Update README.md

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README.md

@@ -8,67 +8,279 @@ language:
 - en
 size_categories:
 - 100K<n<1M
-pretty_name: '​Stanza-2: Geometry-Aware WikiText'
+pretty_name: 'Stanza-2: Geometry-Aware WikiText'
+tags:
+- dependency-parsing
+- universal-dependencies
+- nlp-dataset
+- structural-linguistics
+- named-entity-recognition
+- wikipedia
 ---
+
 # Dataset Card for Stanza-2
 
 ## Dataset Description
-Stanza-2 is a structurally pristine, mathematically verified NLP dataset designed specifically for multi-task language modeling, custom tokenizer training, and mechanistic interpretability research. 
 
-It is a rigorously modernized and annotated derivative of the `wikitext-2-raw-v1` corpus. By utilizing the Stanford NLP `Stanza` pipeline, every word in the corpus has been explicitly mapped to its grammatical, syntactic, and semantic function. Crucially, Stanza-2 preserves document geometry, explicitly labeling Markdown headers to support structure-aware neural architectures.
+Stanza-2 is a structurally pristine, mathematically verified NLP dataset designed for multi-task language modeling, custom tokenizer training, structural NLP research, and mechanistic interpretability work.
+
+It is a rigorously modernized and annotated derivative of the `wikitext-2-raw-v1` corpus. Using the Stanford NLP `Stanza` neural pipeline, every token in the corpus has been explicitly mapped to its grammatical, syntactic, and semantic function across seven aligned annotation layers. Stanza-2 preserves document geometry, explicitly labeling Markdown headers to support structure-aware neural architectures.
 
 - **Curated by:** Jonathan R. Belanger (Exorobourii LLC)
 - **Language:** English (`en`)
 - **License:** CC-BY-SA-4.0
-- **Total Rows:** 101,455 sentences (~2.46 Million Tokens)
+- **DOI:** Locked at publication
+- **Total Sentences:** 101,455 (across all splits)
+- **Total Tokens:** 2,469,912
+
+---
+
+## Corpus Statistics
+
+| Split | Sentences | Tokens |
+|-------|-----------|--------|
+| Train | 82,760 | 2,021,438 |
+| Validation | 8,622 | 210,732 |
+| Test | 10,073 | 237,742 |
+| **Total** | **101,455** | **2,469,912** |
+
+Rows discarded by degradation filter: **8** (out of ~101,463 pre-filter)
+
+---
+
+## Structural Characterization
+
+Unlike standard text corpora, Stanza-2 ships with a full quantitative geometric characterization derived from its dependency structure. These figures are provided to assist researchers in assessing corpus suitability before use.
+
+### Dependency Degree Distribution
+
+Dependency degree (number of dependents per token) follows a power-law distribution with exponent **α = −1.06**. The corpus is heavily left-concentrated — the majority of tokens are leaves.
+
+| Percentile | Degree |
+|-----------|--------|
+| 50th (median) | 0 |
+| 90th | 3 |
+| 99th | 6 |
+| 99.9th | 9 |
+| Maximum | 43 |
+
+- Degree entropy: **1.839 bits**
+- Effective degree vocabulary: degree 0–11 (values above 12 are sparse artifacts of list coordination)
+
+### Token Depth Distribution
+
+Token depth (distance from dependency root, measured upward) characterizes positional distribution within the tree.
+
+| Metric | Value |
+|--------|-------|
+| Range | 0 – 25 |
+| Mean | 2.745 |
+| Std | 1.674 |
+| Entropy | 2.679 bits |
+
+Mean subtree height (measured downward from each node): **5.45 nodes**. Maximum subtree height: **26 nodes**. Root center of mass: **0.24**.
+
+### Structural Grammar Matrix
+
+The cross-product of UPOS tags and DepRel labels yields **451 unique UPOS×DepRel combinations** observed across the corpus. This matrix constitutes a compact geometric fingerprint of the corpus's syntactic behavior and is available as `structural_grammar_matrix.csv` in the associated reports.
+
+### Geometric Motif Analysis
+
+A dependency motif is defined as a parent node (UPOS×DepRel) paired with a sorted tuple of its children's (UPOS×DepRel) labels. The train split contains **106,057 unique motifs** following a power-law frequency distribution.
+
+| Coverage | Motifs Required |
+|----------|----------------|
+| 50% | 343 |
+| 80% | ~3,500 |
+| 90% | ~12,000 |
+| 95% | ~30,000 |
+| 100% | 106,057 |
+
+The top 343 motifs account for half of all motif occurrences — a Zipfian concentration consistent with the structural redundancy hypothesis underlying geometry-aware tokenizer design.
+
+### Structural Rigidity by UPOS
+
+Dependency degree varies substantially by part-of-speech, reflecting syntactic valency differences. VERB is the highest-degree head class; functional categories cluster near zero.
+
+| UPOS | Mean Degree | Max Degree | Entropy (bits) |
+|------|-------------|------------|----------------|
+| VERB | 3.54 | 15 | 2.88 |
+| NOUN | 2.22 | 36 | 2.61 |
+| PROPN | 1.32 | 43 | 2.27 |
+| ADJ | 0.56 | 17 | 1.32 |
+| ADV | 0.25 | 9 | 0.89 |
+| AUX | 0.02 | 8 | 0.11 |
+| DET | 0.02 | 10 | 0.11 |
+| PUNCT | 0.006 | 11 | 0.03 |
+| PART | 0.005 | 6 | 0.03 |
+
+### Structural Information Content
+
+Normalized mutual information between structural measurements and linguistic labels:
+
+| Pair | NMI |
+|------|-----|
+| Degree × UPOS | 0.223 |
+| Degree × DepRel | 0.294 |
+| Depth × UPOS | 0.054 |
+| Depth × DepRel | 0.118 |
+
+Degree carries substantially more linguistic signal than depth. Neither measurement is redundant with linguistic category — they capture geometrically distinct aspects of syntactic structure.
+
+### Per-Sentence Structural Complexity
+
+Per-sentence degree entropy has mean **1.555 bits** (std 0.275, max 1.954 bits). The tight distribution indicates consistent structural complexity across sentences, with limited register variance attributable to the Wikipedia source.
+
+---
 
