Help me find a benchmark dataset for evaluating a search ranking model

Here are some widely used benchmark datasets for evaluating search ranking / learning-to-rank models, depending on your task:

Classic benchmark datasets

1. MS MARCO

   • Very popular for passage/document ranking and semantic search.
   • Large-scale, real user queries with relevance judgments.
   • Good for modern neural rankers and retrieval + reranking.

2. TREC Deep Learning (TREC DL)

   • Based on MS MARCO with additional high-quality relevance judgments.
   • Excellent for evaluating ranking quality more rigorously than MS MARCO alone.

3. LETOR family

   • Classic learning-to-rank benchmark datasets.
   • Includes MQ2007, MQ2008, etc.
   • Good for traditional rankers and feature-based models.

4. MSLR-WEB10K / MSLR-WEB30K

   • Large benchmark datasets from Microsoft.
   • Widely used in academic ranking research.
   • Good when you want standard LTR evaluation with many query-document pairs.

Other useful datasets

5. ClueWeb09 / ClueWeb12

   • Web-scale datasets used with TREC tracks.
   • More realistic but harder to work with.

6. ANTIQUE

   • Question-answer search dataset.
   • Useful if your search model is for answer retrieval or QA-style queries.

7. BEIR benchmark

   • A collection of many retrieval datasets across domains.
   • Great if you want to test generalization across tasks rather than one fixed benchmark.

Choosing one

• If you want a modern default benchmark: MS MARCO + TREC DL
• If you want a classic LTR benchmark: MSLR-WEB10K
• If you want out-of-domain robustness: BEIR
• If you want small, standard academic datasets: LETOR (MQ2007/MQ2008)

If you want, I can also help you pick one based on:

• pointwise / pairwise / listwise ranking
• web search vs semantic search
• small vs large dataset
• Python/benchmark loader availability