README.md

metadata

pretty_name: Qdrant-FineWeb-10B
license: odc-by
language:
  - en
size_categories:
  - 10B<n<100B

Qdrant-FineWeb-10B

Overview

Qdrant-FineWeb-10B (Q-FineWeb-10B) is a 10-billion-vector retrieval benchmark derived from FineWeb. Each document is represented with dense and sparse embeddings from Alibaba-NLP/gte-multilingual-base, alongside its original FineWeb payload and metadata. The benchmark also includes exact brute-force ground truth for ~120,000 MS MARCO queries.

The dataset includes:

• 10 billion dense embeddings
• 10 billion sparse embeddings
• FineWeb document metadata and payloads
• ~120,000 MS MARCO retrieval queries
• Exact top-1000 ground truth for dense, sparse, and filtered retrieval

Dataset Contents

The corpus is a 10,074,324,060-document slice of HuggingFaceFW/fineweb.

Each row carries the document's dense and sparse embeddings, and all eight FineWeb source fields are retained verbatim:

Field	Type	Notes
id	string	FineWeb's <urn:uuid:...> document id
text	string	Full document text
url	string	Source URL
dump	string	Common Crawl dump id, e.g. CC-MAIN-2016-44
date	string	ISO-8601 crawl timestamp, e.g. 2016-10-20T19:25:59Z
language	string	FineWeb language label (en throughout the observed shards)
language_score	double	fastText language-detection confidence, ≥ 0.65
token_count	int32	FineWeb's GPT-2 token count
dense_embedding	list<halffloat>	768-d mGTE dense vector, unit-norm
sparse_embedding	struct<indices: list<uint32>, values: list<float>>	mGTE sparse weights over token vocabulary, unnormalized

Embeddings

Each document is represented using Alibaba-NLP/gte-multilingual-base.

• Dense: 768 dimensions; Vectors are unit-norm. Compare with cosine, or equivalently dot product; do not re-normalize.
• Sparse: indices are mGTE token ids over the model's 250,048-token vocabulary; values are the corresponding token weights. The pair of lists is index-aligned and the vectors are not normalized - score with dot product.

Queries and Ground Truth

The queries and the ground truth results are contained within a single file with the following format:

Column	Type	Meaning
msmarco_config, msmarco_split, msmarco_query_id	string	MS MARCO query identifier
hit_scores	list<float>	similarity scores in descending order
hit_fineweb_ids	list<string>	aligned FineWeb document UUIDs

The filtered files append their query's filter definition after those five; the added columns are described under Filtered Queries. To recover the query text and embeddings, see Regenerating MS MARCO Embeddings.

Queries

The benchmark contains 119,953 MS MARCO queries divided across four query sets:

• 100,000 dense queries
• 10,000 sparse queries
• 4,953 text-filtered dense queries
• 5,000 structured-filtered dense queries

The query sets are derived from MS MARCO v1.1 and v2.1 — the dense, sparse, and structured-filter sets from v2.1 test, the text-filter set from v1.1 (train 4,119 / validation 492 / test 389). Scripts are provided to recover the original queries from MS MARCO and regenerate their embeddings.

Note that text-filtered set started at 5,000 queries; however, 47 of those queries matched no document and were dropped, leaving 4,953.

Ground Truth

Exact top-1000 ground truth was computed with Supernova's nova-bf over the full 10-billion-vector corpus.

File	Rows	Vector	Filter	MS MARCO	Query columns beyond the common five
gt_dense_k1000.parquet	100,000	dense	none	v2.1 / test	—
gt_sparse_k1000.parquet	10,000	sparse	none	v2.1 / test	—
gt_text_filters_k1000.parquet	4,953	dense	text + domain	v1.1 / train + validation + test	selectivity_tier, keyword_phrase, domains
gt_structured_filters_k1000.parquet	5,000	dense	numeric + date + set	v2.1 / test	selectivity_tier, structured_group, ls_gte, date_gte, date_lt, dump_set

Dense results use cosine similarity on unit-normalized vectors; sparse results use dot product. hit_fineweb_ids are stored as bare UUIDs rather than FineWeb's <urn:uuid:...> representation.

