add data

.gitattributes

@@ -58,3 +58,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text
+*.jsonl filter=lfs diff=lfs merge=lfs -text
+*.vw filter=lfs diff=lfs merge=lfs -text
+*.native filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,250 @@
 ---
-license: mit
+language:
+  - en
+license: cc-by-4.0
+task_categories:
+  - text-classification
+tags:
+  - topic-modeling
+  - topic-modelling
+  - text-clustering
+  - document-representation
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: noow
+    default: true
+    data_files:
+      - split: corpus
+        path: data/noow.csv
+  - config_name: bow
+    data_files:
+      - split: corpus
+        path: data/bow.csv
 ---
+
+# WOS-46985
+
+This TopicNet preparation contains 46,972 retained English scientific abstracts from the 46,985-row WOS-46985 source, with 134 observed global classes and 7 parent codes. It provides raw abstracts, two normalized text representations, labels, the original numeric hierarchy and generated research splits. It is a derived preparation of the [Web of Science dataset, version 6](https://data.mendeley.com/datasets/9rw3vkcfy4/6), DOI [10.17632/9rw3vkcfy4.6](https://doi.org/10.17632/9rw3vkcfy4.6).
+
+## Population, labels and splits
+
+Raw duplicate identity is source abstract text after casefold and whitespace collapse. Raw duplicates are checked first, then identical ordered lemma sequences: a same-class group keeps its smallest document ID, while a group with conflicting global classes is quarantined in full. Documents with empty normalized sequences are removed. The removal counts are 0 same-class raw duplicates, 0 rows in raw conflicts, 13 same-class lemma duplicates, 0 rows in lemma conflicts and 0 empty normalized documents; 13 rows are removed in total.
+
+Near duplicates are grouped, not deleted: body five-token shingle Jaccard similarity ≥0.9 identifies 18 pairs among retained documents. Families combine the raw/lemma duplicate relations and these near-duplicate links. Each family stays in one split. Iterative multilabel stratification uses seeds 20260824 and 20260825, targeting approximately 60%/20%/20%. These are newly generated research splits, not official source train/test splits: **fit 28,181, calibration 9,366, validation 9,425**. They are disjoint and exhaustive. The Hugging Face split `corpus` contains every retained document; it is not `fit`.
+
+`Y.txt` supplies the authoritative global class, stored as a string in `data/labels.json`. `YL1.txt` supplies the parent code and `YL2.txt` the local child code, which must be interpreted together with its parent. The observed source codes have 134 global classes and seven parent codes. The source hierarchy is inconsistent: global classes 40 and 43 each occur under parents 2 and 5, and global class 76 occurs under parents 4 and 5. These source values are preserved. No class names are inferred and numeric classes are not assumed comparable across WOS subsets.
+
+Retained class counts:
+
+| Global class | Documents |
+| --- | ---: |
+| 0 | 432 |
+| 1 | 398 |
+| 2 | 445 |
+| 3 | 387 |
+| 4 | 379 |
+| 5 | 412 |
+| 6 | 415 |
+| 7 | 443 |
+| 8 | 377 |
+| 9 | 416 |
+| 10 | 403 |
+| 11 | 43 |
+| 12 | 402 |
+| 13 | 379 |
+| 14 | 425 |
+| 15 | 392 |
+| 16 | 365 |
+| 17 | 447 |
+| 18 | 44 |
+| 19 | 375 |
+| 20 | 54 |
+| 21 | 426 |
+| 22 | 417 |
+| 23 | 392 |
+| 24 | 413 |
+| 25 | 646 |
+| 26 | 378 |
+| 27 | 148 |
+| 28 | 276 |
+| 29 | 274 |
+| 30 | 344 |
+| 31 | 429 |
+| 32 | 419 |
+| 33 | 389 |
+| 34 | 397 |
+| 35 | 391 |
+| 36 | 394 |
+| 37 | 387 |
+| 38 | 350 |
+| 39 | 387 |
+| 40 | 510 |
+| 41 | 376 |
+| 42 | 365 |
+| 43 | 373 |
+| 44 | 368 |
+| 45 | 296 |
+| 46 | 389 |
+| 47 | 404 |
+| 48 | 395 |
+| 49 | 362 |
+| 50 | 416 |
+| 51 | 360 |
+| 52 | 371 |
+| 53 | 402 |
+| 54 | 346 |
+| 55 | 420 |
+| 56 | 386 |
+| 57 | 387 |
