README.md

metadata

license: apache-2.0
language:
  - en
task_categories:
  - text-generation
tags:
  - wikipedia
  - wikitext
  - quality-scored
  - curriculum-learning
  - character-level
  - slm
size_categories:
  - 1M<n<10M
dataset_info:
  features:
    - name: text
      dtype: string
  splits:
    - name: train
      num_examples: 2592530
    - name: validation
      num_examples: 288060

WikiText-103 Quality-Scored Corpus

Cleaned and quality-scored subset of WikiText-103 (curated Wikipedia Good and Featured articles), prepared for character-level language model training with curriculum learning support.

Dataset Description

This dataset contains cleaned text from WikiText-103, with each batch scored on multiple quality dimensions for curriculum-based training. The text has been lowercased and filtered to an ASCII character set suitable for character-level tokenization.

Source

• Original dataset: Salesforce/wikitext (wikitext-103-v1 split)
• Content: ~29,000 Wikipedia Good and Featured articles covering science, history, literature, philosophy, geography, technology, music, law, mathematics, and more

Cleaning Applied

• Moses-style detokenization (recombined subword artifacts from original WikiText tokenization)
• <unk> token removal (WikiText-103 replaces rare words with <unk>)
• Lowercased to ASCII character set: a-z .,;:?!'"()-
• Gutenberg/boilerplate header/footer stripping
• Whitespace normalization and empty line removal

Quality Scoring

Each batch of ~200 articles is scored using heuristic quality metrics:

Metric	Description
quality_score	Weighted composite (vocab diversity, word diversity, length, repetition)
citation_score	Attribution density from citation signals (references, footnotes, bibliographic patterns)
avg_mtld	Measure of Textual Lexical Diversity (higher = richer vocabulary)
avg_flesch	Flesch Reading Ease (lower = more complex text)
pop_culture_density	Pop culture keyword density (lower = more academic)
academic_density	Academic/scholarly vocabulary density
topic_tags	Detected topic categories per batch

Quality Tiers

Batches are classified into tiers for curriculum scheduling:

Tier	Criteria	Count
Gold	High MTLD (>76), high citation score (>0.35), low pop culture	0
Silver	Moderate MTLD (>70), moderate citations (>0.25)	145
Bronze	Below silver thresholds	0
Excluded	High pop culture density (celebrity bios, reality TV, tabloid content)	0

Note: All batches score as silver because each batch file mixes ~200 diverse articles, diluting both pop-culture and academic signals. For finer-grained tier separation, per-article scoring is recommended.

Dataset Statistics

Split	Examples	Size
Train	2,592,530	448 MB
Validation	288,060	50 MB

• Average quality score: 0.3967
• Deduplication: 2.1% duplicate chunks removed (62,280 of 2,942,872)
• 90/10 train/validation split (shuffled)

Topic Distribution

Topics detected across 145 batch files:

Topic	Batches
Literature	145
History	145
Science	144
Law	72
Technology	49
Music	48
Geography	35
Mathematics	23
Philosophy	22
Economics	21

Additional Files

• wikitext_manifest.jsonl: Per-batch quality scores, tier assignments, topic tags, and metadata. Each line is a JSON object with fields: batch_file, chunk_count, tier, quality_score, citation_score, avg_mtld, avg_flesch, topic_tags, pop_culture_density, academic_density.

Usage

from datasets import load_dataset

ds = load_dataset("LisaMegaWatts/wikitext-103-quality-scored")

# Training data
for example in ds["train"]:
    text = example["text"]

# Load quality manifest for curriculum weighting
import json
manifest = []
with open("wikitext_manifest.jsonl") as f:
    for line in f:
        manifest.append(json.loads(line))

Related Datasets

• LisaMegaWatts/philosophy-corpus - Curated philosophy texts (Aristotle, Plato, Kant, etc.)
• LisaMegaWatts/classical-humanities-corpus - Extended classical humanities collection

License

Apache 2.0 (following WikiText-103 licensing)