 ## Dataset Structure
-Stanza-2 abandons flat-text formatting in favor of **Parallel Arrays**. Each row in the dataset represents a single sentence. The linguistic features of that sentence are stored in perfectly aligned, equal-length arrays, guaranteeing 1:1 token-to-tag mapping.
+
+Stanza-2 uses **Parallel Arrays**. Each row represents a single sentence. All linguistic features are stored in co-indexed, equal-length arrays guaranteeing 1:1 token-to-annotation alignment.
 
 ### Schema
-* `chunk_id` (int64): The positional ID of the chunk within the document stream.
-* `sentence_id` (int64): The positional ID of the sentence within its chunk.
-* `raw_text` (string): The cleaned, normalized raw string of the sentence.
-* `is_header` (bool): `True` if the sentence is a structural document header.
-* `section_level` (int64): The Markdown depth of the header (1-6). `0` if not a header.
-* `tokens` (list[str]): The tokenized string sequence.
-* `lemmas` (list[str]): The base morphological root of each token.
-* `upos` (list[str]): Universal Part-of-Speech tags.
-* `xpos` (list[str]): Treebank-specific Part-of-Speech tags.
-* `head` (list[int64]): The 1-based index of the syntactic parent (Dependency Graph).
-* `deprel` (list[str]): The syntactic dependency relation to the head token.
-* `ner` (list[str]): Named Entity Recognition tags in explicit BIOES format.
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `chunk_id` | int64 | Positional ID of the text block within the document stream |
+| `sentence_id` | int64 | Positional ID of the sentence within its chunk |
+| `raw_text` | string | Cleaned, normalized sentence text |
+| `is_header` | bool | `True` if the sentence is a structural document header |
+| `section_level` | int64 | Markdown header depth (1–6); `0` if not a header |
+| `tokens` | list[str] | Surface word forms |
+| `lemmas` | list[str] | Morphological base forms |
+| `upos` | list[str] | Universal POS tags (17-class UD tagset) |
+| `xpos` | list[str] | Penn Treebank POS tags |
+| `head` | list[int64] | 1-indexed syntactic head positions (0 = root anchor) |
+| `deprel` | list[str] | Universal Dependencies relation labels |
+| `ner` | list[str] | Named entity tags in BIOES format |
+
+All array columns are co-indexed: `column[i]` refers to the same token across all columns for a given row.
+
+---
 
