| | --- |
| | license: cc-by-sa-4.0 |
| | task_categories: |
| | - text-generation |
| | - token-classification |
| | - feature-extraction |
| | language: |
| | - en |
| | size_categories: |
| | - 100K<n<1M |
| | pretty_name: 'Stanza-2: Geometry-Aware WikiText' |
| | --- |
| | # 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. |
| |
|
| | - **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) |
| |
|
| | ## 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. |
| |
|
| | ### 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. |
| |
|
| | ## Methodology & Provenance |
| |
|
| | ### 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. |
| |
|
| | ### 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.* |
| |
|
| | ### 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. |
| |
|
| | ## 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 |
| | 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'] |
