e21bcc6667c4072158abdf8f  data/categorie
 b12783fbca1588a57b983032b8eb1534b6d107b1ecfc0633c9f03040d24c6bdf  preprocessing/_wikigood/__init__.py
 996e564b0a96eb8427c77356f6c0f87ba5f09b6cce63629edac807da253be92c  preprocessing/_wikigood/wiki_metadata.py
 af48d12c9d7c5a7560004863790881b0483bc9e26838de4a737fe8d14f71ba56  preprocessing/_wikigood/wikigood_ru_content.py
-8ca18e1ad52b1e83cf0221c87773643033402f666ee03b1a7473b7a0c77c6a55  preprocessing/_wikigood/wikigood_ru_corpus.py
-9a8ea2f173cb81462bd66fb1342d958a48d1add97fea1651db40aa571e153081  preprocessing/_wikigood/wikigood_ru_csv.py
+fb3450b91d7f9a2a3b0b2de72f27a1f02315adb4d00b5d091e18632e56763367  preprocessing/_wikigood/wikigood_ru_corpus.py
+7263d61daf91f62b3a358795868e4f233f29b82036ad483179d5270b6efd2f91  preprocessing/_wikigood/wikigood_ru_csv.py
 d3b04973b735f805164a5ff6dd8c631e523440d1e9d52597a6a314aa888a1f32  preprocessing/_wikigood/wikigood_ru_nlp.py
 ffa062b071e7dbbd4ab1254ba752157df3da8518be7a586f685e5449c5e9c4a7  preprocessing/_wikigood/wikigood_ru_quality_metadata.py
 a1066fd916fb254e3971e505c57c1e6bbc037c74b218f19a3ecd1e83ed4077a4  preprocessing/_wikigood/wikigood_ru_vw.py
@@ -16,4 +16,4 @@ d6d1374d3ea144edc3fd5285ba87dac4daa9dea2857d0f165c6d0943a69a43fc  preprocessing/
 816ab241f6bef38406a35b0b05c1046f210b98fbaa2c9e88e8af3c6d92687f3c  preprocessing/prepare.py
 cc61d5ba11e0521ecfdf9ea818415aaf4b70c1522c9902b99ac271efdf61c5b1  preprocessing/requirements.txt
 baed2546050747c3bedf6eb34db1953e530f99fefe7b9b129d1612631c2ab845  preprocessing/wikipedia-quality-corpus-20261001.json
-f109106d65e960376a3acfeb0fc116c05fcc2e0965af35b1353600edf5355a47  preprocessing/wikipedia-quality-nlp-20261001.json
+0d348f6039015795a323f3663eef786ce69acbe7e20808b2a99be41a95212c8b  preprocessing/wikipedia-quality-nlp-20261001.json

preprocessing/_wikigood/wikigood_ru_corpus.py

@@ -75,7 +75,7 @@ def download_corpus_sources(sources_dir, *, resume=False):
             continue
         offset = part.stat().st_size if part.exists() else 0
         if offset < entry['size_bytes']:
-            headers = {'User-Agent': 'RuWiki-Good-2026-V2-reproduction/1'}
+            headers = {'User-Agent': 'RuWiki-Good-2026-reproduction/1'}
             if offset:
                 headers['Range'] = f'bytes={offset}-'
             print(f'corpus sources download {entry["filename"]} offset={offset}', file=sys.stderr, flush=True)

preprocessing/_wikigood/wikigood_ru_csv.py

@@ -28,13 +28,12 @@ import zipfile
 from .wikigood_ru_nlp import create_wikigood_ru_lemmatizer, select_wikigood_ru_ngrams
 from .wikigood_ru_vw import WikiGoodRuDocument, format_wikigood_ru_vw_row
 
-SOURCE_PINS = {
-    'full': {'repository': 'TopicNet/RuWiki-Good-2026-V2', 'revision': '3c6bdb4ddc1be38b4fd63a480bdd056394a04934', 'count': 17413,
-             'articles': {'sha256': 'c51f9b6bee4c85f25ffb861060f52490a9276bae289d2aa2db7e0d10e2dda950', 'byte_size': 409970311}},
-    'selected': {'repository': 'TopicNet/RuWiki-Good-2026-V2-Selected', 'revision': 'fc1ad9b9491ab122614a95b3aa78b3cdfa5e3881', 'count': 17251,
-                 'articles': {'sha256': '9eb7efffe99fc7c9bc3f4b3eb9f0378d91f387081f97cf9abea9c1686797c1ed', 'byte_size': 413342335},
-                 'topics': {'sha256': 'c8cf602a924f18255e155cdcaa9357907461851e849113c72051c2b89484f22c', 'byte_size': 2671}},
-}
+SPEC_PATH = Path(__file__).resolve().parents[1] / 'specs/wikipedia-quality-nlp-20261001.json'
+# The standalone public package keeps its contract beside the entry point.
+if not SPEC_PATH.is_file():
+    SPEC_PATH = Path(__file__).resolve().parents[1] / 'wikipedia-quality-nlp-20261001.json'
+SOURCE_PINS = json.loads(SPEC_PATH.read_text(encoding='utf-8'))['sources']
+
 STOPWORDS = {'sha256': '48c0e52d8b52546e827f53761fb30300c0ab94f70660d28bd65ba0a86270946b', 'byte_size': 37733}
 STOPWORDS_URL = 'https://raw.githubusercontent.com/nltk/nltk_data/550b6625bcef1f2abff2ff770a5a0d272c9c6b2a/packages/corpora/stopwords.zip'
 RUSSIAN_STOPWORDS_SHA256 = '1743191192b4a4f77fcc216499455dc00c1b8626fdd407076a8deefff80e3d59'

