README.md

metadata

license: apache-2.0
task_categories:
  - text-retrieval
  - feature-extraction
language:
  - en
tags:
  - search
  - benchmark
  - information-retrieval
  - full-text-search
size_categories:
  - 1M<n<10M
configs:
  - config_name: corpus
    data_files:
      - split: train
        path: corpus.parquet
  - config_name: queries
    data_files:
      - split: train
        path: queries.parquet
dataset_info:
  - config_name: corpus
    features:
      - name: id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: train
        num_examples: 5032104
  - config_name: queries
    features:
      - name: query
        dtype: string
      - name: tags
        sequence: string
    splits:
      - name: train
        num_examples: 903

Search Benchmark Dataset

A benchmark dataset for evaluating full-text search engines, derived from the search-benchmark-game project.

Dataset Description

This dataset contains a corpus of Wikipedia articles and a set of search queries designed to benchmark different search engine implementations.

Corpus

• Size: 5,032,104 documents
• Source: English Wikipedia
• Fields:
  • id: Wikipedia article URL (e.g., https://en.wikipedia.org/wiki?curid=48687903)
  • text: Article content (lowercase, cleaned text)

Queries

• Size: 903 queries
• Source: Derived from the AOL query dataset (filtered, no personal information)
• Fields:
  • query: Search query string
  • tags: List of query characteristics

Query Types

Type	Syntax	Example	Description
term	word	the	Single term query
phrase	"word1 word2"	"griffith observatory"	Exact phrase match (words must appear consecutively)
intersection	+word1 +word2	+griffith +observatory	AND query (all terms must appear, position-independent)
union	word1 word2	griffith observatory	OR query (any term can appear)

Query Tags

Tag	Description
term	Single term query
phrase	Phrase query requiring consecutive word positions
intersection	Boolean AND query
union	Boolean OR query
global	Position-independent matching (used with intersection/union, as opposed to phrase which requires consecutive positions)
num_token_N	Query contains N tokens
intersection:num_tokens_N	Intersection query with N tokens
phrase:num_tokens_N	Phrase query with N tokens
union:num_tokens_N	Union query with N tokens
union:num_tokens_>3	Union query with more than 3 tokens
two-phase-critical	Queries requiring two-phase execution

Usage

from datasets import load_dataset

# Load corpus
corpus = load_dataset("WenxingZhu/search-benchmark-dataset", "corpus", split="train")
print(f"Corpus size: {len(corpus)}")
print(corpus[0])

# Load queries
queries = load_dataset("WenxingZhu/search-benchmark-dataset", "queries", split="train")
print(f"Number of queries: {len(queries)}")
print(queries[0])

Dataset Statistics

Config	Documents	Fields
corpus	5,032,104	id, text
queries	903	query, tags

Benchmark Details

The corpus is the English Wikipedia with stemming disabled. Queries have been filtered from the AOL query dataset to include only those with at least two terms that yield at least one hit as a phrase query.

Query types tested include:

• Intersection queries: Multiple terms with AND semantics (all terms must appear)
• Union queries: Multiple terms with OR semantics (any term can appear)
• Phrase queries: Exact phrase matching (terms must appear consecutively)

Collection options:

• COUNT: Only count documents
• TOP 10: Retrieve 10 documents with best BM25 score
• TOP 10 + COUNT: Top 10 documents + total count

Source

This dataset is derived from the search-benchmark-game project by Quickwit, which benchmarks various search engines including Tantivy, Lucene, PISA, and others.

License

Apache 2.0