RizgarOzan/turkish-rag-eval

Turkish RAG Eval

58 hand-written Turkish questions over 54 Turkish Wikipedia health articles (1.09 M characters), each labelled with the article that answers it and a verbatim answer span. Built for turkish-rag-eval, a harness that measures which parts of a RAG pipeline (chunking, stemming, embedding model) pay off on Turkish.

from datasets import load_dataset

corpus = load_dataset("RizgarOzan/turkish-rag-eval", "corpus", split="corpus")
queries = load_dataset("RizgarOzan/turkish-rag-eval", "queries", split="queries")
qrels = load_dataset("RizgarOzan/turkish-rag-eval", split="test")

Files

BEIR layout, the one MTEB retrieval tasks read.

File	Rows	Fields
corpus.jsonl	54	_id (MediaWiki pageid), title, text, url
queries.jsonl	58	_id, text, answer_span
qrels/test.jsonl	58	query-id, corpus-id, score (always 1)

27 of the 54 articles answer at least one question; the other 27 are distractors from the same domain.

passages/ holds the same data at passage level (configs passages-corpus, passages-queries, passages-qrels): the harness's hierarchical chunks (≤700 characters, title is the section path), and a passage is relevant when it comes from the answering article and contains answer_span. Articles average ~20 000 characters, so a 512-token encoder only sees the lead of each one at article level; passages are what a RAG pipeline actually retrieves.

How it was made

• Questions are paraphrased, not copied. "Şeker hastalığı teşhisi konan kişilerin ne kadarında ketoasidoz da bulunuyor?" is asked of text reading "yaklaşık %25'i, diyabet teşhisi konulduğunda...". Copied wording would hand lexical retrievers an unearned advantage.
• One human annotator, one pass. No inter-annotator agreement figure yet.
• Relevance here is article-level. The harness is stricter: a retrieved chunk counts only if it comes from the right article and contains answer_span. Use the span if you chunk the corpus yourself.
• Corpus snapshot. Articles were fetched through the MediaWiki API as plain-text extracts, == Section == markers kept. The GitHub repo refetches live revisions; this upload pins one snapshot.

Results on this data

From the harness: hierarchical chunks, chunk-level relevance, CPU only. All configurations and six models are in the repo README.

Retriever	nDCG@10
dense, newmindai/Mursit-Large-TR-Retrieval	0.781
dense, intfloat/multilingual-e5-base	0.668
dense, paraphrase-multilingual-MiniLM-L12-v2	0.501
BM25, 5-character prefix stemming	0.494

Limits

• 58 queries is small: differences under about 0.05 nDCG are noise.
• One domain (health) for now. Questions from five more domains are being added in the repo and will join this dataset once verified.
• Encyclopaedic text, not clinical text. No patient data is used anywhere, and nothing here is a medical device.

Licence

Article text and answer spans come from Turkish Wikipedia, CC BY-SA 4.0; each article keeps its url. The questions were written for this dataset and are released under the same licence. The harness code is MIT.

Citation

@misc{ozan2026turkishrageval,
  author       = {Rızgar Ozan},
  title        = {Turkish RAG Eval: a retrieval test set for Turkish Wikipedia},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/RizgarOzan/turkish-rag-eval}},
  note         = {Harness: \url{https://github.com/RizgarOzan/turkish-rag-eval}}
}