Datasets:

nmrenyi
/

mamaretrieval

pretty_name: MamaRetrieval
language:
  - en
license: other
license_name: mamaretrieval-research-only-v1
license_link: LICENSE
task_categories:
  - text-retrieval
  - question-answering
tags:
  - medical
  - clinical-guidelines
  - midwifery
  - obstetrics
  - retrieval-benchmark
  - evaluation
  - llm-as-judge
  - rag
size_categories:
  - 1K<n<10K
configs:
  - config_name: queries
    data_files:
      - split: test
        path: data/queries.parquet
  - config_name: rankings
    data_files:
      - split: test
        path: data/rankings.parquet
  - config_name: judgments
    data_files:
      - split: test
        path: data/judgments.parquet
  - config_name: chunks
    data_files:
      - split: test
        path: data/chunks.parquet
  - config_name: judgments_with_reasoning
    data_files:
      - split: test
        path: audit/judgments_with_reasoning.parquet

MamaRetrieval — v0.1.0

A retrieval evaluation benchmark for medical RAG systems serving midwives and doctors. 3,185 clinical queries on midwifery / OBGYN topics, evaluated against the top-3 results of 6 retrievers, with per (query, chunk) pair labels graded by an LLM judge under a four-dimension rubric.

This release is the Tier 2 split (top-3 union of 6 retrievers, 36,418 labelled (q, c) pairs). The Tier 3 split (top-20 union) will land as v0.2.0.

Quick start

from datasets import load_dataset

queries   = load_dataset("nmrenyi/mamaretrieval", "queries",   split="test")
rankings  = load_dataset("nmrenyi/mamaretrieval", "rankings",  split="test")
judgments = load_dataset("nmrenyi/mamaretrieval", "judgments", split="test")
chunks    = load_dataset("nmrenyi/mamaretrieval", "chunks",    split="test")

# Optional — the same judgments + the judge's per-row reasoning trace (~117 MB)
judgments_full = load_dataset("nmrenyi/mamaretrieval",
                              "judgments_with_reasoning", split="test")

Configs

Config	Rows	Columns	What it is
`queries`	3,185	`query_id`, `query_text`, `seed_chunk_id`	The benchmark queries, each generated by an LLM from a single chunk of the corpus.
`rankings`	57,330	`query_id`, `retriever`, `rank`, `chunk_id`, `score`	For every query × retriever combination, the top-3 `chunk_id`s with the retriever's similarity score. 6 retrievers × 3,185 queries × 3 = 57,330.
`judgments`	36,418	`query_id`, `chunk_id`, `d1_topic`, `d2_meaningful`, `d3_actionable`, `d4_density`, `score`	One label per unique `(query, chunk)` pair in the pooled top-3 union. `score = d1 × (d2 + d3 + d4) ∈ [0..6]`.
`judgments_with_reasoning`	36,418	(same as `judgments`) + `thinking`	The same labels with the judge model's reasoning trace per row. Ships in `audit/` because it's ~117 MB and not needed to use the benchmark.
`chunks`	17,827	`chunk_id`, `text`	The chunk text for every `chunk_id` referenced by `queries.seed_chunk_id` or any retriever's top-3 result. Drawn from the producer corpus (see Provenance).

Schema notes

chunk_id is the 16-character hexadecimal identifier from the producer corpus. Every chunk_id that appears in rankings, judgments, judgments_with_reasoning, or queries.seed_chunk_id is guaranteed to be resolvable in chunks.
score in judgments is computed downstream from the four dimensions via score = d1 × (d2 + d3 + d4). The judge emits only d1..d4.
seed_chunk_id records which chunk an LLM was given when it synthesised the query. It's provenance, not a gold label — seed chunks may not appear in any retriever's top-3, and when they do they are not always the highest-rated chunk for that query.

Rubric

The judge scores each (query, chunk) pair on four dimensions:

D1 — Topic (bool): does the chunk address the same clinical problem as the query (same condition, intervention, and clinical-timing context)? If D1 = false, D2 = D3 = D4 = 0 automatically.
D2 — Meaningful clinical content (0–2): how rich is the chunk's clinical content, independent of whether it specifically answers the query?
D3 — Actionable guidance (0–2): how specific is the actionable guidance — vague advice (0), general direction (1), exact doses/thresholds/steps (2)?
D4 — Density (0–2): what fraction of the chunk is directly useful for answering this specific query?

score = d1 × (d2 + d3 + d4) ∈ [0..6].