 ## Methodology & Provenance
 
-### 1. Cryptographic Ingestion
+### Phase 1: Cryptographic Ingestion
+
 To prevent silent upstream updates from compromising downstream reproducibility, this dataset was built from a cryptographically verified snapshot of the `ggml-org/ci` raw mirror.
-* **Source Archive:** `wikitext-2-raw-v1.zip`
-* **SHA-256 Checksum:** `ef7edb566e3e2b2d31b29c1fdb0c89a4cc683597484c3dc2517919c615435a11`
 
-### 2. The Normalization Ledger
-The legacy WikiText corpus contains archaic spacing and tokenization artifacts. Prior to semantic enrichment, the text underwent strict, idempotent modernization passes to ensure sub-word tokenizers are not biased by historical formatting:
-* **Hyphenation:** Legacy `@-@` artifacts were strictly mapped to standard hyphens (`-`).
-* **Punctuation Alignment:** Floating terminal punctuation (e.g., `word , word`) and floating brackets were realigned to their preceding/succeeding semantic tokens. *Note: Vectorized backreferences were routed through standard Python CPU processing to bypass known `libcudf` regex injection vulnerabilities.*
-* **Structural Preservation:** Legacy `= Header =` formats were mapped to standard Markdown (`# Header`) using strict descending-order regex (H6 down to H1) to prevent partial matching and preserve true document hierarchy.
+- **Source Archive:** `wikitext-2-raw-v1.zip`
+- **SHA-256 Checksum:** `ef7edb566e3e2b2d31b29c1fdb0c89a4cc683597484c3dc2517919c615435a11`
+
+### Phase 2: Degradation Filtering
+
+WikiText-2's unknown token substitution (`<unk>`) is non-uniform. A penalized degradation score is computed per text block:
+
+```
+D*(P) = (|unk| / N) · log₂(1 + √N)
+```
+
+The logarithmic penalty prevents discrimination against longer passages with isolated `<unk>` tokens. The discard threshold is set at μ + 2σ over the distribution of affected blocks. **8 rows were discarded** under this criterion.
+
+### Phase 3: GPU-Accelerated Normalization
+
+Text normalization was performed using NVIDIA RAPIDS cuDF on an L4 GPU. Four operations applied in sequence:
 
-### 3. Graph Integrity Protocol
-Following the Stanza `depparse` enrichment, the resulting Parquet files were subjected to a microscopic mathematical audit. The Stanza-2 dataset guarantees 100% structural integrity:
-1. **Dimensional Symmetry:** Every parallel array (`tokens`, `upos`, `ner`, etc.) within a row is guaranteed to be the exact same length.
-2. **Root Singularity:** Every sentence possesses exactly one dependency root (`head == 0`).
-3. **Graph Bounds:** No dependency head points to an index outside the bounds of the sentence. 
-*Note: During the final Phase 4b integrity audit, 8 sentences out of ~101,463 across the training split violated graph bounds or root singularity due to extreme source fragmentation. These 8 rows were surgically dropped to preserve absolute dataset-wide mathematical validity.*
+1. **Whitespace normalization:** leading/trailing whitespace stripped
+2. **Hyphen modernization:** legacy `@-@` artifacts collapsed to standard hyphens
+3. **Punctuation normalization:** floating punctuation corrected via CPU bypass using Python `re` with backreferences *(cuDF vectorized backreferences routed through standard Python to bypass known libcudf regex injection vulnerabilities)*
+4. **Header normalization:** `= Title =` through `====== Title ======` converted to Markdown H1–H6 in strict descending order to preserve document hierarchy
 