+| 58 | 361 |
+| 59 | 289 |
+| 60 | 335 |
+| 61 | 410 |
+| 62 | 423 |
+| 63 | 384 |
+| 64 | 441 |
+| 65 | 447 |
+| 66 | 395 |
+| 67 | 148 |
+| 68 | 418 |
+| 69 | 384 |
+| 70 | 412 |
+| 71 | 373 |
+| 72 | 309 |
+| 73 | 357 |
+| 74 | 368 |
+| 75 | 321 |
+| 76 | 264 |
+| 77 | 317 |
+| 78 | 262 |
+| 79 | 294 |
+| 80 | 309 |
+| 81 | 339 |
+| 82 | 260 |
+| 83 | 335 |
+| 84 | 350 |
+| 85 | 198 |
+| 86 | 237 |
+| 87 | 353 |
+| 88 | 327 |
+| 89 | 97 |
+| 90 | 290 |
+| 91 | 222 |
+| 92 | 372 |
+| 93 | 341 |
+| 94 | 129 |
+| 95 | 291 |
+| 96 | 288 |
+| 97 | 182 |
+| 98 | 358 |
+| 99 | 315 |
+| 100 | 246 |
+| 101 | 336 |
+| 102 | 90 |
+| 103 | 305 |
+| 104 | 267 |
+| 105 | 254 |
+| 106 | 371 |
+| 107 | 178 |
+| 108 | 253 |
+| 109 | 197 |
+| 110 | 359 |
+| 111 | 301 |
+| 112 | 320 |
+| 113 | 339 |
+| 114 | 343 |
+| 115 | 298 |
+| 116 | 148 |
+| 117 | 128 |
+| 118 | 202 |
+| 119 | 188 |
+| 120 | 118 |
+| 121 | 257 |
+| 122 | 365 |
+| 123 | 142 |
+| 124 | 361 |
+| 125 | 746 |
+| 126 | 566 |
+| 127 | 622 |
+| 128 | 652 |
+| 129 | 566 |
+| 130 | 576 |
+| 131 | 749 |
+| 132 | 699 |
+| 133 | 509 |
+
+## Representations and preprocessing
+
+The aligned source files are `WOS46985/X.txt`, `Y.txt`, `YL1.txt` and `YL2.txt` in `WebOfScience.zip`. Stable IDs are `wos46985_<six-digit-zero-based-source-row>`. `raw_text` is the complete source `X.txt` line without its line terminator. There is no title field.
+
+Both UTF-8 CSV files have exactly `id,raw_text,vw_text` columns and lexicographically ordered IDs. The VW string begins with the document ID and contains only the `|@word` namespace; labels are separate metadata. In `noow`, normalized token occurrences retain their order. In `bow`, the same tokens are represented as `token:count` with lexicographically sorted tokens. Neither representation truncates the prepared token sequence.
+
+NOOW means occurrence order after normalization, not original or reversible prose. English preprocessing uses Python 3.11.15, NLTK 3.9.2 and NLTK-data commit `550b6625bcef1f2abff2ff770a5a0d272c9c6b2a`: WordPunctTokenizer, lowercase, lowercased input token length at least 2 before POS tagging and lemmatization, `averaged_perceptron_tagger_eng`, WordNet POS mapping J/V/N/R with noun fallback, English stopword removal and `lemma.isalpha()` filtering. Output lemmas are not filtered by length and can contain one character. Names are not masked. Near-duplicate shingles use lowercased WordPunct body tokens of length at least two containing letters only.
+
+## Loading and metadata
+
+The default configuration is `noow`; the alternative is `bow`. Both expose the full retained population as `corpus`:
+
+```python
+from datasets import load_dataset
+
+repo = "TopicNet/WOS-46985"
+corpus = load_dataset(repo, "noow", split="corpus")
+bow = load_dataset(repo, "bow", split="corpus")
+```
+
+Obtain the complete local package, including actual Git LFS contents. Set `HF_DATASETS_OFFLINE=1` and `HF_HUB_OFFLINE=1` before Python starts for offline loading, and choose a writable cache directory:
+
+```python
+import json
+from pathlib import Path
+from datasets import load_dataset
+
+package = Path("/path/to/WOS-46985")
+corpus = load_dataset(str(package), "noow", split="corpus",
+                      cache_dir="/writable/cache")
+bow = load_dataset(str(package), "bow", split="corpus",
+                   cache_dir="/writable/cache")
+labels = json.loads((package / "data/labels.json").read_text(encoding="utf-8"))
+splits = json.loads((package / "data/splits.json").read_text(encoding="utf-8"))
+hierarchy = json.loads((package / "data/hierarchy.json").read_text(encoding="utf-8"))
+membership = {doc_id: split for split, ids in splits.items() for doc_id in ids}
+annotated = corpus.map(lambda row: {"labels": labels[row["id"]],
+                                   "source_hierarchy": hierarchy[row["id"]],
