metadata
license: mit
task_categories:
- question-answering
- text-retrieval
language:
- en
tags:
- search
- benchmark
- news
- twitter
pretty_name: Desearch Benchmark Questions
size_categories:
- 100K<n<1M
configs:
- config_name: web
data_files:
- split: train
path: questions/*.jsonl
- config_name: x
data_files:
- split: train
path: x/*.jsonl
Desearch Benchmark Questions
Fresh, self-contained benchmark questions for evaluating web and X (Twitter) search. Regenerated daily from recent news and tweets. Each question is answerable from public sources within a dated window — there are no answer keys or source URLs in the public data, so systems have to actually search rather than recall.
Subsets
| Path | Lane | Built from |
|---|---|---|
questions/ |
Web / news | Recent news articles (RSS + news sitemaps) |
x/ |
X / Twitter | High-signal tweets from the past 24h |
Files are one per day: <YYYY-MM-DD>.jsonl (web) and x-<YYYY-MM-DD>.jsonl (X).
Schema
Every row is source-free:
{
"id": "…",
"question": "…",
"difficulty": "easy | medium | hard",
"start_date": "YYYY-MM-DDTHH:MM:SSZ",
"end_date": "YYYY-MM-DDTHH:MM:SSZ"
}
start_date/end_date bound the window in which the question is answerable — use them as a
date filter when searching. Gold answers are kept private and are never uploaded.
Loading
from datasets import load_dataset
web = load_dataset("desearch/dataset", "web", split="train")
x = load_dataset("desearch/dataset", "x", split="train")
Updates
Regenerated daily by an open-source generator (news twice daily, X once daily), so the set grows one file per lane per day.