-### 4. Structural Grammar Baseline
-Analysis of the `wiki.train` split reveals exactly 451 unique `(UPOS, DepRel)` structural combinations across ~2.46 million tokens, demonstrating a highly rigid grammatical scaffold suitable for entropy reduction in custom tokenizer design.
+### Phase 4: Stanza NLP Enrichment
+
+Stanza 1.x initialized with `tokenize, pos, lemma, depparse, ner` on GPU. Output serialized to Parquet with ZSTD compression (level 3).
+
+Following enrichment, all Parquet files were subjected to a microscopic integrity audit guaranteeing:
+
+1. **Dimensional symmetry:** all parallel arrays within a row are equal length
+2. **Root singularity:** every sentence has exactly one dependency root (`head == 0`)
+3. **Graph bounds:** no head index points outside the sentence boundary
+
+The Stanza-2 dataset is **100% structurally valid** across all splits.
+
+### Phase 5: Structural Metadata Injection
+
+`is_header` and `section_level` columns injected via vectorized Markdown header detection. Enables structure-aware models to condition on document position without reprocessing raw text.
+
+---
 
 ## Usage
-Because the dataset uses PyArrow-backed lists for parallel arrays, loading it into standard ML pipelines is highly efficient:
 
 ```python
 import pandas as pd
+
+# Load a split
 df = pd.read_parquet("hf://datasets/EXOROBOURII/Stanza-Wikitext-2/wiki.train.enriched.parquet")
 
-# Example: Accessing perfectly aligned tokens and their dependency relations
-first_sentence_tokens = df.iloc[0]['tokens']
-first_sentence_relations = df.iloc[0]['deprel']
+# Aligned token access
+sentence = df.iloc[0]
+for token, upos, deprel, head in zip(
+    sentence['tokens'],
+    sentence['upos'],
+    sentence['deprel'],
+    sentence['head']
+):
+    print(f"{token:<20} {upos:<8} {deprel:<16} head={head}")
+
+# Filter to content sentences only (exclude headers)
+content = df[~df['is_header']].reset_index(drop=True)
+
+# Filter to a specific section level
+h2_headers = df[df['section_level'] == 2]
+
+# Reconstruct dependency tree for a sentence
+from collections import defaultdict
+
+def get_children(head_array):
+    children = defaultdict(list)
+    for i, h in enumerate(head_array):
+        if h > 0:
+            children[h - 1].append(i)  # convert to 0-indexed
+    return children
+
+row = df.iloc[10]
+children = get_children(row['head'])
+root_idx  = list(row['head']).index(0)
+print(f"Root token: {row['tokens'][root_idx]} ({row['upos'][root_idx]})")
+print(f"Root dependents: {[row['tokens'][c] for c in children[root_idx]]}")
+```
+
+---
+
+## Reports and Analysis Artifacts
+
+The following analytical reports are available in the dataset repository:
+
+| File | Description |
+|------|-------------|
+| `structural_grammar_matrix.csv` | 451 UPOS×DepRel combinations with frequencies |
+| `geometric_motifs_wiki.train.enriched.csv` | 106,057 unique dependency motifs |
+| `entity_distribution.csv` | Named entity frequencies and types |
+| `entity_cooccurrence.csv` | Sentence-level entity co-occurrence pairs |
+| `motif_analytics_summary.txt` | Power-law analysis and valency statistics |
+| `structural_rigidity_full.csv` | Per-UPOS weighted valency statistics |
+| `degree_distribution.csv` | Full token degree frequency table |
+| `depth_distribution.csv` | Full token depth frequency table |
+| `mi_summary.csv` | NMI values for degree/depth × UPOS/DepRel |
+| `sentence_structural_stats.csv` | Per-sentence degree and depth statistics |
+
+---
+
+## Citation
+
+```bibtex
+@dataset{belanger2025stanza2,
+  author    = {Belanger, Jonathan R.},
+  title     = {Stanza-2: A Structurally Enriched Modernization of WikiText-2},
+  year      = {2025},
+  publisher = {HuggingFace},
+  url       = {https://huggingface.co/datasets/EXOROBOURII/Stanza-Wikitext-2},
+  doi       = {[10.57967/hf/8060]}
+}
+```
+
+---
+
+## License
+
+CC-BY-SA-4.0. Derivative of WikiText-2 (CC-BY-SA-4.0, Merity et al. 2016).