FineQuestions is derived from FineWeb (ODC-By 1.0), which is built from Common Crawl. By requesting access you agree to: (1) use FineQuestions only for non-commercial purposes under CC BY-NC 4.0, with attribution to FineQuestions and FineWeb; (2) follow the FineWeb license and the Common Crawl Terms of Use for any page content you retrieve through the released document IDs and URLs; (3) not attempt to identify or contact individuals from the data; and (4) acknowledge that the questions are unfiltered web text that may contain profanity, offensive or explicit content, and personal names.

FineQuestions

1.49 billion unique questions mined from the web, each linked to its source documents in FineWeb: a map of what people actually ask.

If Common Crawl is a memory of the web, FineQuestions is a map of its intentions.

FineQuestions extracts every question from FineWeb (110 CC-MAIN snapshots, 2013–2025), then normalizes and aggregates them. Each question comes with its occurrence count, source pointers and, for questions seen at least 5 times, content categories.

Stage	Count
FineWeb source documents	25.9B
Raw extracted questions	13,396,141,075
Unique normalized questions	1,493,938,805
Occurring ≥5 times, with content categories	599,720,275

Configs

The five count tiers don't overlap and together make up the full dataset. sample is the default.

Config	Occurrence count	Rows	Content categories
`sample` (default)	all tiers, 20K each	100,000	≥5 only
`count_ge50`	≥50	27,375,443	yes
`count_10_49`	10–49	302,933,413	yes
`count_5_9`	5–9	269,411,419	yes
`count_3_4`	3–4	238,021,805	null
`count_1_2`	1–2	656,196,725	null

from datasets import load_dataset
# quick look: 100K rows, 20K random from each tier, one ~100MB file
sample = load_dataset("BodhiumLabs/FineQuestions", "sample", split="train")
# one tier: stream rather than download
ds = load_dataset("BodhiumLabs/FineQuestions", "count_ge50", split="train", streaming=True)
# several tiers at once, e.g. every question seen at least 5 times (599.7M rows, all with categories);
# use "data/*/*.parquet" for all 1.49B
ge5 = load_dataset("BodhiumLabs/FineQuestions", split="train", streaming=True,
                   data_files=["data/gte50/*.parquet", "data/10_49/*.parquet", "data/5_9/*.parquet"])

Schema

Column	Type	Notes
`norm_qn_id`	string	xxhash64 of the normalized question (key)
`question`	string	Normalized question text
`count`	int64	Total occurrences across all 110 snapshots (a page recrawled in several snapshots counts once per snapshot; a question repeated on one page counts each time)
`tier`	string	`gte50` / `10_49` / `5_9` / `3_4` / `1_2`
`samples`	list⟨struct⟩	The 100 most recent occurrences by crawl date: `uuid`, `url`, `host`, `date`, `question` (as written on the page, before normalization), `word_count`, `content_length`
`content_cat1..5`	string	Top-5 content categories (26-category taxonomy). Null when `count` < 5.
`content_conf1..5`	double	Classifier confidence for each category. Null when `count` < 5.

Files are data/<tier>/<shard>.parquet. The 256 shards come from hash-partitioning norm_qn_id, so a given question is always in the same shard number across tiers.

Page content: pointers, not pages

We do not redistribute page text. Each sample's uuid is a FineWeb document ID, so content can be rejoined exactly:

-- DuckDB
SELECT q.question, s.url, fw.text
FROM 'hf://datasets/BodhiumLabs/FineQuestions/data/gte50/00.parquet' q, UNNEST(q.samples) AS t(s)
JOIN 'hf://datasets/HuggingFaceFW/fineweb/data/<CC-MAIN-dump>/*.parquet' fw
  ON fw.id = '<urn:uuid:' || s.uuid || '>'
LIMIT 100;

How it was built

Regex question extraction (sentences ending in ?, 5–100 words, starting with a question word or containing a question keyword; 0.59 questions per document) over FineWeb → normalization (lowercase, punctuation removed, whitespace collapsed) → xxhash64 → partition-then-aggregate map-reduce (256 buckets) keeping each question's total count and 100 most recent occurrences → GLiNER2 zero-shot content classification for questions occurring ≥5 times.

Known limitations (read before using)

Frequency ≠ importance. High-count questions skew toward SEO and boilerplate text, so naive top-N selection surfaces junk. count measures how often a question is written on crawled pages, not how often it is asked.
Extraction quality: on a human-validated sample of the count ≥5 slice, 79% of rows are genuine questions. The count <5 tiers contain more noise.
Content categories are zero-shot labels, not ground truth. On a judged sample, content_cat1 was defensible for 70% of questions. Use them as filters.
samples holds the 100 most recent occurrences, so for popular questions (max count 12.3M) it is a recent slice rather than a full list, and count > len(samples). Rejoin through FineWeb for full page text.
Deduplication is exact-match on normalized text, so paraphrases remain separate rows.
English only. The data inherits FineWeb's crawl and filtering biases.

Content warning

We did not filter questions for content beyond the filtering FineWeb applies. The data includes profanity, offensive and explicit questions, spam, and questions on sensitive subjects (health, religion, politics) at roughly the rates they appear on the web. Filter for your own use case before training or evaluating models.

Personal information

All text is excerpted from FineWeb, which replaced email addresses and public IP addresses with placeholders. A structured-PII scan of FineQuestions found emails, phone numbers and ID-like strings in under 0.05% of rows, mostly false positives such as product codes. Personal names are not redacted, consistent with the source corpus. Questions may contain sensitive content that appeared on public web pages.

Removal requests: email vincent@bodhiumlabs.com with the norm_qn_id or URL.

License

FineQuestions is released under CC BY-NC 4.0: you may use, share and adapt it for non-commercial purposes, with attribution. Commercial use is not permitted.

FineQuestions is derived from FineWeb, which is released under ODC-By 1.0. Page text rejoined from FineWeb remains under FineWeb's terms, and use is also subject to the Common Crawl Terms of Use. Please credit both FineQuestions and FineWeb.

Citation

A technical report describing the pipeline and experiments is coming soon on arXiv. Until then, please cite this dataset page:

@misc{bodhiumlabs2026finequestions,
  title  = {FineQuestions: 1.49 Billion Questions from the Web},
  author = {McCloskey, Vincent and Srinivasan, Krishna},
  year   = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/BodhiumLabs/FineQuestions}}
}

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