add info

README.md

@@ -27,13 +27,14 @@ configs:
 
 This TopicNet derived preparation contains 11,965 retained scientific abstracts from the WOS-11967 release, with 33 observed integer labels serialized as strings. The source has 11,967 rows. The source README declares 35 categories and seven parents, but Y.txt actually contains 33 observed labels (0–32). Names for those 33 labels are not inferred from other WOS subsets. This is a derived research preparation, not the original release.
 
-## Population, labels and research splits
 
-Raw duplicate identity is body text after casefold and whitespace collapse. There are no raw duplicate groups or cross-label conflicts. Two identical lemma-sequence groups remove two rows; five near-duplicate pairs join families without removing their members. Y.txt is the authoritative global class. YL1.txt/YL2.txt remain provenance only: global label 6 has child values 2 and 3, label 10 has child values 2 and 3, and parent/child pair (2,3) maps to global labels 10 and 11. These ambiguities are preserved without repairing Y or inventing class names.
+## Population, labels and splits
 
-Family groups combine raw/lemma duplicate relations, source URL/DOI relations where present, and body five-token shingle Jaccard similarity ≥0.9. Groups are assigned by iterative multilabel stratification with seeds 20260824 and 20260825; all retained members of a group stay together. The resulting disjoint, exhaustive research splits are **fit 7,182, calibration 2,391, validation 2,392**. The Hugging Face split `corpus` contains every retained document; it is not `fit`. Original source membership is preserved in accepted audit/documents metadata and does not define these research splits.
+Raw duplicate identity is body text after casefold and whitespace collapse. There are no raw duplicate groups or cross-label conflicts. Two identical lemma-sequence groups remove two rows; five near-duplicate pairs join families without removing their members. Exported labels use only Y.txt, the authoritative global class. The original source hierarchy has these ambiguities: global label 6 has child values 2 and 3, label 10 has child values 2 and 3, and parent/child pair (2,3) maps to global labels 10 and 11. The original YL1.txt/YL2.txt files and per-document hierarchy metadata are not included in this package. Y values are not repaired and class names are not invented.
 
-Retained class supports come directly from `audit.json.output_rows`:
+Family groups combine raw/lemma duplicate relations, source URL/DOI relations where present, and body five-token shingle Jaccard similarity ≥0.9. Groups are assigned by iterative multilabel stratification with seeds 20260824 and 20260825; all retained members of a group stay together. The resulting disjoint, exhaustive research splits are **fit 7,182, calibration 2,391, validation 2,392**. The Hugging Face split `corpus` contains every retained document; it is not `fit`.
+
+Retained class counts:
 
 | Label | Documents |
 | --- | ---: |
@@ -71,53 +72,28 @@ Retained class supports come directly from `audit.json.output_rows`:
 | 31 | 335 |
 | 32 | 301 |
 
-## Representations and pinned preparation
-
-The source adapter reads aligned X/Y/YL1/YL2 lines with zero-based row indices. IDs are `wos11967_<six-digit-row-index>`. The CSV body is the saved X.txt line without its line terminator; no title is present.
-
-Both configurations read a single UTF-8 CSV with exactly `id,raw_text,vw_text`, sorted lexicographically by ID. `raw_text` is only the saved `documents.jsonl.raw_text` body. Title text is separate metadata. NOOW VW preserves the saved prepared line exactly, excluding its line terminator: document ID followed by only `|@word`. It retains every prepared occurrence, including terms outside the fitted vocabulary. BOW counts those same terms separately within each namespace as `token:count`, with lexicographic token ordering and the same namespace ordering. Class labels never appear as VW namespaces.
 
-NOOW means occurrence order after preprocessing, not untouched or reversible prose. The accepted English preprocessing pins 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. The copied audit records the exact stopwords and resource hashes. Names are not masked.
+## Representations and preprocessing
 
-The research model uses **only @word**, with vocabulary fitted on `fit` only, `min_df=5` and `max_df=0.9`. The saved `*-model.vw` and `*.vw` inputs project prepared text to @word; the separate fitted dictionary controls the vocabulary while the sequence retains OOV positions. The CSVs preserve full prepared sequences, including the title namespace where present. Occurrence offsets and chains are separate saved artifacts, not CSV columns.
+The source adapter reads aligned X/Y/YL1/YL2 lines with zero-based row indices. IDs are `wos11967_<six-digit-row-index>`. The CSV body is the saved X.txt line without its line terminator; no title is present.
 
-`data/accepted/documents.jsonl` stores `offsets` and `title_offsets` as `[start, end]` arrays: zero-based Python character positions with an exclusive end (not byte offsets), relative to `raw_text` and `raw_title`, respectively. WOS has an empty `raw_title` and empty title arrays; no additional title text is included. The corresponding `occurrences` and `title_occurrences` records contain `lemma`, `start`, `end` and `occurrence_index`, aligned in occurrence order with `tokens`/`offsets` and `title_tokens`/`title_offsets`; a lemma need not equal the raw substring. Per-split `data/accepted/wos-11967/*-occurrences.jsonl` files contain TF-IDF decision records keyed by `document_id` and `occurrence_index`, rather than raw spans. Join `document_id` to the document's `id` and use `occurrence_index` to select its body occurrence and raw span.
+Each UTF-8 CSV has exactly `id,raw_text,vw_text` columns and lexicographically ordered document IDs. `raw_text` contains the source abstract. The VW string starts with the document ID followed by only `|@word`. There is no title namespace. 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.
 
