FineWeb2-HQ-QA (Raw Release)

Notice: This repository contains the unfiltered raw generation of FineWeb2-HQ-QA across all 20 languages. The filtering pipelines and curation code used for experiments will be released separately soon.

FineWeb2-HQ-QA is a large-scale multilingual synthetic dataset spanning 188.8 million web documents and their generated question-and-answer pairs. Unlike traditional multilingual synthetic data created by translating English source data, FineWeb2-HQ-QA generates QA pairs directly in the target language grounded in native web documents sampled from FineWeb2-HQ.

We are releasing the raw, unfiltered data so researchers can experiment with custom filtering heuristics, tailor pipelines to specific language scripts, or refine individual QA pairs directly.

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Dataset Summary

• Type: Raw / Unfiltered Generations
• Input Document Count: 188,826,048 documents
• Languages: 20 languages
• Generator Model: Qwen/Qwen3-30B-A3B (prompted with reasoning enabled; reasoning traces removed in final sample concatenation)
• Format: Parquet files partitioned by language configuration

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Raw Document Counts by Language

The numbers below reflect the raw input pool provided in this repository before applying the paper's filtering pipeline:

Configuration (GlotLID)	Language	Raw Documents
arb_Arab	Arabic	3,353,745
ces_Latn	Czech	2,452,481
cmn_Hani	Mandarin Chinese	26,076,021
dan_Latn	Danish	3,021,637
deu_Latn	German	20,759,205
ell_Grek	Greek	1,619,179
fas_Arab	Persian	1,984,552
fra_Latn	French	15,973,025
hun_Latn	Hungarian	2,166,540
ind_Latn	Indonesian	3,378,722
ita_Latn	Italian	10,195,425
jpn_Jpan	Japanese	15,043,155
nld_Latn	Dutch	6,680,086
pol_Latn	Polish	6,640,453
por_Latn	Portuguese	7,848,376
rus_Cyrl	Russian	34,829,012
spa_Latn	Spanish	17,346,026
swe_Latn	Swedish	3,306,324
tur_Latn	Turkish	2,796,347
vie_Latn	Vietnamese	3,355,737
Total		188,826,048

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Important Usage Notes (Raw Data)

Because this is the unfiltered release, users should be aware that the raw pool contains:

1. Incomplete Generations: A small subset (~1.87%) where generation cut off or the </think> closing tag was incomplete.
2. Potential Language Spills: Cases (~1.24%) where generation defaulted to English or another language instead of the requested target language.
3. Repetition Artifacts: Repetitive questions or n-gram loops (most pronounced in Japanese generations).
4. Benchmark Contamination: Potential n-gram overlaps with standard multilingual benchmarks (GlobalMMLU, INCLUDE, XNLI, etc.).

If you plan to use this dataset directly for pre-training, we recommend applying language identification (e.g., GlotLID) and Gopher-style repetition filters prior to training.

Additionally, we recommend running additional filtering for sensitive content, PII, and repetitions before pre-training. See the FineWeb2 documentation for discussions on biases, limitations, and societal impact.

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Schema

root
 |-- text: string (nullable = true)
 |-- id: string (nullable = true)
 |-- metadata: struct (nullable = true)
 |    |-- context_text: string (nullable = true)
 |    |-- file_path: string (nullable = true)
 |    |-- url: string (nullable = true)
 |    |-- raw_llm_output: string (nullable = true)
 |    |-- questions: string (nullable = true)
 |    |-- source_metadata_json: string (nullable = true)

• text: Primary document text (matches context_text).
• id: Unique document ID.
• metadata.context_text: Input web document used as context for generation.
• metadata.questions: Generated QA pairs for the document.
• metadata.raw_llm_output: Unprocessed model generation string.
• metadata.url: Source web URL.
• metadata.source_metadata_json: Original FineWeb2-HQ metadata serialized as JSON.

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Quickstart

from datasets import load_dataset

# Stream a single language
ds = load_dataset(
    "epfml/FineWeb2-HQ-QA",
    data_files="data/deu_Latn/*.parquet",
    split="train",
    streaming=True,
)

sample = next(iter(ds))
print(sample["id"])
print(sample["metadata"]["questions"])

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Dataset origin

Being a subset of FineWeb2, this data covers websites over the 2013-2024 time period.

FineWeb2 is sourced from the internet at large, it is very likely that some personable identifiable information (PII) will be present, even if the FineWeb2 processing has already anonymized email addresses and public IP addresses. If you find your own PII and would like it removed, please fill out the FineWeb2 PII removal/opt out form.

CommonCrawl respects robots.txt at crawl time, but if you are a webmaster and find your website in FineWeb2 and would like to have it removed, you may also use the FineWeb2 PII removal/opt out form.

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License

The synthetic annotations and dataset curation are released under the Open Data Commons Attribution License (ODC-By v1.0). Underlying web text remains subject to the original source web terms and Common Crawl's Terms of Use.