MS MARCO v2.1

A Lance-formatted version of MS MARCO v2.1 — Microsoft’s machine-reading-comprehension benchmark built from anonymized Bing query logs. Each row is one user query, the up-to-10 candidate passages Bing retrieved for it with relevance flags, and the…

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Source dataset card and downloadable files for lance-format/ms-marco-v2.1-lance.

A Lance-formatted version of MS MARCO v2.1 — Microsoft’s machine-reading-comprehension benchmark built from anonymized Bing query logs. Each row is one user query, the up-to-10 candidate passages Bing retrieved for it with relevance flags, and the human-written reference answers, with MiniLM query embeddings stored inline and pre-built ANN/FTS indices, available directly from the Hub at hf://datasets/lance-format/ms-marco-v2.1-lance/data.

Key features

• Self-contained passage-ranking rows — each query carries up to 10 candidate passages in parallel passage_text / passage_url / passage_is_selected columns, alongside the human-written answers and well_formed_answers.
• First relevant passage promoted to its own field in selected_passage, so RAG / answer-evaluation workflows can read the gold context without indexing into the parallel passage lists.
• Pre-computed 384-dim query embeddings (query_emb, sentence-transformers/all-MiniLM-L6-v2, cosine-normalized) with a bundled IVF_PQ index for semantic query lookup.
• One columnar dataset — scan query metadata cheaply, defer the heavy passage text reads to the rows that matter.

Splits

Split	Rows
train.lance	808,731
validation.lance	101,093

Schema

Column	Type	Notes
query_id	int64	MS MARCO query id
query	string	The user’s natural-language query
query_type	string	One of DESCRIPTION, NUMERIC, ENTITY, LOCATION, PERSON
answers	list<string>	Human-written reference answers
well_formed_answers	list<string>	Reference answers re-written as full sentences
passage_text	list<string>	Up to 10 candidate passages
passage_url	list<string>	Source URLs for each candidate
passage_is_selected	list<int8>	1 if Bing labelled the passage relevant
selected_passage	string?	First relevant passage (null if none)
query_emb	fixed_size_list<float32, 384>	MiniLM query embedding

Pre-built indices

• IVF_PQ on query_emb — semantic query lookup (cosine)
• INVERTED (FTS) on query and selected_passage — keyword and hybrid search
• BTREE on query_id — stable lookup by identifier
• BITMAP on query_type — cheap predicate evaluation for query class

Why Lance?

1. Blazing Fast Random Access: Optimized for fetching scattered rows, making it ideal for random sampling, real-time ML serving, and interactive applications without performance degradation.
2. Native Multimodal Support: Store text, embeddings, and other data types together in a single file. Large binary objects are loaded lazily, and vectors are optimized for fast similarity search.
3. Native Index Support: Lance comes with fast, on-disk, scalable vector and FTS indexes that sit right alongside the dataset on the Hub, so you can share not only your data but also your embeddings and indexes without your users needing to recompute them.
4. Efficient Data Evolution: Add new columns and backfill data without rewriting the entire dataset. This is perfect for evolving ML features, adding new embeddings, or introducing moderation tags over time.
5. Versatile Querying: Supports combining vector similarity search, full-text search, and SQL-style filtering in a single query, accelerated by on-disk indexes.
6. Data Versioning: Every mutation commits a new version; previous versions remain intact on disk. Tags pin a snapshot by name, so retrieval systems and training runs can reproduce against an exact slice of history.

Load with datasets.load_dataset

You can load Lance datasets via the standard HuggingFace datasets interface, suitable when your pipeline already speaks Dataset / IterableDataset or you want a quick streaming sample.

import datasets

hf_ds = datasets.load_dataset("lance-format/ms-marco-v2.1-lance", split="validation", streaming=True)
for row in hf_ds.take(3):
    print(row["query"], "->", row["answers"])

Load with LanceDB

LanceDB is the embedded retrieval library built on top of the Lance format (docs), and is the interface most users interact with. Each .lance file in data/ is a table — open by name (train, validation). The same handle is used by the Search, Curate, Evolve, Versioning, and Materialize-a-subset sections below.

import lancedb

db = lancedb.connect("hf://datasets/lance-format/ms-marco-v2.1-lance/data")
tbl = db.open_table("validation")
print(len(tbl))

Load with Lance

pylance is the Python binding for the Lance format and works directly with the format’s lower-level APIs. Reach for it when you want to inspect dataset internals — schema, scanner, fragments, the list of pre-built indices.

import lance

ds = lance.dataset("hf://datasets/lance-format/ms-marco-v2.1-lance/data/validation.lance")
print(ds.count_rows(), ds.schema.names)
print(ds.list_indices())

Tip — for production use, download locally first. Streaming from the Hub works for exploration, but heavy random access and ANN search are far faster against a local copy:

hf download lance-format/ms-marco-v2.1-lance --repo-type dataset --local-dir ./ms-marco-v2.1-lance

Then point Lance or LanceDB at ./ms-marco-v2.1-lance/data.