-`tfidf-bank.json` and `*-chains.jsonl` preserve the immutable accepted TF-IDF reference bank and extracted chains. These are automatic reference constructions, not gold human annotations. This packaging converts saved representations and does not rerun preprocessing, dictionary creation, reference fitting, scoring or model training. Candidate model results and selected human-audit examples are not 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 joins
+## Loading and metadata
 
-Authenticate with an account that has access if the intended remote repository is private:
+The default configuration is `noow`; `bow` contains aggregated counts. Both expose the complete population as the Hugging Face split `corpus`. Labels and the stored fit/calibration/validation memberships are auxiliary files, joined by document ID. `data/labels.json` directly maps each string ID to a list of string labels; `data/splits.json` directly maps each split name to an ordered ID list.
 
 ```python
 from datasets import load_dataset
 
 repo = "TopicNet/WOS-11967"
-corpus = load_dataset(repo, "noow", split="corpus", token=True)
-bow = load_dataset(repo, "bow", split="corpus", token=True)
+corpus = load_dataset(repo, "noow", split="corpus")
+bow = load_dataset(repo, "bow", split="corpus")
 ```
 
-Labels and research splits are auxiliary JSON files. Their actual schema is a direct ID-to-label-list object and a direct split-name-to-ID-list object, respectively; there are no `labels` or `splits` wrappers:
-
-```python
-import json
-from huggingface_hub import hf_hub_download
-
-def metadata_path(name):
-    return hf_hub_download(repo, "data/accepted/wos-11967/" + name,
-                           repo_type="dataset", token=True)
-
-with open(metadata_path("labels.json"), encoding="utf-8") as handle:
-    labels = json.load(handle)
-with open(metadata_path("splits.json"), encoding="utf-8") as handle:
-    splits = json.load(handle)
-membership = {doc_id: split for split, ids in splits.items() for doc_id in ids}
-annotated = corpus.map(lambda row: {"labels": labels[row["id"]],
-                                   "research_split": membership[row["id"]]})
-fit = annotated.filter(lambda row: row["research_split"] == "fit")
-```
-
-For offline access, obtain the complete package including actual LFS contents and set `HF_DATASETS_OFFLINE=1` and `HF_HUB_OFFLINE=1` before starting Python. Use a writable cache:
+For local use, obtain the complete package including actual Git LFS contents. Set `HF_DATASETS_OFFLINE=1` and `HF_HUB_OFFLINE=1` before Python starts and choose a writable cache directory:
 
 ```python
 import json
@@ -129,25 +105,21 @@ corpus = load_dataset(str(package), "noow", split="corpus",
                       cache_dir="/writable/cache")
 bow = load_dataset(str(package), "bow", split="corpus",
                    cache_dir="/writable/cache")
-with (package / "data/accepted/wos-11967/labels.json").open(encoding="utf-8") as handle:
+with (package / "data/labels.json").open(encoding="utf-8") as handle:
     labels = json.load(handle)
-with (package / "data/accepted/wos-11967/splits.json").open(encoding="utf-8") as handle:
+with (package / "data/splits.json").open(encoding="utf-8") as handle:
     splits = json.load(handle)
 membership = {doc_id: split for split, ids in splits.items() for doc_id in ids}
-fit = corpus.filter(lambda row: membership[row["id"]] == "fit")
+annotated = corpus.map(lambda row: {"labels": labels[row["id"]],
+                                   "research_split": membership[row["id"]]})
+fit = annotated.filter(lambda row: row["research_split"] == "fit")
 ```
 
-An explicit CSV reader is also available:
-`load_dataset("csv", data_files={"corpus": str(package / "data/noow.csv")}, split="corpus", cache_dir="/writable/cache")`.
-For independent Python CSV inspection, raise `csv.field_size_limit` when needed for long bodies.
-
-## Accepted files and integrity
-
-The byte-preserving accepted tree totals **537,238,307 bytes** before adding export CSVs. NOOW CSV is **28,197,299 bytes** and BOW CSV is **26,653,958 bytes**.
+For remote metadata, download `data/labels.json` and `data/splits.json` with `huggingface_hub.hf_hub_download(repo, filename, repo_type="dataset")`, then read the same direct mappings. Private repositories require authorized authentication.
 
