lance-format/trivia-qa-lance

TriviaQA (Lance Format)

Lance-formatted version of TriviaQA (rc.nocontext config) — a question-answering dataset of trivia questions paired with answer aliases — with MiniLM sentence embeddings stored inline.

Why rc.nocontext?

The full TriviaQA dataset bundles entire Wikipedia / web pages per question (entity_pages, search_results), which makes it tens of GB. The rc.nocontext slice keeps the question + answer + answer aliases in a compact form — ideal for closed-book QA, retrieval research, and as a search target.

Splits

Split	Rows
train.lance	138,384
validation.lance	17,944

Schema

Column	Type	Notes
question_id	string	TriviaQA question id (e.g. tc_1)
question	string	The trivia question
question_source	string	URL / source where the question came from
answer_value	string	Canonical answer
answer_aliases	list<string>	Other accepted phrasings (e.g. ["Sinclair Lewis", "Harry Sinclair Lewis"])
normalized_answer	string	Lowercased / normalized form for exact-match scoring
answer_type	string	TriviaQA entity type (e.g. WikipediaEntity, FreebaseEntity)
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
• BTREE on question_id and answer_value
• BITMAP on answer_type

Quick start

import lance

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

Semantic search over questions

import lance
import pyarrow as pa
from sentence_transformers import SentenceTransformer

encoder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cuda")
q = encoder.encode(["who painted the sistine chapel ceiling"], normalize_embeddings=True)[0]

ds = lance.dataset("hf://datasets/lance-format/trivia-qa-lance/data/train.lance")
emb_field = ds.schema.field("question_emb")
hits = ds.scanner(
    nearest={"column": "question_emb", "q": pa.array([q.tolist()], type=emb_field.type)[0], "k": 5},
    columns=["question", "answer_value", "answer_aliases"],
).to_table().to_pylist()

LanceDB semantic search

import lancedb
from sentence_transformers import SentenceTransformer

encoder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cuda")
q = encoder.encode(["who painted the sistine chapel ceiling"], normalize_embeddings=True)[0]

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

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

LanceDB full-text search

import lancedb

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

results = (
    tbl.search("sistine chapel")
    .select(["question", "answer_value"])
    .limit(10)
    .to_list()
)

Filter by answer type

ds = lance.dataset("hf://datasets/lance-format/trivia-qa-lance/data/train.lance")
wiki = ds.scanner(filter="answer_type = 'WikipediaEntity'", columns=["question"], limit=5).to_table()

Filter with LanceDB

import lancedb

db = lancedb.connect("hf://datasets/lance-format/trivia-qa-lance/data")
tbl = db.open_table("train")
wiki = (
    tbl.search()
    .where("answer_type = 'WikipediaEntity'")
    .select(["question"])
    .limit(5)
    .to_list()
)

Why Lance?

• One dataset carries questions + answers + aliases + 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, generated answers, task labels) without rewriting the data.

Source & license

Converted from mandarjoshi/trivia_qa (rc.nocontext). TriviaQA is released under the Apache 2.0 license.

Citation

@article{joshi2017triviaqa,
  title={TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension},
  author={Joshi, Mandar and Choi, Eunsol and Weld, Daniel S and Zettlemoyer, Luke},
  journal={arXiv preprint arXiv:1705.03551},
  year={2017}
}