Search

The bundled IVF_PQ index on query_emb makes nearest-neighbour query lookup a single call. In production you would encode an incoming user query through the same 384-dim MiniLM encoder used at ingest and pass the resulting vector to tbl.search(...). The example below uses the embedding from row 42 as a runnable stand-in so the snippet works without loading a model.

import lancedb

db = lancedb.connect("hf://datasets/lance-format/ms-marco-v2.1-lance/data")
tbl = db.open_table("validation")

seed = (
    tbl.search()
    .select(["query_emb", "query"])
    .limit(1)
    .offset(42)
    .to_list()[0]
)

hits = (
    tbl.search(seed["query_emb"], vector_column_name="query_emb")
    .metric("cosine")
    .where("query_type = 'NUMERIC'", prefilter=True)
    .select(["query_id", "query", "selected_passage", "answers"])
    .limit(10)
    .to_list()
)
for r in hits:
    print(r["query"], "->", (r["selected_passage"] or "")[:120])

The result set carries only the projected columns; the 384-d query_emb is never read on the result side, and the full passage_text list is left untouched, keeping the working set small even when the underlying scan touches every row of the validation split.

Because the dataset also ships an INVERTED index on both query and selected_passage, the same query can be issued as a hybrid search that combines the dense vector with a keyword query against the gold passage. LanceDB merges the two result lists and reranks them in a single call, which is useful when a phrase must literally appear in the relevant passage but the dense side still does most of the ranking.

hybrid_hits = (
    tbl.search(query_type="hybrid")
    .vector(seed["query_emb"])
    .text("determinant matrix")
    .select(["query", "selected_passage", "answers"])
    .limit(10)
    .to_list()
)
for r in hybrid_hits:
    print(r["query"])

Tune metric, nprobes, and refine_factor on the vector side to trade recall against latency for your workload.

Curate

A typical curation pass over MS MARCO starts by combining metadata filters with structural predicates over the parallel passage lists before any heavy text gets read. Lance evaluates the filter inside a single scan, so the candidate set comes back already filtered, and the bounded .limit(1000) keeps the output small enough to inspect. The example below assembles a set of numeric questions for which Bing labelled at least one passage relevant and the annotators produced a well-formed reference answer.

import lancedb

db = lancedb.connect("hf://datasets/lance-format/ms-marco-v2.1-lance/data")
tbl = db.open_table("train")

candidates = (
    tbl.search()
    .where(
        "query_type = 'NUMERIC' "
        "AND selected_passage IS NOT NULL "
        "AND array_length(well_formed_answers) > 0 "
        "AND length(query) >= 30",
        prefilter=True,
    )
    .select(["query_id", "query", "answers", "well_formed_answers"])
    .limit(1000)
    .to_list()
)
print(f"{len(candidates)} candidates; first: {candidates[0]['query']}")

The result is a plain list of dictionaries, ready to inspect, persist as a manifest of query_ids, or hand to the Evolve and Train sections below. Neither passage_text nor query_emb is read by this scan, so a 1000-row curation pass against the Hub moves only kilobytes of metadata.

Evolve

Lance stores each column independently, so a new column can be appended without rewriting the existing data. The lightest form is a SQL expression: derive the new column from columns that already exist, and Lance computes it once and persists it. The example below adds a query_length column and a num_selected count over the parallel passage_is_selected list, either of which can then be used directly in where clauses without recomputing the predicate on every query.

Note: Mutations require a local copy of the dataset, since the Hub mount is read-only. See the Materialize-a-subset section at the end of this card for a streaming pattern that downloads only the rows and columns you need, or use hf download to pull the full corpus.

import lancedb

db = lancedb.connect("./ms-marco-v2.1-lance/data")  # local copy required for writes
tbl = db.open_table("train")

tbl.add_columns({
    "query_length": "length(query)",
    "num_selected": "array_length(passage_is_selected)",
    "has_well_formed": "array_length(well_formed_answers) > 0",
})

If the values you want to attach already live in another table (cross-encoder reranker scores, generated-answer judgments, alternate embeddings from a stronger model), merge them in by joining on query_id:

import pyarrow as pa

reranker_scores = pa.table({
    "query_id": pa.array([1185869, 9083, 524332], type=pa.int64()),
    "reranker_top1_score": pa.array([0.91, 0.47, 0.83]),
})
tbl.merge(reranker_scores, on="query_id")

The original columns and indices are untouched, so existing code that does not reference the new columns continues to work unchanged. New columns become visible to every reader as soon as the operation commits. For column values that require a Python computation (e.g., running a different encoder over the query text), Lance provides a batch-UDF API — see the Lance data evolution docs.

