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FineWeb-Edu Curated

A curated subset of FineWeb-Edu optimised for training language models with strong STEM and structured-reasoning capabilities.

Dataset Summary

  • Total documents: 3,656,053
  • Total tokens: 4.3B
  • Source: FineWeb-Edu sample-100BT
  • Curation method: Multi-label topic classification + complexity scoring + distribution-aware sampling + MinHash deduplication

Topic Distribution

Documents are classified into 11 target groups using a 17-label multi-label classifier (ModernBERT-base, sigmoid threshold=0.3). STEM-core topics (Mathematics, Computer Science, ML/AI) are boosted relative to their natural distribution.

Group Target % Actual % Tokens Docs
Mathematics 7.0% 14.7% 632,372,399 642,275
Computer Science 8.0% 16.9% 730,556,604 704,479
ML/AI 5.0% 4.4% 188,754,622 168,769
Physical Sciences 4.0% 14.2% 612,268,635 525,241
Life Sciences 3.0% 21.9% 947,146,778 810,167
Engineering/Tech 5.0% 24.3% 1,047,041,226 925,919
Environmental Sci 2.0% 20.4% 878,638,307 782,712
Medicine/Health 4.0% 21.4% 921,433,959 804,158
Business/Economics 4.0% 24.3% 1,048,966,632 837,069
Law/Government 3.0% 38.0% 1,640,637,230 1,232,236
General Knowledge 55.0% 92.7% 4,001,290,512 3,338,895

Complexity Distribution

Reasoning complexity scored by a ModernBERT-base regression model (1.0-4.0 scale). Mean complexity: 2.77, Median: 2.91.

Level Range Target % Actual % Docs
L1 [1.0, 1.75) 10.0% 14.3% 523,809
L2 [1.75, 2.5) 20.0% 22.1% 809,032
L3 [2.5, 3.25) 40.0% 39.6% 1,446,264
L4 [3.25, 4.0) 30.0% 24.0% 876,948

Multi-Label Statistics

  • Mean labels per document: 4.0
  • Documents with 2+ labels: 99.5%
  • Documents with 3+ labels: 90.7%

Token Count Distribution

Percentile Tokens
P10 265
P25 439
P50 (median) 738
P75 1,260
P90 2,221
Mean 1,180

Top Domains

Domain Tokens %
en.wikipedia.org 65,706,303 1.52%
en.m.wikipedia.org 10,121,881 0.23%
slideplayer.com 8,915,082 0.21%
www.encyclopedia.com 8,112,424 0.19%
journals.plos.org 6,990,412 0.16%
en.wikisource.org 6,889,207 0.16%
www.nap.edu 6,673,714 0.15%
www.newworldencyclopedia.org 6,555,558 0.15%
www.reference.com 6,136,223 0.14%
link.springer.com 5,912,585 0.14%

Schema

Each row contains:

Field Type Description
text string Document text
url string Source URL
token_count int Token count
dump string Common Crawl dump identifier
topic_scores list[float] 17-dim sigmoid scores from topic classifier
complexity float Reasoning complexity score (1.0-4.0)
assigned_groups list[string] Target groups at threshold=0.3
relevance_score float Composite relevance score used for sampling

Methodology

  1. Classification: Both classifiers (topic: 17-label multi-label, complexity: regression) run on each document. Full sigmoid vectors stored for maximum flexibility.
  2. Filtering: Priority-based sampling targeting STEM-core topics first, with complexity distribution targets within each group. Multi-label documents count toward multiple quotas.
  3. Deduplication: MinHash LSH (128 perms, 13-gram word shingles, 0.7 Jaccard threshold). Highest-relevance document kept from each cluster.
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