Filtered Queries

Text-filtered queries combine a required keyword_phrase with 1–9 domains matched by OR. They also include a selectivity_tier (low, medium, or high). Of the 4,953 retained queries, 557 have fewer than 1,000 matching documents; 47 queries with no matches were dropped.

Structured-filtered queries use combinations of language score (ls_gte), date range (date_gte, date_lt), and Common Crawl dump membership (dump_set):

Group	Constraints
A	language score
B	date
C	dump set
D	date + dump set
E	language score + date + dump set

Each group contains 1,000 queries, and all structured queries return 1,000 results. A bound a group does not constrain is set to null.

Regenerating MS MARCO Embeddings

The provided scripts recover MS MARCO query text from the original source and regenerate the corresponding dense and sparse embeddings.

Requirements: pyarrow, requests, torch, transformers, sentence-transformers

All query sets:

python scripts/regenerate_all.py --sets . --out-dir regenerated/

A single set:

python scripts/regenerate_queries.py \
    --in gt_dense_k1000.parquet \
    --out regenerated/dense_regenerated.parquet \
    --vectors dense

The regenerated files preserve the original row order and add query plus the requested dense_embedding and/or sparse_embedding columns. MS MARCO splits are cached locally and reused; use --device cuda for GPU embedding.

--limit N is intended only for smoke tests: batching differences in SentenceTransformer.encode() can cause subset embeddings to differ from a full run by approximately 1e-7.

Sanity Check

python scripts/verify_regeneration.py --sets . --regenerated regenerated/

This verifies recovered query text against _checksums.json; it does not compare embeddings.

Numerical Reproducibility

The original hit_scores and hit_fineweb_ids were computed using bfloat16 GPU arithmetic, while the regeneration scripts produce float32 embeddings for reproducibility. The resulting numerical differences are small, but may reorder tied results or change membership near the top-1000 cutoff.

We are working on a fix and expect to release it within the next week.

Intended Uses

Q-FineWeb-10B is intended for large-scale evaluation of:

• dense vector search
• sparse vector search
• filtered vector search
• hybrid retrieval
• vector database ingestion and indexing
• approximate nearest-neighbor algorithms
• distributed retrieval systems

It is also useful for work that needs a very large embedded corpus rather than a retrieval benchmark: quantization and compression studies, index-build cost and memory-footprint measurement, sharding and routing strategies, embedding-space analysis at web scale, and cost/recall trade-off curves where 10B vectors make the trade-off visible in a way 1M-vector benchmarks do not.

Licensing

Q-FineWeb-10B

The FineWeb corpus, embeddings, and ground-truth results are released under the ODC-BY-1.0 license.

Note that ODC-BY covers the database and its contents as assembled here; the underlying web pages remain subject to their own terms, and FineWeb's own note that use is additionally subject to the Common Crawl Terms of Use applies to this dataset as well.

MS MARCO Queries

The ground truth was generated using MS MARCO queries, which remain subject to their original licensing terms. We do not redistribute MS MARCO query text or the derived embeddings; instead, the provided regeneration script downloads the queries directly from the original Hugging Face source and embeds them.

MS MARCO is made available by Microsoft for non-commercial research purposes only: "The MS MARCO datasets are intended for non-commercial research purposes only to promote advancement in the field of artificial intelligence and related areas, and is made available free of charge without extending any license or other intellectual property rights." Running the regeneration scripts places the resulting query text and vectors under those terms, independent of this dataset's ODC-BY license.

Users should review the MS MARCO license before use.

Acknowledgments

Thank you to Vulr for partnering with us to generate the FineWeb embeddings and HuggingFace for providing a storage grant to host this dataset. Additionally, this dataset would not exist without the upstream work it is built on:

• HuggingFaceFW and the FineWeb authors — Guilherme Penedo, Hynek Kydlíček, Loubna Ben Allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, and Thomas Wolf — for releasing FineWeb under ODC-BY and documenting its construction in enough detail to build on.
• Common Crawl, whose crawls inform FineWeb and therefore this corpus.
• Alibaba-NLP / Tongyi Lab for gte-multilingual-base and the mGTE work, released under Apache 2.0.
• Microsoft for MS MARCO, the source of the benchmark queries.