add csv data for topic modeling

.gitattributes

@@ -60,3 +60,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
 *.jsonl filter=lfs diff=lfs merge=lfs -text
 *.jsonl.gz filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -56,14 +56,16 @@ their subject list is nonempty: **162 source articles** have no assigned subject
 omitted. No coverage quota, topic balancing or maximum label count was used.
 Subject frequencies and intersections are consequently uneven.
 
-There is no NLP preprocessing, model scoring or research train/test split in this
-package. The `corpus` split is a storage partition. Source text has not been
-shortened or rewritten for subject selection. No exact duplicate source text was
+The original JSON records have no NLP preprocessing or model scoring. The
+optional preparation workflow below produces separate NLP CSV files without a
+research train/test split. The `corpus` split is a storage partition. Source text
+has not been shortened or rewritten for subject selection. No exact duplicate source text was
 removed. This collection is not a representative sample of Russian writing.
 
 ## Loading
 
-Both configurations use compressed JSON Lines and require no dataset script.
+The `articles` configuration is the default. `articles` and `topics` use
+compressed JSON Lines and the `corpus` split, with no dataset script.
 
 ```python
 from datasets import load_dataset
@@ -90,6 +92,48 @@ topics = load_dataset(
 )
 ```
 
+The prepared NLP files `data/noow.csv` and `data/bow.csv` contain the same
+17,251 article IDs, in source order, with string columns `id`, `raw_text`,
+`vw_text`. Use the CSV reader for these files, separately from the repository's
+JSON Lines configurations:
+
+```python
+noow = load_dataset(
+    "csv",
+    data_files={"corpus": "hf://datasets/TopicNet/RuWiki-Good-2026-V2-Selected/data/noow.csv"},
+    split="corpus",
+)
+bow = load_dataset(
+    "csv",
+    data_files={"corpus": "hf://datasets/TopicNet/RuWiki-Good-2026-V2-Selected/data/bow.csv"},
+    split="corpus",
+)
+```
+
+From the parent of a downloaded repository:
+
+```python
+noow = load_dataset(
+    "csv", data_files={"corpus": "RuWiki-Good-2026-V2-Selected/data/noow.csv"}, split="corpus"
+)
+bow = load_dataset(
+    "csv", data_files={"corpus": "RuWiki-Good-2026-V2-Selected/data/bow.csv"}, split="corpus"
+)
+```
+
+The NLP preparation section describes the normalized namespaces and text fields.
+
+The sidecar [data/labels.json](data/labels.json) is a JSON object mapping every
+Selected article ID to its list of subject slugs, matching both article `topics`
+and the CSV `@topic` namespace. It is not a separate dataset configuration.
+
+```python
+import json
+from pathlib import Path
+
+labels = json.loads(Path("RuWiki-Good-2026-V2-Selected/data/labels.json").read_text(encoding="utf-8"))
+```
+
 ## Article fields
 
 
@@ -227,6 +271,67 @@ fragments of markup or awkward text may remain. `wikitext` is retained so reader
 can inspect these losses or apply a different extraction procedure. Neither
 `raw_text` nor the category metadata is a manually corrected annotation.
 
