Datasets:

TopicNet
/

RuWiki-Good-2026

@@ -25,8 +25,8 @@ 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
@@ -290,7 +290,7 @@ Person names are not masked.
 Bigrams and trigrams are selected independently using NLTK PMI after a corpus
 frequency threshold of 5, with `nbest(32100)[100:]` for each order. Selection uses
 all normalized article bodies in the chosen collection; it is **corpus-global**,
-not fitted only on a later training split. Full and Selected therefore have
 separately selected n-gram vocabularies. These CSVs are not a claim of evaluation
 without information from held-out documents. For text-only experiments, use only
 `@word`; title, category and subject metadata may reveal evaluation targets.
@@ -300,7 +300,7 @@ without information from held-out documents. For text-only experiments, use only
 Run the preparation commands from `preprocessing/` in the downloaded Full
 collection. Local repository directories may have any names. Replace the paths
 below with your own absolute paths: `work_dir` is a separate working directory,
-and `selected_data` points to the Selected collection's `data/` directory.
 Choose fresh output and scratch paths on a disk with enough space; corpus output
 and scratch inside a published clone are rejected. Python 3.11.15 is the pinned
 runtime for both build and verify: VW escaping depends on its Unicode database.
@@ -364,7 +364,7 @@ those articles remain in the output.
 Verification needs no NLP packages. It checks source identities, output hashes
 against the build manifest, schemas, source text and NOOW/BOW token-count
 agreement. The shared entry point then independently checks the generated CSVs
-and Selected labels against published byte sizes and SHA-256 pins in the corpus
 contract, reporting `PUBLISHED_NLP_OK` on success. These expectations are separate
 from the newly generated manifest. Verification does not rerun tokenization,
 lemmatization or PMI selection.
@@ -396,13 +396,13 @@ bow = load_dataset(
 The same entry point supports `--stage corpus`, with `download`, `build` and
 `verify` actions. The [corpus contract](preprocessing/wikipedia-quality-corpus-20261001.json)
 pins the six 20261001 dump files, the published article/category payloads and the
-retained Selected annotations. This public raw-dump replay has **not yet been run
 end to end**. Its download time, build time and peak disk and memory requirements
 have not been measured; the NLP measurements above do not describe this stage.
 Keep the same `preprocessing/` working directory and path variables as in the
 NLP commands above.
-The Selected annotation directory must already contain `labels.json` and
 `topics.jsonl.gz`. These retained mappings reproduce the published subject
 assignments; the script does not rerun AI labeling.
@@ -433,7 +433,7 @@ published `data/` untouched. Corpus verification compares outputs with the
 independently bundled expected published payloads, rather than trusting hashes
 newly written by the build itself.
-For Full only, set `--dataset full` and omit `--annotations-dir`. Selected can
 also be reconstructed from a previously accepted Full corpus without rereading
 the dumps. From the same `preprocessing/` directory, resolve Full's data path:
@@ -450,7 +450,7 @@ python prepare.py --stage corpus --action verify --dataset selected \
   --output-dir "$work_dir/selected-output"
 ```
-This Selected-only workflow has been verified byte for byte against the published
 archive of all 17,251 articles, the labels file and the topic definitions.
 To continue from reconstructed JSONL to NLP, the corpus output root is directly

 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).
+In the preparation commands below, these datasets are called Full and Thematized, respectively.
 ## Files at a glance
 Bigrams and trigrams are selected independently using NLTK PMI after a corpus
 frequency threshold of 5, with `nbest(32100)[100:]` for each order. Selection uses
 all normalized article bodies in the chosen collection; it is **corpus-global**,
+not fitted only on a later training split. Full and Thematized therefore have
 separately selected n-gram vocabularies. These CSVs are not a claim of evaluation
 without information from held-out documents. For text-only experiments, use only
 `@word`; title, category and subject metadata may reveal evaluation targets.
 Run the preparation commands from `preprocessing/` in the downloaded Full
 collection. Local repository directories may have any names. Replace the paths
 below with your own absolute paths: `work_dir` is a separate working directory,
+and `selected_data` points to the Thematized collection's `data/` directory.
 Choose fresh output and scratch paths on a disk with enough space; corpus output
 and scratch inside a published clone are rejected. Python 3.11.15 is the pinned
 runtime for both build and verify: VW escaping depends on its Unicode database.
 Verification needs no NLP packages. It checks source identities, output hashes
 against the build manifest, schemas, source text and NOOW/BOW token-count
 agreement. The shared entry point then independently checks the generated CSVs
+and Thematized labels against published byte sizes and SHA-256 pins in the corpus
 contract, reporting `PUBLISHED_NLP_OK` on success. These expectations are separate
 from the newly generated manifest. Verification does not rerun tokenization,
 lemmatization or PMI selection.
 The same entry point supports `--stage corpus`, with `download`, `build` and
 `verify` actions. The [corpus contract](preprocessing/wikipedia-quality-corpus-20261001.json)
 pins the six 20261001 dump files, the published article/category payloads and the
+retained Thematized annotations. This public raw-dump replay has **not yet been run
 end to end**. Its download time, build time and peak disk and memory requirements
 have not been measured; the NLP measurements above do not describe this stage.
 Keep the same `preprocessing/` working directory and path variables as in the
 NLP commands above.
+The Thematized annotation directory must already contain `labels.json` and
 `topics.jsonl.gz`. These retained mappings reproduce the published subject
 assignments; the script does not rerun AI labeling.
 independently bundled expected published payloads, rather than trusting hashes
 newly written by the build itself.
+For Full only, set `--dataset full` and omit `--annotations-dir`. Thematized can
 also be reconstructed from a previously accepted Full corpus without rereading
 the dumps. From the same `preprocessing/` directory, resolve Full's data path:
   --output-dir "$work_dir/selected-output"
 ```
+This Thematized-only workflow has been verified byte for byte against the published
 archive of all 17,251 articles, the labels file and the topic definitions.
 To continue from reconstructed JSONL to NLP, the corpus output root is directly