README.md

---
license: cc-by-sa-4.0
task_categories:
- text-generation
- token-classification
- feature-extraction
language:
- en
size_categories:
- 1M<n<10M
pretty_name: 'Stanza-Wikitext-2: A Structurally Enriched Modernization of WikiText-2'
tags:
- dependency-parsing
- universal-dependencies
- nlp-dataset
- structural-linguistics
- named-entity-recognition
- wikipedia
---

# Dataset Card for Stanza-Wikitext-2

## Dataset Description

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-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:** 10.57967/hf/8060
- **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 removed by Phase 4c integrity repair: **8** (train split only)

---

## Structural Characterization

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) 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 |
|-----------|--------|
| 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** with a strongly right-skewed frequency distribution.

| 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. 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

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). 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-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

| 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

### 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`

### Phase 2: Degradation Audit

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 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 (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 1.11.1 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:

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

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

`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

```python
import pandas as pd

# Load a split
df = pd.read_parquet("hf://datasets/EXOROBOURII/Stanza-Wikitext-2/wiki.train.enriched.parquet")

# 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` | 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 |
| `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-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}
}
```

---

## License

CC-BY-SA-4.0. Derivative of WikiText-2 (CC-BY-SA-4.0, Merity et al. 2016).