lance-format/natural-questions-val-lance

Natural Questions — Validation (Lance Format)

Lance-formatted version of the Natural Questions validation split — 7,830 real Google search queries with their full Wikipedia articles and 1–5 annotator labels per question. Sourced from google-research-datasets/natural_questions.

The NQ train split is 143 GB (307,373 rows); it is intentionally not bundled here. Add it via natural_questions/dataprep.py --splits train once disk + bandwidth allow.

Splits

Split	Rows
validation.lance	7,830

Schema

Column	Type	Notes
id	string	NQ example id
question	string	Original Google search query
document_title	string	Wikipedia article title
document_url	string	Wikipedia article URL
document_html	large_binary	Full HTML of the article (inline; UTF-8 bytes)
short_answers	list<string>	Deduped short-answer spans across all annotators
num_short_answers	int32	Total annotator spans (incl. duplicates)
has_short_answer	bool	At least one annotator provided a short-answer span
has_long_answer	bool	At least one annotator selected a long-answer candidate
yes_no_answer	string	YES / NO / NONE — majority vote across annotators
question_emb	fixed_size_list<float32, 384>	sentence-transformers all-MiniLM-L6-v2 (cosine-normalized)

Pre-built indices

• IVF_PQ on question_emb — metric=cosine
• INVERTED (FTS) on question
• BTREE on id, document_title
• BITMAP on yes_no_answer, has_short_answer, has_long_answer

Quick start

import lance
ds = lance.dataset("hf://datasets/lance-format/natural-questions-val-lance/data/validation.lance")
print(ds.count_rows(), ds.schema.names, ds.list_indices())

Load with LanceDB

These tables can also be consumed by LanceDB, the multimodal lakehouse and embedded search library built on top of Lance, for simplified vector search and other queries.

import lancedb

db = lancedb.connect("hf://datasets/lance-format/natural-questions-val-lance/data")
tbl = db.open_table("validation")
print(f"LanceDB table opened with {len(tbl)} questions")

LanceDB semantic question search

import lancedb
from sentence_transformers import SentenceTransformer

encoder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cuda")
q = encoder.encode(["who wrote the declaration of independence"], normalize_embeddings=True)[0]

db = lancedb.connect("hf://datasets/lance-format/natural-questions-val-lance/data")
tbl = db.open_table("validation")

results = (
    tbl.search(q.tolist(), vector_column_name="question_emb")
    .metric("cosine")
    .select(["question", "short_answers", "document_title"])
    .limit(5)
    .to_list()
)

LanceDB full-text search

import lancedb

db = lancedb.connect("hf://datasets/lance-format/natural-questions-val-lance/data")
tbl = db.open_table("validation")

results = (
    tbl.search("declaration of independence")
    .select(["question", "document_title"])
    .limit(10)
    .to_list()
)

Get only questions with short-answer spans

import lance
ds = lance.dataset("hf://datasets/lance-format/natural-questions-val-lance/data/validation.lance")
short = ds.scanner(
    filter="has_short_answer = true",
    columns=["question", "short_answers", "document_title"],
    limit=10,
).to_table().to_pylist()

Filter with LanceDB

import lancedb

db = lancedb.connect("hf://datasets/lance-format/natural-questions-val-lance/data")
tbl = db.open_table("validation")
short = (
    tbl.search()
    .where("has_short_answer = true")
    .select(["question", "short_answers", "document_title"])
    .limit(10)
    .to_list()
)

Read the full Wikipedia HTML for one question

import lance
ds = lance.dataset("hf://datasets/lance-format/natural-questions-val-lance/data/validation.lance")
row = ds.take([0], columns=["question", "document_html", "document_url"]).to_pylist()[0]
print(row["question"], "->", row["document_url"])
print(row["document_html"][:500].decode("utf-8", errors="replace"))

Source & license

Converted from google-research-datasets/natural_questions. NQ is released under CC BY-SA 3.0 (matching the Wikipedia source).

Citation

@article{kwiatkowski2019natural,
  title={Natural Questions: A Benchmark for Question Answering Research},
  author={Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, Ming-Wei and Dai, Andrew M. and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav},
  journal={Transactions of the Association for Computational Linguistics},
  year={2019}
}