preprocessing/wikipedia-quality-nlp-20261001.json

@@ -1 +1 @@
-{"csv_fields":["id","raw_text","vw_text"],"encoding":"UTF-8","full_namespaces":["@title","@word","@bigram","@trigram","@category"],"line_terminator":"LF","ngram":{"candidate_limit":32100,"head_skip":100,"minimum_frequency":5,"ranking":"PMI","selection":"independent per corpus; no document boundaries crossed"},"normalization":"shared common articles once; preserve all source IDs in numeric order","raw_text":"title + two LF + source raw_text stripped only of leading CR/LF","runtime":{"packages":{"nltk":"3.9.2","pymorphy3":"2.0.6","spacy":"3.8.14"},"python":"3.11.15"},"schema":"wikipedia-quality-nlp-20261001-v1","selected_namespaces":["@title","@word","@bigram","@trigram","@category","@topic"],"sources":{"full":{"articles":{"byte_size":409970311,"sha256":"c51f9b6bee4c85f25ffb861060f52490a9276bae289d2aa2db7e0d10e2dda950"},"count":17413,"repository":"TopicNet/RuWiki-Good-2026-V2","revision":"3c6bdb4ddc1be38b4fd63a480bdd056394a04934"},"selected":{"articles":{"byte_size":413342335,"sha256":"9eb7efffe99fc7c9bc3f4b3eb9f0378d91f387081f97cf9abea9c1686797c1ed"},"count":17251,"repository":"TopicNet/RuWiki-Good-2026-V2-Selected","revision":"fc1ad9b9491ab122614a95b3aa78b3cdfa5e3881","topics":{"byte_size":2671,"sha256":"c8cf602a924f18255e155cdcaa9357907461851e849113c72051c2b89484f22c"}}},"stopwords":{"byte_size":37733,"russian_sha256":"1743191192b4a4f77fcc216499455dc00c1b8626fdd407076a8deefff80e3d59","sha256":"48c0e52d8b52546e827f53761fb30300c0ab94f70660d28bd65ba0a86270946b","url":"https://raw.githubusercontent.com/nltk/nltk_data/550b6625bcef1f2abff2ff770a5a0d272c9c6b2a/packages/corpora/stopwords.zip"},"verification":"source binding and structural verification without morphology replay"}
+{"csv_fields":["id","raw_text","vw_text"],"encoding":"UTF-8","full_namespaces":["@title","@word","@bigram","@trigram","@category"],"line_terminator":"LF","ngram":{"candidate_limit":32100,"head_skip":100,"minimum_frequency":5,"ranking":"PMI","selection":"independent per corpus; no document boundaries crossed"},"normalization":"shared common articles once; preserve all source IDs in numeric order","raw_text":"title + two LF + source raw_text stripped only of leading CR/LF","runtime":{"packages":{"nltk":"3.9.2","pymorphy3":"2.0.6","spacy":"3.8.14"},"python":"3.11.15"},"schema":"wikipedia-quality-nlp-20261001-v1","selected_namespaces":["@title","@word","@bigram","@trigram","@category","@topic"],"sources":{"full":{"articles":{"byte_size":409970311,"sha256":"c51f9b6bee4c85f25ffb861060f52490a9276bae289d2aa2db7e0d10e2dda950"},"count":17413,"repository":"TopicNet/RuWiki-Good-2026","revision":"3c6bdb4ddc1be38b4fd63a480bdd056394a04934"},"selected":{"articles":{"byte_size":413342335,"sha256":"9eb7efffe99fc7c9bc3f4b3eb9f0378d91f387081f97cf9abea9c1686797c1ed"},"count":17251,"repository":"TopicNet/RuWiki-Good-Thematized-2026","revision":"fc1ad9b9491ab122614a95b3aa78b3cdfa5e3881","topics":{"byte_size":2671,"sha256":"c8cf602a924f18255e155cdcaa9357907461851e849113c72051c2b89484f22c"}}},"stopwords":{"byte_size":37733,"russian_sha256":"1743191192b4a4f77fcc216499455dc00c1b8626fdd407076a8deefff80e3d59","sha256":"48c0e52d8b52546e827f53761fb30300c0ab94f70660d28bd65ba0a86270946b","url":"https://raw.githubusercontent.com/nltk/nltk_data/550b6625bcef1f2abff2ff770a5a0d272c9c6b2a/packages/corpora/stopwords.zip"},"verification":"source binding and structural verification without morphology replay"}