Update README.md

README.md

@@ -8,7 +8,7 @@ language:
 - en
 size_categories:
 - 100K<n<1M
-pretty_name: 'Stanza-2: Geometry-Aware WikiText'
+pretty_name: 'Stanza-Wikitext-2: A Structurally Enriched Modernization of WikiText-2'
 tags:
 - dependency-parsing
 - universal-dependencies
@@ -18,18 +18,18 @@ tags:
 - wikipedia
 ---
 
-# Dataset Card for Stanza-2
+# Dataset Card for Stanza-Wikitext-2
 
 ## Dataset Description
 
-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.
+Stanza-Wikitext-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.
+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-Wikitext-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
-- **DOI:** Locked at publication
+- **DOI:** 10.57967/hf/8060
 - **Total Sentences:** 101,455 (across all splits)
 - **Total Tokens:** 2,469,912
 
@@ -44,17 +44,17 @@ It is a rigorously modernized and annotated derivative of the `wikitext-2-raw-v1
 | Test | 10,073 | 237,742 |
 | **Total** | **101,455** | **2,469,912** |
 
-Rows discarded by degradation filter: **8** (out of ~101,463 pre-filter)
+Rows removed by Phase 4c integrity repair: **8** (train split only)
 
 ---
 
 ## 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.
+Unlike standard text corpora, Stanza-Wikitext-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.
+Dependency degree (number of dependents per token) is strongly right-skewed with faster-than-power-law decay. A KS-based MLE scan (Clauset et al., 2009) found no well-supported power-law regime across the observable degree range. The corpus is heavily left-concentrated — the majority of tokens are leaves.
 
 | Percentile | Degree |
 |-----------|--------|
@@ -86,17 +86,17 @@ The cross-product of UPOS tags and DepRel labels yields **451 unique UPOS×DepRe
 
 ### 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.
+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** with a strongly right-skewed frequency distribution.
 
-| Coverage | Motifs Required |
-|----------|----------------|
-| 50% | 343 |
-| 80% | ~3,500 |
-| 90% | ~12,000 |
-| 95% | ~30,000 |
-| 100% | 106,057 |
+| Coverage | Motifs Required | % of Total Motifs |
+|----------|----------------|-------------------|
+| 50% | 343 | 0.32% |
+| 80% | 7,743 | 7.30% |
+| 90% | 33,080 | 31.19% |
+| 95% | 69,571 | 65.60% |
+| 100% | 106,057 | 100% |
 
-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.
+The top 343 motifs account for half of all motif occurrences. The distribution is heavily long-tailed: 95% coverage requires 65.6% of the full motif vocabulary, indicating a compact high-frequency structural core alongside a large population of rare configurations.
 
 ### Structural Rigidity by UPOS
 
@@ -129,13 +129,13 @@ Degree carries substantially more linguistic signal than depth. Neither measurem
 
 ### 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.
+Per-sentence degree entropy has mean **1.555 bits** (std 0.275, max 1.954 bits). Structural complexity means are stable across all three splits, confirming that the canonical WikiText-2 split boundaries do not introduce distributional artifacts.
 
 ---
 
 ## Dataset Structure
 
-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.
+Stanza-Wikitext-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
 
@@ -167,28 +167,28 @@ To prevent silent upstream updates from compromising downstream reproducibility,
 - **Source Archive:** `wikitext-2-raw-v1.zip`
 - **SHA-256 Checksum:** `ef7edb566e3e2b2d31b29c1fdb0c89a4cc683597484c3dc2517919c615435a11`
 
-### Phase 2: Degradation Filtering
+### Phase 2: Degradation Audit
 
-WikiText-2's unknown token substitution (`<unk>`) is non-uniform. A penalized degradation score is computed per text block:
+WikiText-2 is distributed in two variants: a pre-tokenized `.tokens` format in which low-frequency terms are replaced with `<unk>` substitution tokens, and a `.raw` format retaining original surface forms. This pipeline operates on the `.raw` files exclusively. A precautionary contamination audit computed a penalized degradation score 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.
+The audit confirmed zero `<unk>` tokens across all 23,767 text blocks, returning a clean result. No filtering was applied or required. This validates the source file selection: by operating on `.raw` rather than `.tokens`, the pipeline inherits no vocabulary substitution artifacts, and downstream analyses reflect genuine surface token distributions.
 
 ### Phase 3: GPU-Accelerated Normalization
 
 Text normalization was performed using NVIDIA RAPIDS cuDF on an L4 GPU. Four operations applied in sequence:
 
 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)*
+2. **Hyphen modernization:** legacy `@-@` artifacts collapsed to standard hyphens (e.g. `Apollo @-@ Soyuz` → `Apollo-Soyuz`)
+3. **Punctuation normalization:** floating punctuation corrected via CPU bypass using Python `re` with backreferences (e.g. `word ,` → `word,`)
 4. **Header normalization:** `= Title =` through `====== Title ======` converted to Markdown H1–H6 in strict descending order to preserve document hierarchy
 
 ### 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).
+Stanza 1.11.1 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:
 
@@ -196,7 +196,7 @@ Following enrichment, all Parquet files were subjected to a microscopic integrit
 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.
+8 structurally invalid sentences were identified in the train split and removed via automated ledger repair. The Stanza-Wikitext-2 dataset is **100% structurally valid** across all splits.
 
 ### Phase 5: Structural Metadata Injection
 
@@ -257,7 +257,7 @@ The following analytical reports are available in the dataset repository:
 | `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 |
+| `motif_analytics_summary.txt` | Motif coverage 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 |
@@ -271,11 +271,11 @@ The following analytical reports are available in the dataset repository:
 ```bibtex
 @dataset{belanger2025stanza2,
   author    = {Belanger, Jonathan R.},
-  title     = {Stanza-2: A Structurally Enriched Modernization of WikiText-2},
+  title     = {Stanza-Wikitext-2: A Structurally Enriched Modernization of WikiText-2},
   year      = {2026},
   publisher = {HuggingFace},
   url       = {https://huggingface.co/datasets/EXOROBOURII/Stanza-Wikitext-2},
-  doi       = {[10.57967/hf/8060]}
+  doi       = {10.57967/hf/8060}
 }
 ```