Datasets:

TopicNet
/

SciNews

@@ -55,8 +55,6 @@ The source adapter saves `News_Body` as the body and `News_Title` as the origina
 Each UTF-8 CSV has exactly `id,raw_text,vw_text` columns and lexicographically ordered document IDs. `raw_text` contains the original, unnormalized headline followed by a blank line (two LF characters) and the original body. Only leading CR and LF characters are removed from the body at this boundary; all other headline and body characters are preserved. The VW string starts with the document ID followed by `|@title` then `|@word`: `@title` contains normalized original headline tokens, and `@word` contains normalized body tokens only. Normalizing the complete `raw_text` therefore does not reproduce the existing features in each modality. Labels are separate from the VW string. `noow` preserves normalized token occurrences in order; `bow` counts the same tokens independently per namespace as `token:count`, with lexicographic token order. The full prepared token sequences are included.
-The headline/body concatenation affects only the display/export field `raw_text`; it does not define a newly processed population. Existing document selection, body-based deduplication, IDs, labels, split memberships and prepared VW features are unchanged.
 NOOW means occurrence order after preprocessing, not untouched or reversible prose. English preprocessing uses Python 3.11.15, NLTK 3.9.2 and NLTK-data commit `550b6625bcef1f2abff2ff770a5a0d272c9c6b2a`: WordPunctTokenizer, lowercase per original span, minimum token length 2, averaged_perceptron_tagger_eng, WordNet POS mapping J/V/N/R with noun fallback, English stopword removal and `lemma.isalpha()` filtering. Names are not masked.
 ## Loading and metadata

 Each UTF-8 CSV has exactly `id,raw_text,vw_text` columns and lexicographically ordered document IDs. `raw_text` contains the original, unnormalized headline followed by a blank line (two LF characters) and the original body. Only leading CR and LF characters are removed from the body at this boundary; all other headline and body characters are preserved. The VW string starts with the document ID followed by `|@title` then `|@word`: `@title` contains normalized original headline tokens, and `@word` contains normalized body tokens only. Normalizing the complete `raw_text` therefore does not reproduce the existing features in each modality. Labels are separate from the VW string. `noow` preserves normalized token occurrences in order; `bow` counts the same tokens independently per namespace as `token:count`, with lexicographic token order. The full prepared token sequences are included.
 NOOW means occurrence order after preprocessing, not untouched or reversible prose. English preprocessing uses Python 3.11.15, NLTK 3.9.2 and NLTK-data commit `550b6625bcef1f2abff2ff770a5a0d272c9c6b2a`: WordPunctTokenizer, lowercase per original span, minimum token length 2, averaged_perceptron_tagger_eng, WordNet POS mapping J/V/N/R with noun fallback, English stopword removal and `lemma.isalpha()` filtering. Names are not masked.
 ## Loading and metadata