-All 34 files from the accepted source subtree are preserved byte for byte under `data/accepted/`, including the original nested `wos-11967/` directory. Root accepted files include documents, audit, config, spec, diagnostics, manifest, checksum manifest and TF-IDF bank. The nested directory retains labels, splits, vocabulary, eligible labels, dictionary exports/native dictionary and per-split prepared/model VW, occurrences, summaries and chains. Saved machine paths are provenance, not loading requirements.
+## Files and integrity
 
-[EXPORT_PROVENANCE.json](EXPORT_PROVENANCE.json) records each original file size/hash, input source pins, the pinned source manifest digest, verbatim source README identity, conversion rules and both CSV sizes/hashes. The original inner checksum manifests are unchanged. Root [SHA256SUMS](SHA256SUMS) covers every package file except itself and Git internals; run `sha256sum -c SHA256SUMS` from this directory. Git LFS attributes apply to CSV, JSONL, VW and native dictionaries; small JSON metadata and cards remain ordinary Git files.
+The package contains this card, unchanged `.gitattributes`, [SHA256SUMS](SHA256SUMS), two CSV representations and [labels](data/labels.json)/[splits](data/splits.json) metadata, plus the verbatim source README. Run `sha256sum -c SHA256SUMS` from the package root to check every publication file except the checksum list itself. The representation files retain their original generated bytes.
 
 ## Rights, limitations and citation
 
@@ -157,4 +129,4 @@ These local packages are intended for private research storage; remote visibilit
 
 Kamran Kowsari, Donald E. Brown, Mojtaba Heidarysafa, Kiana Jafari Meimandi, Matthew S. Gerber and Laura E. Barnes (2017), *HDLTex: Hierarchical Deep Learning for Text Classification*, IEEE ICMLA.
 
-Report this TopicNet derived population and the source manifest hash in [EXPORT_PROVENANCE.json](EXPORT_PROVENANCE.json). The [research repository](https://github.com/Alvant/Alekseev2026IntraTextCoherence) contains the [source adapters and preprocessing](https://github.com/Alvant/Alekseev2026IntraTextCoherence/blob/master/work/wintra/lib/dataset_adapters.py) and [model input preparation](https://github.com/Alvant/Alekseev2026IntraTextCoherence/blob/master/work/wintra/experiments/prepare_model_inputs.py). It is currently private and access-controlled; viewing those links requires permission. Code is linked rather than bundled, and this package does not promise public standalone reproducibility.
+When citing this preparation, identify its retained population and repository revision as well as the source publication.

SHA256SUMS

@@ -0,0 +1,7 @@
+947f03cf79a99d7f9061220c575021c918ae95f64ab75c4112917be804fb51cd  .gitattributes
+303bf68fc20feb4b23046647f098f9d0533c65c7b4710eac8588283ff92bc5cd  README.md
+6da4ab1e624c4ef61c46600d727770f873ff4db3c15541fb481e7af8a715c0ec  SOURCE_README.txt
+4e27d8e46bad08061568222a0931b963f6ad637074b1b227de095af05757d339  data/bow.csv
+138c7a5ed0ae4787afdbd306a7fd2732b392ad8db65c22e7ce312c33a178470c  data/labels.json
+101d1c8a690ae0fe1b68491cb144ff42114280e12770f8a332647106bd12580e  data/noow.csv
+608c14b244f02df40b897dfc7de3411352079bed0a507f2fd8ccf70b9c5996d7  data/splits.json

SOURCE_README.txt

@@ -0,0 +1,71 @@
+Copyright (c) 2017 Kamran Kowsari
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this dataset and associated documentation files (the "Dataset"), to deal
+in the dataset without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Dataset, and to permit persons to whom the dataset is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Dataset.
+
+If you use this dataset please cite:
+Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification
+
+
+Description of Dataset: 
+
+Here is three datasets which include WOS-11967 , WOS-46985, and WOS-5736
+Each folder contains:
+-X.txt 
+-Y.txt
+-YL1.txt
+-YL2.txt
+
+X is input data that include text sequences 
+Y is target value 
+YL1 is target value of level one (parent label)
+YL2 is target value of level one (child label)
+
+Meta-data:
+This folder contain on data file as following attribute:
+Y1	Y2	Y	Domain	area	keywords	Abstract
+
+Abstract is input data that include text sequences of  46,985 published paper
+Y is target value 
+YL1 is target value of level one (parent label)
+YL2 is target value of level one (child label)
+Domain is majaor domain which include 7 labales: {Computer  Science,Electrical  Engineering,  Psychology,  Mechanical  Engineering,Civil  Engineering,  Medical  Science,  biochemistry}
+area is subdomain or area of the paper such as CS-> computer graphics which contain 134 labels.
+keywords : is authors keyword of the papers
+
+
+
+
+Web of Science Dataset WOS-11967
+-This dataset contains 11,967 documents with 35 categories which include 7 parents categories.
+
+
+Web of Science Dataset WOS-46985
+-This dataset contains 46,985 documents with 134 categories which include 7 parents categories.
+
+
+Web of Science Dataset WOS-5736
+-This dataset contains 5,736 documents with 11 categories which include 3 parents categories.
+
+
+
+
+
+Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification
+
+Bib:
+
+@inproceedings{kowsari2017HDLTex,
+  title={HDLTex: Hierarchical Deep Learning for Text Classification},
+  author={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E},
+  booktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on},
+  year={2017},
+  organization={IEEE}
+}