FineWeb2-embedded

Dataset summary

FineWeb2-embedded is an extension of the FineWeb2 dataset, annotated with document-level XLM-RoBERTa embeddings for 20 languages, making the dataset useful for a variety of tasks, including document clustering, filtering, and other multilingual research.

Since XLM-RoBERTa has a sequence length limit of 512 tokens, each document's embeddings are obtained by mean-pooling 512 token chunks of the XLM-RoBERTa output. Therefore, longer texts have more embeddings available (one per 512 tokens).

The embeddings were initially computed as part of our FineWeb2-HQ dataset (a high-quality subset of FineWeb2). However, we believe that they can be useful for other multilingual research and applications.

For more details, see our paper Enhancing Multilingual LLM Pretraining with Model-Based Data Selection.

Languages and subsets

Subset name	Language name	Number of documents	Disk size
rus_Cyrl	Russian	605,468,615	5.3T
cmn_Hani	Chinese	578,332,129	4.4T
deu_Latn	German	427,700,394	2.5T
spa_Latn	Spanish	405,634,303	2.3T
jpn_Jpan	Japanese	376,134,745	2.4T
fra_Latn	French	332,646,715	2.0T
ita_Latn	Italian	219,117,921	1.3T
por_Latn	Portuguese	189,851,449	1.1T
pol_Latn	Polish	138,337,436	794G
nld_Latn	Dutch	133,855,612	720G
ind_Latn	Indonesian	92,992,647	537G
tur_Latn	Turkish	88,769,907	487G
ces_Latn	Czech	62,703,458	390G
arb_Arab	Arabic	57,752,149	363G
fas_Arab	Persian	51,043,666	322G
hun_Latn	Hungarian	46,879,826	328G
swe_Latn	Swedish	45,329,979	261G
ell_Grek	Greek	44,202,550	267G
dan_Latn	Danish	42,975,661	262G
vie_Latn	Vietnamese	40,741,340	298G

We might consider adding new languages supported by the XLM-RoBERTa model to an upcoming version of the present dataset.

Dataset structure

Data fields

Each data entry includes the original FineWeb2 data fields with the addition of:

• embeddings: array of float arrays containing 768-dimensional XLM-RoBERTa embeddings for every 512 token chunk of the tokenized text

Data instance

{
  "id": "<urn:uuid:f26003c7-6084-4791-b3fe-240eedc37e76>",
  "text": "Plutonium ist einer der gefährlichsten Stoffe der Welt. Es entsteht als hochgiftiges und radioaktives Nebenprodukt der Energiegewinnung in Atomkraftwerken. Wer nur ein Millionstel Gramm – ein kaum staubkorngroßes Teilchen – der Substanz einatmet, kann daran sterben. In der Natur kommt der Stoff nur in geringsten Mengen vor, wird aber künstlich hergestellt, weil man damit Bomben bauen kann. Je nach Reinheitsgrad reichen für eine Atombombe bereits fünf Kilogramm. Bis zum Beginn der achtziger Jahre des letzten Jahrhunderts hatten die Reaktoren weltweit bereits rund 300.000 Kilogramm erbrütet. Jährlich kommen etwa 20.000 Kilo hinzu. Genau dieser Stoff wird zu Land und zu Wasser um den ganzen Erdball herum transportiert. Legendär sind die Castor-Transporte, bei denen unter strengsten Sicherheitsvorkehrungen und entsprechenden Kosten abgebrannte Brennelemente aus deutschen Kernkraftwerken zur Wiederaufbereitung nach La Hague (Frankreich) oder Sellafield (Großbritannien) gebracht werden. Erst vergangenen Mai hat ein Frachter die größte Menge wiederaufbereiteten Mülls aller Zeiten von Frankreich nach Japan gebracht. Nicht auszudenken, was ein Unfall auf See bedeuten würde.",
  "date": "2014-03-16T08:53:38Z",
  "dump": "CC-MAIN-2014-10",
  "embeddings": [[ ... ]],
  "file_path": "s3://commoncrawl/crawl-data/CC-MAIN-2014-10/segments/1394678702159/warc/CC-MAIN-20140313024502-00039-ip-10-183-142-35.ec2.internal.warc.gz",
  "language": "deu",
  "language_score":  0.9983288645744324,
  "language_script": "Latn",
  "minhash_cluster_size": 2,
  "top_langs": {"deu_Latn_score": 0.9983288645744324},
  "url": "http://www.greenpeace.org/austria/de/themen/atom/probleme/atomtransporte/",
}

Usage

You can load the dataset in Python using datasets:

from datasets import load_dataset

dataset = load_dataset("epfml/FineWeb2-embedded", "deu_Latn")

Licensing information

Like FineWeb2, this dataset is released under Open Data Commons Attribution License (ODC-By) v1.0 license and is subject to CommonCrawl's Terms of Use.

Dataset origin

Being based on 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.

Considerations for Using the Data

For the aspects of social impact, discussion of biases, and known limitations, we also refer to the FineWeb2 documentation.

Citation information

If you use this dataset in your research or applications, please use the following citation:

@article{messmer2025multilingdatacomp,
  title={Enhancing Multilingual LLM Pretraining with Model-Based Data Selection}, 
  author={Bettina Messmer and Vinko Sabolčec and Martin Jaggi},
  journal={arXiv},
  year={2025},
  url={https://arxiv.org/abs/2502.10361}, 
}