Datasets:

lance-format
/

squad-v2-lance

license: cc-by-sa-4.0
task_categories:
  - question-answering
  - text-retrieval
language:
  - en
tags:
  - squad
  - squad-v2
  - reading-comprehension
  - lance
  - sentence-transformers
pretty_name: squad-v2-lance
size_categories:
  - 100K<n<1M

SQuAD v2 (Lance Format)

Lance-formatted version of SQuAD v2 — Stanford Question Answering Dataset, version 2 — with MiniLM sentence embeddings stored inline alongside the questions, contexts, and answers.

Why this version?

One self-contained Lance dataset with 130k+ Wikipedia-grounded questions and reference answers.
Pre-computed text embeddings (sentence-transformers/all-MiniLM-L6-v2, 384-dim, L2-normalized) on the question column with an IVF_PQ index — instant semantic question retrieval.
Full-text inverted indices on both question and context for keyword search.
BITMAP on is_impossible for fast filtering between answerable and unanswerable questions.

Splits

Split	Rows
`train.lance`	130,319
`validation.lance`	11,873

Schema

Column	Type	Notes
`id`	`string`	SQuAD question id
`title`	`string`	Wikipedia article title
`context`	`string`	Paragraph the question was generated from
`question`	`string`	The question text
`answers`	`list<string>`	Accepted answer spans (empty for impossible questions)
`answer_starts`	`list<int32>`	Character offsets of each answer within `context`
`is_impossible`	`bool`	`true` for SQuAD 2.0 unanswerable questions
`question_emb`	`fixed_size_list<float32, 384>`	MiniLM embedding of `question` (cosine-normalized)

Pre-built indices

IVF_PQ on question_emb — metric=cosine
INVERTED on question and context
BTREE on id and title
BITMAP on is_impossible

Quick start

import lance

ds = lance.dataset("hf://datasets/lance-format/squad-v2-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/squad-v2-lance/data")
tbl = db.open_table("validation")
print(f"LanceDB table opened with {len(tbl)} questions")

Semantic question retrieval

import lance
import pyarrow as pa
from sentence_transformers import SentenceTransformer

encoder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cuda")
q_vec = encoder.encode(["what year was the eiffel tower built?"], normalize_embeddings=True)[0]

ds = lance.dataset("hf://datasets/lance-format/squad-v2-lance/data/train.lance")
emb_field = ds.schema.field("question_emb")
query = pa.array([q_vec.tolist()], type=emb_field.type)

hits = ds.scanner(
    nearest={"column": "question_emb", "q": query[0], "k": 10, "nprobes": 16, "refine_factor": 30},
    columns=["id", "title", "question", "answers"],
).to_table().to_pylist()

LanceDB semantic question retrieval

import lancedb
from sentence_transformers import SentenceTransformer

encoder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cuda")
q_vec = encoder.encode(["what year was the eiffel tower built?"], normalize_embeddings=True)[0]

db = lancedb.connect("hf://datasets/lance-format/squad-v2-lance/data")
tbl = db.open_table("train")

results = (
    tbl.search(q_vec.tolist(), vector_column_name="question_emb")
    .metric("cosine")
    .select(["id", "title", "question", "answers"])
    .limit(10)
    .to_list()
)

Full-text search on contexts

ds = lance.dataset("hf://datasets/lance-format/squad-v2-lance/data/train.lance")
hits = ds.scanner(
    full_text_query="great pyramid of giza",
    columns=["title", "question", "context"],
    limit=5,
).to_table().to_pylist()

LanceDB full-text search

import lancedb

db = lancedb.connect("hf://datasets/lance-format/squad-v2-lance/data")
tbl = db.open_table("train")

results = (
    tbl.search("great pyramid of giza")
    .select(["title", "question", "context"])
    .limit(5)
    .to_list()
)

Filter answerable vs impossible questions

ds = lance.dataset("hf://datasets/lance-format/squad-v2-lance/data/validation.lance")
impossible = ds.scanner(filter="is_impossible = true", columns=["question"], limit=5).to_table()

Filter with LanceDB

import lancedb

db = lancedb.connect("hf://datasets/lance-format/squad-v2-lance/data")
tbl = db.open_table("validation")
impossible = (
    tbl.search()
    .where("is_impossible = true")
    .select(["question"])
    .limit(5)
    .to_list()
)

Why Lance?

One dataset carries questions + contexts + answers + embeddings + indices — no sidecar files.
On-disk vector and full-text indices live next to the data, so search works on local copies and on the Hub.
Schema evolution: add columns (alternate embeddings, model predictions, task labels) without rewriting the data.

Source & license

Converted from rajpurkar/squad_v2. SQuAD v2 is released under CC BY-SA 4.0.

Citation

@article{rajpurkar2018know,
  title={Know What You Don't Know: Unanswerable Questions for SQuAD},
  author={Rajpurkar, Pranav and Jia, Robin and Liang, Percy},
  journal={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Short Papers)},
  year={2018},
}