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9
| [-0.022997364401817322,0.036807991564273834,0.004890945740044117,0.028384272009134293,0.043858278542(...TRUNCATED)
|
End of preview. Expand
in Data Studio
Arctic-Wiki-English-1M
VDBBench-compatible vector benchmark case published as a Hugging Face dataset repository.
What’s in this repo
vdbbench/: a Parquet-only folder intended to be downloaded as-is for VDBBench.- The Dataset Viewer indexes only train/test (and optionally shuffled), and intentionally ignores
neighbors.parquet.
vdbbench/ file contract (VDBBench)
- Train vectors:
train.parquetortrain-*-of-*.parquet - Query vectors:
test.parquetortest-*-of-*.parquet - Ground truth:
neighbors.parquetwith columnsid,neighbors_id(list[int]) - Optional shuffled train:
shuffle_train.parquetorshuffle_train-*-of-*.parquet
Important: do not add non-parquet files inside vdbbench/ (VDBBench requires the folder to be parquet-only).
Download for VDBBench
Download only the parquet files and point VDBBench at the vdbbench/ folder.
hf download "AI71ai/Arctic-Wiki-English-1M" \
--repo-type dataset \
--include "vdbbench/*.parquet" \
--local-dir ./my_case
Then set VDBBench /custom → Folder Path to:
./my_case/vdbbench
How this case was created
- Source:
wikimedia/wikipediaat revisionb04c8d1ceb2f5cd4588862100d08de323dccfbaa(pinned for reproducibility when set). - Embedding model:
Snowflake/snowflake-arctic-embed-l-v2.0(dimension 1024). - Languages:
en - Train size:
1,000,000vectors - Test size:
1,000query vectors - Phase 2 seed:
42
Size filtering (chosen to embed well)
We filter Wikipedia articles by character length of the article text to avoid pages that are too short (low-signal) or too long (risking truncation / poor embedding behavior).
Approx token ranges below are computed from the Snowflake Arctic tokenizer’s observed chars/token ratios (using the per-language ~8k-token max lengths we chose).
| Language | Text length filter (chars) | Approx token range |
|---|---|---|
en |
400–32,605 chars |
~98–~8,000 tokens |
Test set (queries)
- Queries are derived from Wikipedia titles of documents that appear in the training stream.
- Phase 2 Stage 1 selects
test_sizetitles using reservoir sampling (bounded memory) while writing train. - Phase 2 Stage 2 embeds those titles to produce
test*.parquet(query vectors).
Shuffled train (optional)
- Some vector DBs are sensitive to ingestion order; this benchmark optionally provides a shuffled training set to evaluate that behavior.
- This repo includes
shuffle_train*.parquetfor VDBBench “Use Shuffled Data” mode.
Ground truth (neighbors.parquet)
neighbors.parquetcontains the exact top-400nearest train IDs for each test query ID.- Similarity is computed with cosine (implemented as L2-normalize + inner product).
Provenance: ID map sidecars (maps/)
This repo also includes optional ID mapping sidecars under maps/ that let you trace VDBBench IDs back to the original Wikipedia/source IDs used during embedding.
maps/is intentionally not part of any Dataset Viewer split.- Files are Parquet (same sharding convention as vectors when applicable).
Files
train_id_map*.parquet: train VDBBench id → source id mappingtest_id_map*.parquet: test query id → train_id + source id mappingshuffle_train_id_map*.parquet: shuffled-train VDBBench id → source id mapping
Schemas
train_id_map:id,source_idtest_id_map:id,train_id,source_idshuffle_train_id_map:id,source_id
Notes:
source_idrefers to the upstream article ID from the embedded Wikipedia dump (see the pipeline repo for details).- For mixed-language cases, the
languagecolumn indicates which language shard the source came from.
Design decisions
- Per-case repos: keeps each benchmark case independently versionable and easily downloadable without filtering huge multi-case repos.
- Parquet-only
vdbbench/folder: matches VDBBench’s strict folder selection rule and enables “download folder → run benchmark”. - Neighbors kept next to vectors, but not indexed:
neighbors.parquetis required by VDBBench, but it’s not useful to display as a dataset split in the viewer. - Prefer single-file
test.parquet: VDBBench duplicates test queries across processes; keeping queries small and single-file reduces overhead. - Embedding model choice: Snowflake Arctic Embed L v2.0 is high on the MTEB-R leaderboard (see
https://huggingface.co/spaces/mteb/leaderboard) and is a strong fit for evaluating compressed representations (including binarized/quantized vectors).
Usage with datasets
from datasets import load_dataset
ds = load_dataset("AI71ai/Arctic-Wiki-English-1M")
print(ds)
Note: neighbors.parquet is intentionally not part of any split.
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