---
license: other
license_name: see-source-terms
task_categories:
  - text-retrieval
  - question-answering
language:
  - en
tags:
  - biomedical
  - pubmed
  - retrieval-augmented-generation
  - oncology
  - embeddings
  - benchmark
size_categories:
  - 10K<n<100K
---

# LUMEN RAG Data

A chunked biomedical passage corpus with aligned precomputed embeddings and relevance
judgments, built as the retrieval layer for **LUMEN**, the AI service behind the MediLink
healthcare platform.

The corpus covers oncology literature drawn from PubMed and PMC, chunked for retrieval
and paired with dense vectors from a biomedical bi-encoder. It ships with pooled
relevance judgments for 30 queries, so retrieval quality can be measured on it directly.
It's published so the retrieval stage of the system is reproducible without re-running
ingestion or re-encoding 77K passages.

## Files

| File | Size | Contents |
|---|---|---|
| `SpubMedBERT_chunks.json` | 60.2 MB | 77,649 chunk records with metadata |
| `embeddings_spubmedbert.npy` | 116 MB | `float16` array, shape `(77649, 768)` |
| `qrels/lumen_qrels.tsv` | 9.6 kB | Relevance judgments, TREC format |
| `qrels/lumen_queries.jsonl` | 3.2 kB | 30 queries with intent tags |
| `qrels/lumen_pool.json` | 9.5 kB | Pool composition and judging config |

The corpus and embedding files are aligned row for row. Row `i` of the `.npy` is the
embedding of chunk `i` in the JSON. Any filtering has to be applied to both together.

## Schema

| Field | Type | Description |
|---|---|---|
| `text` | string | Chunk text, 41–14,500 characters |
| `pmid` | string | PubMed identifier of the source article |
| `pmcid` | string | PMC identifier, empty when the article isn't in PMC |
| `title` | string | Source article title |
| `keyword` | string | Ingestion query that retrieved the article (13 classes) |
| `source` | string | Origin of the text, e.g. `full_text` (6 classes) |
| `chunk_type` | string | `general` or `symptom` |

### Topic distribution

The corpus was built by querying PubMed with 13 keyword strings. Five account for most
of its mass:

| Chunks | Ingestion keyword |
|---:|---|
| 13,656 | melanoma diagnosis clinical features |
| 13,489 | skin cancer types symptoms treatment |
| 13,287 | squamous cell carcinoma symptoms |
| 12,852 | multiple myeloma diagnosis signs |
| 12,825 | basal cell carcinoma signs symptoms |
| 4,337 | melanoma prevention UV protection |
| 6,738 | eight lower frequency keyword variants |

Chunks are stored in ingestion order, so passages on the same topic occupy contiguous
index ranges.

## Embeddings

- **Model:** [`pritamdeka/S-PubMedBert-MS-MARCO`](https://huggingface.co/pritamdeka/S-PubMedBert-MS-MARCO)
- **Dimension:** 768
- **Dtype:** `float16`. Cast to `float32` before matrix operations; `float16` matmul is
  slow on CPU and loses precision at this scale
- **Normalization:** vectors are stored unnormalized; L2 normalize before cosine similarity

## Usage

```python
import json
import numpy as np
from huggingface_hub import hf_hub_download

REPO = "mohamedkhaledmk7/LUMEN-rag-data"

chunks_path = hf_hub_download(REPO, "SpubMedBERT_chunks.json", repo_type="dataset")
embed_path  = hf_hub_download(REPO, "embeddings_spubmedbert.npy", repo_type="dataset")

with open(chunks_path) as f:
    chunks = json.load(f)

E = np.load(embed_path).astype("float32")
E /= np.linalg.norm(E, axis=1, keepdims=True) + 1e-12

assert E.shape[0] == len(chunks), "chunks and embeddings are misaligned"
```

Retrieval with the same encoder:

```python
from sentence_transformers import SentenceTransformer

encoder = SentenceTransformer("pritamdeka/S-PubMedBert-MS-MARCO")
q = encoder.encode(["What is the ABCDE rule for melanoma detection?"],
                   normalize_embeddings=True)

scores = (q @ E.T)[0]
for i in np.argsort(-scores)[:5]:
    print(f"{scores[i]:.3f}  {chunks[i]['title'][:70]}")
```

Exact search over 77K vectors is a single dot product, no vector database required. The
production system uses Weaviate for hybrid BM25 + dense retrieval, but that's a serving
choice, not a requirement of the data.

## Evaluation labels (qrels/)

Relevance judgments for 30 oncology queries, so nDCG, Recall@K, and MRR can be computed
on this corpus the same way they're computed on labeled benchmarks like BEIR.

