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There is no single best web-data source for every AI project. Common Crawl gives maximum breadth and control, while processed collections such as FineWeb, C4, Dolma and RedPajama reduce the engineering burden. Language coverage, domain mix, freshness, rights review, deduplication quality and your storage budget should determine the choice.
This 13-source shortlist is therefore a set of fit-for-purpose options, not a universal league table. Use the comparison first, then apply the selection and compliance checks before downloading or training.
The 13 best web data sources at a glance
| Source | Best fit | What is established | Important caution |
|---|---|---|---|
| 1. Common Crawl | Teams that want raw, broad web coverage | Archive of web crawls available through AWS Public Data Sets and academic cloud platforms. | You must extract text, remove boilerplate, deduplicate, filter quality and audit provenance yourself. |
| 2. FineWeb | English general-purpose pretraining | Its current dataset card reports more than 18.5 trillion tokens from 96 Common Crawl dumps spanning summer 2013 through April 2024, with ODC-By 1.0 listed. | Large scale does not mean current web coverage; the underlying dumps end in April 2024. Read the card’s limitations and social-impact notes. |
| 3. FineWeb-Edu | Models where educational text is central | Education-oriented subset of FineWeb. | Confirm the live card’s release size, filtering recipe and terms before use; current figures were not independently established here. |
| 4. FineWeb-2 | Multilingual pretraining | A 2025 paper reports 20 TB, five billion documents and more than 1,000 languages. | Those are paper-reported figures. Verify the hosted release, language balance and current terms before treating them as present-day specifications. |
| 5. C4 / mC4 | Cleaned English or multilingual Common Crawl mixtures | Multiple variants exist, including English C4 and multilingual mC4 subsets. | Filtering differs materially between variants. Record the exact configuration you train on. |
| 6. Dolma | Broad mixtures beyond ordinary web pages | AI2’s card describes a three-trillion-token collection spanning web, academic publications, code, books and encyclopedic material; ODC-BY is listed. | Original source terms still apply. Review component provenance, personal-data documentation and commercial-use conditions. |
| 7. RedPajama-Data-V2 | Quality-scored, multilingual Common Crawl work | The card describes 84 snapshots, more than 100 billion documents, quality signals for 30 billion and a path to a 20-billion-document deduplicated collection. Listed languages are English, German, French, Spanish and Italian. | Signals and duplicate identifiers are inputs to your own selection policy, not a guarantee of clean training text. |
| 8. RefinedWeb | Teams evaluating a heavily filtered web corpus | Common Crawl-derived corpus associated with Falcon training. | Compare its filtering pipeline, snapshot scope and access terms with current alternatives; a current hosted release was not established here. |
| 9. DCLM-Baseline | General-web research baselines | Research-documented Common Crawl-derived baseline used in comparative dataset work. | Verify the live release card, version and use terms before depending on it for a production run. |
| 10. The Stack v2 | Code-model training | Code-focused source family built from repositories. | Repository licenses and opt-out or removal policies are heterogeneous. Preserve source metadata and review rights per repository. |
| 11. The Pile | Adding varied text to a web-heavy mixture | Mixed-source corpus that can broaden a training blend beyond crawled pages. | Assess constituent sources, age and terms individually; the aggregate name does not settle component rights. |
| 12. Wikimedia projects | Reference and encyclopedic knowledge | Useful factual complement through project dumps. | Follow the applicable project’s attribution and license requirements. It is a focused complement, not a web-scale replacement. |
| 13. arXiv and scholarly corpora (including S2ORC/peS2o) | Scientific and technical language | Domain-specific papers and metadata can improve research-heavy models. | Confirm corpus version, access terms and publisher rights for included papers. |
For a practical starting point, choose Common Crawl when you need maximum control, FineWeb or C4 when you want processed English web text, FineWeb-2 or mC4 for multilingual coverage, Dolma or The Pile for a broader mixture, and specialist sources such as The Stack v2, Wikimedia or scholarly corpora for target domains.
What “best” means for your training objective
Match the source to the task
A base language model needs broad language and subject coverage. A coding model needs repository text and programming-language balance. A scientific assistant needs papers and references that web-only corpora underrepresent. A factual or retrieval-oriented model benefits from encyclopedic and curated material. Treat the 13 sources as components you can mix, not mutually exclusive choices.
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Choose raw control or processed convenience
Common Crawl is an archive or input layer. You decide which snapshots to retain, how to parse WARC records, how to identify boilerplate, and how aggressively to remove duplicates. FineWeb, C4, Dolma and RedPajama encode those decisions for you and expose metadata or quality signals, saving engineering time while reducing control over the pipeline. Keep the original snapshot identifiers and your filtering configuration so another run can be reproduced.
Plan language and domain balance explicitly
FineWeb is English-focused. FineWeb-2 reports coverage above 1,000 languages, but you still need to inspect per-language volume and quality rather than assuming equal representation. RedPajama-Data-V2 lists five languages. Add code, scholarly or encyclopedic sources when the model’s evaluation set demands them, and measure the resulting mixture instead of relying on token totals alone.