Train

Projection lets a training loop read only the columns each step actually needs. LanceDB tables expose this through Permutation.identity(tbl).select_columns([...]), which plugs straight into the standard torch.utils.data.DataLoader so prefetching, shuffling, and batching behave as in any PyTorch pipeline. For a reader-style QA model the natural projection is the query plus the gold passage and the answer; for a query-encoder retraining loop the precomputed embedding is enough on its own.

import lancedb
from lancedb.permutation import Permutation
from torch.utils.data import DataLoader

db = lancedb.connect("hf://datasets/lance-format/ms-marco-v2.1-lance/data")
tbl = db.open_table("train")

train_ds = Permutation.identity(tbl).select_columns(["query", "selected_passage", "answers"])
loader = DataLoader(train_ds, batch_size=32, shuffle=True, num_workers=4)

for batch in loader:
    # batch carries only the projected columns; tokenize, forward, backward...
    ...

Switching feature sets is a configuration change: passing ["query_emb", "passage_text", "passage_is_selected"] to select_columns(...) on the next run reads only those columns, which is the right shape for training a passage reranker on cached query embeddings. Columns added in Evolve cost nothing per batch until they are explicitly projected.

Versioning

Every mutation to a Lance dataset, whether it adds a column, merges labels, or builds an index, commits a new version. Previous versions remain intact on disk. You can list versions and inspect the history directly from the Hub copy; creating new tags requires a local copy since tags are writes.

import lancedb

db = lancedb.connect("hf://datasets/lance-format/ms-marco-v2.1-lance/data")
tbl = db.open_table("train")

print("Current version:", tbl.version)
print("History:", tbl.list_versions())
print("Tags:", tbl.tags.list())

Once you have a local copy, tag a version for reproducibility:

local_db = lancedb.connect("./ms-marco-v2.1-lance/data")
local_tbl = local_db.open_table("train")
local_tbl.tags.create("numeric-v1", local_tbl.version)

A tagged version can be opened by name, or any version reopened by its number, against either the Hub copy or a local one:

tbl_v1 = db.open_table("train", version="numeric-v1")
tbl_v5 = db.open_table("train", version=5)

Pinning supports two workflows. A retrieval system locked to numeric-v1 keeps returning stable passages while the dataset evolves in parallel — newly added reranker scores or labels do not change what the tag resolves to. A training experiment pinned to the same tag can be rerun later against the exact same queries and passages, so changes in metrics reflect model changes rather than data drift. Neither workflow needs shadow copies or external manifest tracking.

Materialize a subset

Reads from the Hub are lazy, so exploratory queries only transfer the columns and row groups they touch. Mutating operations (Evolve, tag creation) need a writable backing store, and a training loop benefits from a local copy with fast random access. Both can be served by a subset of the dataset rather than the full corpus. The pattern is to stream a filtered query through .to_batches() into a new local table; only the projected columns and matching row groups cross the wire, and the bytes never fully materialize in Python memory.

import lancedb

remote_db = lancedb.connect("hf://datasets/lance-format/ms-marco-v2.1-lance/data")
remote_tbl = remote_db.open_table("train")

batches = (
    remote_tbl.search()
    .where(
        "query_type = 'NUMERIC' "
        "AND selected_passage IS NOT NULL "
        "AND array_length(well_formed_answers) > 0"
    )
    .select(["query_id", "query", "query_type", "answers", "well_formed_answers", "selected_passage", "query_emb"])
    .to_batches()
)

local_db = lancedb.connect("./ms-marco-numeric")
local_db.create_table("train", batches)

The resulting ./ms-marco-numeric is a first-class LanceDB database. Every snippet in the Search, Evolve, Train, and Versioning sections above works against it by swapping hf://datasets/lance-format/ms-marco-v2.1-lance/data for ./ms-marco-numeric.

Source & license

Converted from microsoft/ms_marco (v2.1). MS MARCO is released under the MIT license.

Citation

@article{nguyen2016ms,
  title={MS MARCO: A Human Generated MAchine Reading COmprehension Dataset},
  author={Nguyen, Tri and Rosenberg, Mir and Song, Xia and Gao, Jianfeng and Tiwary, Saurabh and Majumder, Rangan and Deng, Li},
  journal={arXiv preprint arXiv:1611.09268},
  year={2016}
}