+## NLP preparation
+
+The [standalone preparation code](https://huggingface.co/datasets/TopicNet/RuWiki-Good-2026-V2/tree/main/preprocessing)
+and [full instructions](https://huggingface.co/datasets/TopicNet/RuWiki-Good-2026-V2#nlp-preparation)
+are maintained in the source collection. Download the code directory and use
+`--dataset selected` to prepare this collection, or `--dataset both` to prepare
+both collections from their pinned sources. Build and verify require Python
+3.11.15; verify needs no NLP packages.
+
+```bash
+python prepare.py --action download --dataset selected --sources-dir ./sources
+python prepare.py --action build --dataset selected --sources-dir ./sources --output-dir ./output --scratch-dir ./scratch
+python prepare.py --action verify --dataset selected --sources-dir ./sources --output-dir ./output
+```
+
+The workflow produces `output/selected/noow.csv` and `bow.csv` with exactly
+`id`, `raw_text`, `vw_text` columns for all 17,251 selected IDs, preserving article
+order. CSV `raw_text` is the title, two LF characters and the original readable
+body with its leading CR/LF characters removed. Original JSON `raw_text` remains
+the readable body alone; the source JSON files are unchanged.
+
+The normalized VW namespaces are `@title`, `@word`, `@bigram`, `@trigram`,
+`@category`, then `@topic`. `@word` contains the body only. `@category` retains
+all direct categories, including service and maintenance categories. `@topic`
+contains the accepted subject slugs described above, not raw Wikipedia rubric
+names. `labels.json` maps article IDs to those same subject slugs. These labels
+remain partial and AI-assisted; preprocessing does not improve their validity.
+NOOW preserves normalized token order within each namespace, not raw prose.
+BOW counts precisely the same tokens separately in every namespace.
+
+Normalization uses spaCy Russian tokenization, pymorphy3 lemmas and the union of
+spaCy and pinned NLTK Russian stopwords. Punctuation is removed; tags, URLs and
+email addresses become placeholders. Cyrillic stress marks are removed and
+lexical Cyrillic hyphenated compounds are retained with ASCII hyphens. Names
+are not masked. There is no article removal, token trimming, research split,
+model training or chain extraction. Build prints phase and document-count
+progress; the manifest reports empty `@word` articles by count and ordered IDs
+without dropping them.
+
+Bigrams and trigrams use independent NLTK PMI selection after frequency ≥5,
+with `nbest(32100)[100:]` for each order. This selection uses **all Selected
+article bodies**, independently of Full; it is corpus-global rather than fitted
+only on a future training partition. For text-only experiments, use only `@word`
+to avoid exposing category, title or subject metadata to the model. The workflow
+does not establish an evaluation without information from held-out documents.
+
+Load a generated CSV directly:
+
+```python
+noow = load_dataset(
+    "csv", data_files={"corpus": "output/selected/noow.csv"}, split="corpus"
+)
+bow = load_dataset(
+    "csv", data_files={"corpus": "output/selected/bow.csv"}, split="corpus"
+)
+```
+
+Preparation code is MIT licensed in the source collection. Article text and its
+normalized derivatives retain the CC BY-SA 4.0 terms and attribution obligations
+below.
+
 ## Attribution, license and use
 
 
@@ -248,15 +353,18 @@ sample of Russian writing or an evenly balanced subject collection. Quality
 status does not guarantee accuracy, neutrality, completeness or suitability for
 any downstream application. Articles can contain sensitive, biased, outdated or
 offensive material. The partial subject labels described here do not establish a complete reference
-annotation. Downstream preprocessing, deduplication policy and research splits
-must be specified separately.
+annotation. Further downstream preprocessing, deduplication policy and research
+splits must be specified separately.
 
 ## Published file identity
 
-| File | Compressed bytes | SHA-256 |
+| File | Bytes | SHA-256 |
 | --- | ---: | --- |
 | `data/articles.jsonl.gz` | 413,342,335 | `9eb7efffe99fc7c9bc3f4b3eb9f0378d91f387081f97cf9abea9c1686797c1ed` |
 | `data/topics.jsonl.gz` | 2,671 | `c8cf602a924f18255e155cdcaa9357907461851e849113c72051c2b89484f22c` |
+| `data/noow.csv` | 1,118,455,706 | `2c71aa6b5a327ebe924f5f0160eaf6f923a86da1a3a7fe8c68aa67f8858f8b6b` |
+| `data/bow.csv` | 875,143,956 | `bff3a82c29ab3b866fe86135ff99b3897deb98dc4e5ed6db4ea7b24bd4011bae` |
+| `data/labels.json` | 649,823 | `e7ceacd69d76601df24bb42e6d0177f46142d0b8ac1bf6e8c22ad4d5a0b0ca70` |
 
 The source article payload SHA-256 is
 `c51f9b6bee4c85f25ffb861060f52490a9276bae289d2aa2db7e0d10e2dda950`.

data/bow.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bff3a82c29ab3b866fe86135ff99b3897deb98dc4e5ed6db4ea7b24bd4011bae
+size 875143956

data/noow.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2c71aa6b5a327ebe924f5f0160eaf6f923a86da1a3a7fe8c68aa67f8858f8b6b
+size 1118455706