Add dataset card

README.md

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+---
+pretty_name: RAGCompass
+license: other
+license_name: mixed
+language:
+  - en
+task_categories:
+  - question-answering
+  - text-retrieval
+tags:
+  - retrieval-augmented-generation
+  - retriever-routing
+  - multi-hop-qa
+size_categories:
+  - 100K<n<1M
+configs:
+  - config_name: default
+    data_files: "Llama-3.1-8B-Instruct_Qwen3-Embedding-0.6B/*/*.jsonl"
+---
+
+# RAGCompass data
+
+Per-retriever results and passage corpora for the paper *RAGCompass: Generalizable Retriever Routing via Reranking*. Code: [github.com/Graph-COM/RAGCompass](https://github.com/Graph-COM/RAGCompass).
+
+For every query, each of five retrievers returned its top-5 passages, and a fixed reader (`meta-llama/Llama-3.1-8B-Instruct`) answered from them. The F1 of that answer is the utility a retriever router is trained to predict. The data covers 4 datasets x 3 splits x 5 retrievers x 5,000 queries, plus the passage corpus of every split, so routing methods can be trained and evaluated without rerunning retrieval or the reader.
+
+## Download
+
+From the root of a clone of the code repository:
+
+```bash
+hf download Graph-COM/RAGCompass --repo-type dataset --local-dir . \
+  --include "dataset/*" "Llama-3.1-8B-Instruct_Qwen3-Embedding-0.6B/*"
+```
+
+## Layout
+
+```
+Llama-3.1-8B-Instruct_Qwen3-Embedding-0.6B/
+  <dataset>_train/<retriever>.jsonl
+  <dataset>_val/<retriever>.jsonl
+  <dataset>/<retriever>.jsonl              # test split
+dataset/
+  <dataset>[_train|_val]_corpus.json       # list of {"title", "text"}
+```
+
+`<dataset>` is one of `nq`, `musique`, `hotpotqa`, `2wikimultihopqa`. `<retriever>` is one of `rag_1_step` (dense, Qwen3-Embedding-0.6B), `bm25_1_step`, `splade_v3_1_step`, `colbertv2_1_step` or `hipporag2-0.5_1_step` (HippoRAG 2). Every JSONL file has 5,000 rows, one per query, and the five files of a split hold the same queries.
+
+| field | meaning |
+|---|---|
+| `id`, `question`, `answers` | query id, text, gold answers |
+| `top_k_indices`, `top_k_scores` | the retriever's top-5 passages (indices into the split's corpus) and their scores |
+| `pred_ans`, `raw_response` | the reader's extracted answer and full response |
+| `f1` | F1 of `pred_ans` against `answers` |
+| `recall`, `ndcg`, `gold_doc_list` | retrieval quality against the gold passages |
+| `latency` | seconds for retrieval plus generation |
+
+Corpus sizes (passages):
+
+| dataset | train | val | test |
+|---|---|---|---|
+| nq | 19,384 | 19,510 | 19,453 |
+| musique | 45,240 | 45,195 | 48,236 |
+| hotpotqa | 46,077 | 46,074 | 46,353 |
+| 2wikimultihopqa | 25,217 | 24,993 | 26,424 |
+
+The questions and corpora follow the [HippoRAG 2 release](https://huggingface.co/datasets/osunlp/HippoRAG_2).
+
+## License
+
+The data is derived from Natural Questions (CC BY-SA 3.0), HotpotQA (CC BY-SA 4.0), MuSiQue (CC BY 4.0) and 2WikiMultihopQA (Apache 2.0), with passages taken from Wikipedia (CC BY-SA). Each part is redistributed under the license of the dataset it comes from. The reader outputs were generated with Llama 3.1 and are subject to the [Llama 3.1 Community License](https://www.llama.com/llama3_1/license/).
+
+## Citation
+
+```bibtex
+@misc{six2026ragcompass,
+  title  = {RAGCompass: Generalizable Retriever Routing via Reranking},
+  author = {Six, Valentin and Li, Mufei and Fu, Dongqi and Xia, Yinglong and Li, Hong and Li, Pan},
+  year   = {2026},
+  note   = {Preprint}
+}
+```