+                                   "research_split": membership[row["id"]]})
+fit = annotated.filter(lambda row: row["research_split"] == "fit")
+```
+
+`labels.json` directly maps each ID to `[Y]`, a list containing one string class. `splits.json` directly maps `fit`, `calibration` and `validation` to ordered ID arrays. `hierarchy.json` directly maps each ID to `{"Y": "...", "YL1": "...", "YL2": "..."}`, preserving the three source codes as strings. Join all metadata by ID, not by JSON position.
+
+For remote metadata, download each auxiliary file and use the same ID joins:
+
+```python
+import json
+from huggingface_hub import hf_hub_download
+
+metadata = {}
+for name in ("labels", "splits", "hierarchy"):
+    path = hf_hub_download(repo, f"data/{name}.json", repo_type="dataset")
+    with open(path, encoding="utf-8") as handle:
+        metadata[name] = json.load(handle)
+```
+
+## Related datasets and overlap
+
+Related preparations are [TopicNet/WOS-5736](https://huggingface.co/datasets/TopicNet/WOS-5736), [TopicNet/WOS-11967](https://huggingface.co/datasets/TopicNet/WOS-11967) and [TopicNet/WOS-46985](https://huggingface.co/datasets/TopicNet/WOS-46985). They are not independent datasets. Comparing the original source abstracts after casefold and whitespace collapse gives 5,736 shared abstract identities between WOS-5736 and WOS-46985, 11,268 between WOS-11967 and WOS-46985, and 1,708 between WOS-5736 and WOS-11967. These counts describe source abstracts, not final retained populations. WOS-11967 is not entirely contained in WOS-46985 under this comparison. Research splits are generated independently for each preparation; combining subsets for training and evaluation can therefore leak shared documents across splits.
+
+## Files and integrity
+
+The package contains `.gitattributes`, this card, [SHA256SUMS](SHA256SUMS), the verbatim [SOURCE_README.txt](SOURCE_README.txt), `data/noow.csv`, `data/bow.csv`, [labels](data/labels.json), [splits](data/splits.json) and [hierarchy](data/hierarchy.json). Run `sha256sum -c SHA256SUMS` from the package root to check every publication file except the checksum list itself. Git internals are excluded from the list.
+
+## Rights, limitations and citation
+
+The [official version 6 release](https://data.mendeley.com/datasets/9rw3vkcfy4/6) declares CC BY 4.0. [SOURCE_README.txt](SOURCE_README.txt) is preserved verbatim, including Copyright (c) 2017 Kamran Kowsari, the permission notice and the request to cite HDLTex. Preserve the copyright and permission notices in copies or substantial portions and cite the source. The release license does not establish that all abstracts or other third-party content are free of additional rights.
+
+Source selection, historical coverage and class imbalance limit generalization. Source hierarchy codes are preserved without inferred corrections. Grouping limits known duplicate leakage but does not establish that every semantic duplicate was detected. Normalization changes text and does not guarantee current facts, absence of personal information or suitability for downstream decisions.
+
+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*](https://arxiv.org/abs/1709.08267), IEEE ICMLA. DOI: [10.1109/ICMLA.2017.0-134](https://doi.org/10.1109/ICMLA.2017.0-134).
+
+When citing this preparation, identify its retained population and repository revision as well as the source publication.

SHA256SUMS

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+e888c4293f207a1abe0e1e08b736561fed5829c4325d0618e2e7f9c9ff329fa7  README.md
+6da4ab1e624c4ef61c46600d727770f873ff4db3c15541fb481e7af8a715c0ec  SOURCE_README.txt
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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}
+}

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