Separate scale from quality
More documents or tokens do not automatically improve a model. Extraction errors, repeated pages, machine-generated text, spam and near-duplicates can consume compute while adding little information. The FineWeb maintainers report aggregate benchmark comparisons favoring FineWeb over several commonly used open datasets; that is a maintainer-reported result, not a universal ranking independent of model, tokenizer, mixture and evaluation setup.
How to build a defensible dataset mixture
- Write the target specification. Record languages, domains, expected context length, code proportion, freshness requirement and whether commercial deployment is planned.
- Select snapshots or releases. Pin the dataset version, Common Crawl dump dates or project commit. Do not describe an old paper’s number as the current size of a hosted release.
- Ingest with provenance. Preserve URL, crawl date, repository or paper identifier, license fields and the source dataset name for every retained record.
- Normalize and extract. Convert encodings consistently, remove navigation and boilerplate, reject malformed records and keep the raw record identifier for audit.
- Filter before deduplication. Apply language identification, length limits, quality rules, personal or sensitive-data policies and malware checks for code. Then perform exact and near-duplicate removal across all components, not just within each source.
- Sample the mixture. Set explicit proportions for web, code, scholarly, reference and educational material. Run pilot training or data-ablation experiments rather than assuming the largest source should dominate.
- Run contamination checks. Compare evaluation and benchmark documents against training hashes or URL sets. Quarantine suspected test-set overlaps and record the decision.
- Publish a manifest. Include release identifiers, filters, deduplication method, counts before and after each stage, storage locations and the terms you accepted.
Rights, provenance and privacy checks
A top-level label such as ODC-By 1.0 or ODC-BY is only one part of the review. FineWeb and Dolma both document limitations or source-level qualifications, and Dolma states that original source terms continue to apply. For each component, check:
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- the dataset card or paper’s stated license and version;
- licenses and terms inherited from original websites, repositories, books or papers;
- attribution, notice and share-alike obligations;
- commercial-use restrictions and whether model training is covered;
- personal, sensitive or copyrighted material and the provider’s removal process;
- your jurisdiction’s rules and the contracts governing deployment.
Keep a takedown workflow. Store source identifiers so a removed URL, repository or paper can be located in shards and excluded from future epochs. Do not infer that public access means unrestricted reuse.
Freshness, storage and reproducibility
Snapshot dates matter as much as token counts. FineWeb’s reported 18.5-trillion-token total comes from crawls ending in April 2024, while RedPajama-Data-V2’s figures describe its documented release and FineWeb-2’s figures come from a 2025 paper. Before budgeting storage or compute, verify the live card, shard format, compression, download method and checksum list.
Keep raw and processed tiers separate when possible. Raw WARC or repository archives preserve auditability; a columnar, tokenized derivative is faster and cheaper to train. Version the tokenizer, filtering code and random seeds. A reproducible manifest lets you add a new crawl without silently changing the old training set.
Using screenshots as a supplemental multimodal signal
Text datasets remain the core of language-model pretraining, but a multimodal project may also need page images paired with URLs or extracted text. Screenshots are useful for layout, charts and visual UI states; they should be collected under the same provenance, privacy and rights review as text. They do not replace crawl parsing or establish permission to redistribute page content.
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Troubleshooting common dataset problems
Downloads are incomplete or inconsistent
Pin the release or snapshot, verify checksums, retry failed shards and retain a manifest of successful files. Cloud availability does not guarantee that your local transfer completed correctly.
Token counts change between runs
Check tokenizer version, Unicode normalization, truncation and filtering order. Report both document counts and token counts before and after every major stage.
Duplicates remain after cleaning
Canonicalize URLs and whitespace, then run exact hashes followed by near-duplicate detection across the entire mixture. Source-level deduplication alone misses copies that appear in two datasets.
Training data contains unwanted personal or unsafe material
Strengthen language, quality and sensitive-data filters, retain source IDs, and implement a removal pipeline. Do not rely solely on a dataset’s marketing description or top-level license.
A specialist source overwhelms the general web
Set explicit sampling caps or temperature-based mixing and validate on task-specific evaluations. A three-trillion-token mixture can still be poorly balanced if one source dominates useful batches.
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FAQ
Frequently Asked Questions
Which dataset is best for training an LLM in English?
FineWeb is a strong processed starting point for English general-purpose training, while Common Crawl is preferable when you need to control extraction and filtering yourself. Choose after checking the release date, terms and your compute budget.
Is Common Crawl ready to train on directly?
No. It is a raw crawl archive. You need parsing, text extraction, quality filtering, deduplication, provenance tracking and rights review before creating training shards.
Can I use these datasets commercially?
Not automatically. Review each dataset’s stated terms and the licenses or restrictions inherited from its original sources, then obtain legal advice for your jurisdiction and use case.
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No. Mixture balance, filtering, deduplication, freshness and evaluation contamination can matter more than aggregate size.
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