**How they were built.** Five retrieval configurations (dense only, MedCPT
cross-encoder, and Cohere `rerank-v3.5` / `v4.0-fast` / `v4.0-pro`) each contributed
their top 10 per query over a shared top 100 candidate set. The union of all five,
about 732 unique passages, roughly 25 per query after deduplication, was judged on a 0
to 3 relevance scale. Pooling from all five systems equally matters here: a pool drawn
from a single reranker would score that reranker highly by construction.

**Judging.** Claude Haiku (`claude-haiku-4-5-20251001`), graded 0 to 3, following TREC
assessment practice. The judge saw only a query and a passage, never which system
retrieved it or at what rank. Pool order was shuffled per query too, so position
carried no signal either.

| Grade | Meaning | Share |
|---|---|---:|
| 3 | Directly and substantially answers the query | 33.1% |
| 2 | Partially answers the query | 32.1% |
| 1 | On topic but doesn't answer the query | 26.4% |
| 0 | Off topic | 8.5% |

Following BEIR convention, `lumen_qrels.tsv` only contains positive judgments. A
(query, passage) pair that's absent from the file is non-relevant. The full judged
pool, including grade-0 passages, is recorded in `lumen_pool.json`.

**Reliability.** A random 10% sample was re-judged across four independent runs: exact
agreement 67 to 70%, within one grade agreement 98 to 100%. Disagreement is confined to
adjacent grades. The judge separates relevant from irrelevant reliably, with some
remaining uncertainty on the partial versus full answer boundary.

**Loading:**

```python
from collections import defaultdict
from huggingface_hub import hf_hub_download

path = hf_hub_download(REPO, "qrels/lumen_qrels.tsv", repo_type="dataset")

qrels = defaultdict(dict)
with open(path) as f:
    next(f)                                   # header
    for line in f:
        qid, did, score = line.rstrip("\n").split("\t")
        qrels[qid][did] = int(score)          # did indexes into chunks / E
```

`corpus-id` is the integer row index into `SpubMedBERT_chunks.json` and the `.npy`, not
a PMID.

### Known properties of these labels

- **LLM generated, not human.** Weaker evidence than a hand-annotated benchmark like
  NFCorpus. Report results on this dataset separately from human-labeled benchmarks
  rather than averaging the two together.
- **Pooling bias.** A relevant passage that no pooled system ranked in its top 10 was
  never judged, and counts as non-relevant. Recall measured against these qrels only
  reflects what these five systems found. A sixth system evaluated later is mildly
  disadvantaged if it surfaces something all five missed. This is inherent to pooled
  evaluation, not specific to this dataset.
- **Queries are corpus derived.** They were written against the corpus's known topic
  distribution, so nearly all of them are answerable. Absolute scores run higher than
  they would on real production traffic, where some questions have no good answer.
  System comparisons stay valid though, since every system faces the same conditions.
- **One query is a known hard case.** `LQ011` ("histological subtypes of cutaneous
  squamous cell carcinoma") draws half its pool from grade 0 passages spread across the
  full corpus index range. The corpus's squamous cell keyword is symptom oriented and
  carries little histopathology content. It's kept in deliberately, as a case where
  retrieval genuinely fails.

## How it was used

In LUMEN, this corpus is served through Weaviate with intent aware hybrid retrieval
(BM25 blended with dense, `alpha = 0.60` by default and varied per query intent),
followed by cross-encoder reranking with
[`ncbi/MedCPT-Cross-Encoder`](https://huggingface.co/ncbi/MedCPT-Cross-Encoder) and
citation grounded generation.

## Limitations

- **Scope.** Oncology focused, driven by the 13 ingestion keywords. It's not a general
  biomedical corpus and will retrieve poorly outside its topics.
- **Keyword match drift.** Ingestion matched on keyword strings, so some articles are
  topical false positives: oesophageal squamous cell carcinoma, hepatocellular
  carcinoma, CAR-T for rheumatoid arthritis. These act as genuine distractors in
  retrieval evaluation.
- **Chunk boundaries.** Chunks are contiguous spans and some begin mid-sentence,
  carrying a trailing fragment of the previous chunk.
- **Not clinical guidance.** Research literature, retrieved by keyword, not reviewed
  for clinical accuracy or currency. Not suitable for medical decision-making.

## Licensing

Content is derived from PubMed abstracts and PMC articles. Terms vary per article: the
PMC Open Access Subset carries per-article Creative Commons or similar licenses, while
other PMC content is more restricted. Consult the terms for the underlying articles via
the `pmid` / `pmcid` fields before redistributing or using commercially.

## Citation

```bibtex
@misc{khaled2026lumen,
  title  = {LUMEN RAG Data: A Biomedical Passage Corpus with Aligned Embeddings
            and Pooled Relevance Judgments},
  author = {Mohamed Khaled},
  year   = {2026},
  url    = {https://huggingface.co/datasets/mohamedkhaledmk7/LUMEN-rag-data}
}
```