orgrctera/beir_msmarco

MS MARCO (BEIR) — Passage retrieval

Dataset description

MS MARCO (MicroSoft MAchine Reading COmprehension) is a large-scale resource built from real Bing search queries, web documents, and human-authored answers. It has become a standard benchmark for neural information retrieval and passage ranking under abundant training data: hundreds of thousands of queries with human relevance signals, aligned with realistic web search behavior.

BEIR (Benchmarking IR) is a heterogeneous benchmark for zero-shot evaluation of retrieval models across many domains and tasks. The BEIR release repackages MS MARCO passage ranking—queries, the official passage corpus identifiers, and qrels—in a unified format so that MS MARCO can be evaluated alongside 15+ other datasets with the same tooling and metrics.

This repository (orgrctera/beir_msmarco) provides train, dev, and test splits in Parquet form for CTERA-style retrieval evaluation. Each row is one query with relevance judgments pointing at MS MARCO passage IDs (the same string IDs used in the official collection.tsv and BEIR distributions).

Splits in this dataset

Split	Rows (this repo)	Role
train	502,939	Large-scale supervision (typically one or more labeled passage IDs per query; scores follow MS MARCO / BEIR conventions).
dev	6,980	Standard BEIR MS MARCO dev evaluation set (queries with qrels; aligns with common BEIR leaderboard setups).
test	43	Small held-out evaluation with candidate lists and graded relevance scores (0–3) in expected_output for reranking-style or multi-candidate evaluation.

Corpus: MS MARCO uses a fixed corpus of ~8.8M short passages. Full retrieval experiments require joining passage IDs to text via the official MS MARCO passage collection or a BEIR mirror (e.g. BeIR/msmarco on the Hub).

Task: retrieval (MS MARCO passage ranking, BEIR version)

The task is ad hoc passage retrieval (passage ranking):

1. Input: a natural-language query (information need from real search logs).
2. Output: a ranked list of passage IDs from the MS MARCO collection, or scores over the full index, such that relevant passages—according to the official qrels—receive high rank.

Downstream metrics are standard IR metrics used in MS MARCO and BEIR (e.g. MRR@10, nDCG@10, Recall@k), depending on the evaluation script and whether you run full retrieval or reranking over a candidate set.

Note: This Hub dataset stores queries and labels (passage IDs + scores). You still need the passage corpus keyed by the same IDs for indexing, embedding, or BM25 baselines.

Data format (this repository)

Each record includes:

Field	Description
id	UUID for this example row.
input	The query text.
expected_output	JSON string: list of objects {"id": "<passage-pid>", "score": <relevance>}. Passage IDs are MS MARCO passage pids (strings). Train/dev often use binary relevance (1 = relevant); test rows in this repo include graded scores (0–3) over a candidate list.
metadata.query_id	Original MS MARCO / BEIR query identifier (string).
metadata.split	Split name: train, dev, or test.

Examples

Example 1 (dev — single relevant passage)

{
  "id": "c61c30e4-d7dc-464a-b168-2f85a11f896c",
  "input": "how many years did william bradford serve as governor of plymouth colony?",
  "expected_output": "[{\"id\": \"7067032\", \"score\": 1}]",
  "metadata.query_id": "300674",
  "metadata.split": "dev"
}

Example 2 (train — query with one labeled passage)

{
  "id": "0e8bc96c-daa7-467d-b28a-180893c6946c",
  "input": "temperature sorrento italy september",
  "expected_output": "[{\"id\": \"14410\", \"score\": 1}]",
  "metadata.query_id": "512836",
  "metadata.split": "train"
}

References

MS MARCO (original dataset)

Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, Tong Wang.
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. arXiv:1611.09268, 2016.

Abstract (short): MS MARCO introduces a large-scale machine reading comprehension dataset built from real Bing queries, with human-generated answers and millions of passages from retrieved web documents. The work defines multiple tasks of varying difficulty, including passage ranking—establishing MS MARCO as a benchmark for realistic, large-scale QA and IR research.

• Paper: arXiv:1611.09268

@article{bajaj2016ms,
  title={Ms marco: A human generated machine reading comprehension dataset},
  author={Bajaj, Payal and Campos, Daniel and Craswell, Nick and Deng, Li and Gao, Jianfeng and Liu, Xiaodong and Majumder, Rangan and McNamara, Andrew and Mitra, Bhaskar and Nguyen, Tri and others},
  journal={arXiv preprint arXiv:1611.09268},
  year={2016}
}

MS MARCO ranking evaluation (leaderboards & validity)

Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Jimmy Lin.
MS MARCO: Benchmarking Ranking Models in the Large-Data Regime. arXiv:2105.04021, 2021.

Abstract (from arXiv): “Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public leaderboard such as MS MARCO, are intended to encourage research and track our progress, addressing big questions in our field. However, the goal is not simply to identify which run is "best", achieving the top score. The goal is to move the field forward by developing new robust techniques, that work in many different settings, and are adopted in research and practice. This paper uses the MS MARCO and TREC Deep Learning Track as our case study, comparing it to the case of TREC ad hoc ranking in the 1990s. We show how the design of the evaluation effort can encourage or discourage certain outcomes, and raising questions about internal and external validity of results. We provide some analysis of certain pitfalls, and a statement of best practices for avoiding such pitfalls. We summarize the progress of the effort so far, and describe our desired end state of "robust usefulness", along with steps that might be required to get us there.”

• Paper: arXiv:2105.04021

BEIR benchmark (MS MARCO as a subset)

Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, Iryna Gurevych.
BEIR: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models.
NeurIPS 2021 (Datasets and Benchmarks Track).

Abstract (from arXiv): “Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of-distribution (OOD) generalization capabilities. To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval. We leverage a careful selection of 18 publicly available datasets from diverse text retrieval tasks and domains and evaluate 10 state-of-the-art retrieval systems including lexical, sparse, dense, late-interaction and re-ranking architectures on the BEIR benchmark. Our results show BM25 is a robust baseline and re-ranking and late-interaction-based models on average achieve the best zero-shot performances, however, at high computational costs. In contrast, dense and sparse-retrieval models are computationally more efficient but often underperform other approaches, highlighting the considerable room for improvement in their generalization capabilities.”

• Paper: arXiv:2104.08663 — OpenReview; code and data: BEIR on GitHub.

@article{thakur2021beir,
  title={BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
  author={Thakur, Nandan and Reimers, Nils and R{\"u}ckl{\'e}, Andreas and Srivastava, Abhishek and Gurevych, Iryna},
  journal={arXiv preprint arXiv:2104.08663},
  year={2021}
}

Official resources

• MS MARCO — Datasets for Document and Passage Ranking — corpus, qrels, terms of use.
• MSMARCO-Passage-Ranking (GitHub)

Related Hugging Face mirrors (BEIR layout)

• BeIR/msmarco — corpus / queries / qrels in classic BEIR form.
• irds/beir_msmarco_dev — ir-datasets packaging of BEIR MS MARCO dev.

License and terms

The underlying MS MARCO data is subject to Microsoft’s terms for non-commercial research as published on the official MS MARCO Datasets page. Review Terms and Conditions before use in products or redistribution.

This card marks license: other to reflect MS MARCO’s upstream terms (not a single SPDX code). When publishing results, cite MS MARCO and BEIR as appropriate.

Citation

If you use MS MARCO, cite Bajaj et al. (2016). If you use the BEIR benchmark formulation, cite Thakur et al. (2021). BibTeX for BEIR is also available in the official BEIR repository.

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Dataset card maintained for the orgrctera/beir_msmarco Hub repository.