The full prompt — including four worked examples that anchor the calibration — is shipped verbatim at audit/judge_relevance_prompt.txt. Its prompt_hash is recorded in manifest.json.

Retrievers

`name`	`model`
`bm25`	BM25 (lexical baseline)
`medcpt`	`ncbi/MedCPT` (Query + Article encoders)
`octen`	`Octen/Octen-Embedding-8B`
`voyage`	`voyage-4-large`
`lateon`	`lightonai/GTE-ModernColBERT-v1` (late-interaction ColBERT)
`gecko`	`gecko-1024-quant-v0.2.0` (on-device TFLite, deployed retriever)

All retrievers were run on the producer corpus (see Provenance) and their top-20 results stored. This release exposes the top-3 of each — the deployment-honest depth for the RAG system this benchmark was built for.

How the dataset was made

Query generation. For each clinically-relevant chunk in the producer corpus, an LLM (Qwen/Qwen3.6-27B-FP8) was prompted to produce one ≤20-word clinical question the chunk could answer. Chunks judged non-clinical (e.g. course outlines, references, learning objectives) were skipped. The full prompt is shipped at audit/query_generation_prompt.txt.
Retrieval. Each query was run against the producer corpus by every retriever. Top-20 candidates per retriever were stored.
Pooling. For each query, the union of every retriever's top-3 was deduped (~11.4 unique chunks per query at this scale).
Judging. Every (query, chunk) pair in the pool was scored by Qwen/Qwen3.5-397B-A17B-FP8 against the four-dimension rubric. The judge's reasoning was captured separately and is shipped in judgments_with_reasoning.

Validation: the judge model was calibrated against Claude Opus 4.7 reference labels on a 62-pair pilot, with 95% threshold agreement at score ≥ 3 and 85% at score ≥ 5.

Provenance

Producer corpus: rag-bundle-v0.2.0, produced at commit a1abe003 of nmrenyi/mamai-medical-guidelines. The 63,650-chunk corpus the retrievers were run against. Built from a mix of WHO guidelines, Tanzania / Zanzibar MOH documents, and a small set of midwifery references.
Versioning: v0.1.0 = Tier 2 (top-3 union). v0.2.0 will add Tier 3 (top-20 union) on the same query set when judging finishes.
Audit trail: manifest.json pins exact judge and generator model IDs, prompt hashes, and schema versions.

License — Research use only

This dataset is released for non-commercial academic research and retrieval-evaluation benchmarking only. By downloading or using it, you agree to all of the following:

Permitted

Academic research, including publication of aggregate metrics, qualitative analysis, ablations, and methodology comparisons.
Use as an evaluation benchmark for retrieval systems.
Re-running the rubric or running new judges against the included (query, chunk) pairs for methodology research.

Not permitted without explicit written permission

Any commercial use, including evaluation as part of internal product decisions at for-profit organisations.
Use of the chunk text as training data for any model — generative, embedding, retrieval, or otherwise.
Redistribution of the chunk text, in whole or in part, outside the form shipped here (i.e. do not extract chunks.parquet, repackage, mirror, or re-host the chunk content).
Production deployment of any system whose retrieval or judging behaviour has been tuned on this data.
Clinical use of the chunk text. None of the chunk content has been reviewed for clinical accuracy in the form presented here; do not surface it to patients or clinicians.

Full terms — including upstream-licensing constraints, attribution, and warranty disclaimers — are in LICENSE.

Citation

Ren, Yi. MamaRetrieval v0.1.0. 2026. https://huggingface.co/datasets/nmrenyi/mamaretrieval

Limitations

Scope: midwifery / OBGYN / neonatal care, framed for guidelines deployed in Zanzibar. Performance numbers do not transfer cleanly to general medical retrieval.
Depth-3 ceiling: ~25% of queries have no score ≥ 5 chunk in any retriever's top-3, even from the strongest retriever. This is an inherent depth-3 pool limit, not a retriever failure.
Single relevance judge: every (query, chunk) relevance label in this dataset is produced by one LLM (Qwen/Qwen3.5-397B-A17B-FP8) under the four-dimension rubric. That judge was calibrated against Claude Opus 4.7 on a 62-pair pilot — 95% threshold agreement at score ≥ 3, 85% at ≥ 5 — but that's a small LLM-vs-LLM sanity check, not a human-annotated gold standard. Practical consequences: retriever-vs-retriever rankings tend to be stable across reasonable relevance judges, but absolute score distributions and per-row labels will shift if you re-grade the same (query, chunk) pairs with a different judge. Treat each label as one judge's calibrated opinion, not ground truth.