Configs: sample is the default, ge50 renamed count_ge50, drop overlapping all/count_ge5; sort sample by count
bb0c4f5 verified | license: cc-by-nc-4.0 | |
| language: | |
| - en | |
| pretty_name: FineQuestions | |
| size_categories: | |
| - 1B<n<10B | |
| task_categories: | |
| - text-retrieval | |
| - text-classification | |
| - question-answering | |
| tags: | |
| - questions | |
| - intent | |
| - web-as-corpus | |
| - commoncrawl | |
| - fineweb | |
| source_datasets: | |
| - HuggingFaceFW/fineweb | |
| extra_gated_heading: "Access FineQuestions" | |
| extra_gated_description: "FineQuestions is released for non-commercial use under CC BY-NC 4.0. Please read the terms below before downloading." | |
| extra_gated_prompt: >- | |
| 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. | |
| extra_gated_fields: | |
| I agree to use FineQuestions for non-commercial purposes only: checkbox | |
| I agree to follow the FineWeb license and Common Crawl Terms of Use for any source content I retrieve: checkbox | |
| I understand the data is unfiltered web text and may contain offensive or sensitive content: checkbox | |
| extra_gated_button_content: "Agree and access" | |
| configs: | |
| - config_name: sample | |
| default: true | |
| data_files: | |
| - split: train | |
| path: sample/sample.parquet | |
| - config_name: count_ge50 | |
| data_files: | |
| - split: train | |
| path: data/gte50/*.parquet | |
| - config_name: count_10_49 | |
| data_files: | |
| - split: train | |
| path: data/10_49/*.parquet | |
| - config_name: count_5_9 | |
| data_files: | |
| - split: train | |
| path: data/5_9/*.parquet | |
| - config_name: count_3_4 | |
| data_files: | |
| - split: train | |
| path: data/3_4/*.parquet | |
| - config_name: count_1_2 | |
| data_files: | |
| - split: train | |
| path: data/1_2/*.parquet | |
| # 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](https://huggingface.co/datasets/HuggingFaceFW/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 | | |
| ```python | |
| 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: | |
| ```sql | |
| -- 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](https://creativecommons.org/licenses/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](https://huggingface.co/datasets/HuggingFaceFW/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](https://commoncrawl.org/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: | |
| ```bibtex | |
| @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}} | |
| } | |
| ``` | |