dataset: Add REAL_MM_RAG benchmark

mteb/benchmarks/benchmarks/__init__.py

@@ -36,6 +36,7 @@
     MTEB_RETRIEVAL_WITH_INSTRUCTIONS,
     NANOBEIR,
     R2MED,
+    REAL_MM_RAG,
     RU_SCI_BENCH,
     SEB,
     VIDORE,
@@ -116,4 +117,5 @@
     "RTEB_GERMAN",
     "RTEB_JAPANESE",
     "HUME",
+    "REAL_MM_RAG",
 ]

mteb/benchmarks/benchmarks/benchmarks.py

@@ -2302,6 +2302,30 @@
 }""",
 )
 
+REAL_MM_RAG = Benchmark(
+    name="REAL_MM_RAG",
+    display_name="IBM Visual Document Retrieval",
+    tasks=get_tasks(
+        tasks=[
+            "RealMMRagFinReportRetrieval",
+            "RealMMRagFinSlidesRetrieval",
+            "RealMMRagTechReportRetrieval",
+            "RealMMRagTechSlidesRetrieval",
+        ],
+    ),
+    description="""REAL-MM-RAG is a realistic benchmark that reflects real-world multi-modal document retrieval challenges.
+                It includes infographic-rich documents such as slides, reports, and technical manuals with tables, charts, and figures, requiring models to integrate textual and visual evidence.
+                The benchmark features multi-modality, realistic queries, and accurate labeling for comprehensive evaluation.""",
+    reference="https://arxiv.org/abs/2502.12342",
+    citation=r"""
+@article{wasserman2025real,
+  author = {Wasserman, Navve and Pony, Roi and Naparstek, Oshri and Goldfarb, Adi Raz and Schwartz, Eli and Barzelay, Udi and Karlinsky, Leonid},
+  journal = {arXiv preprint arXiv:2502.12342},
+  title = {REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark},
+  year = {2025},
+}
+""",
+)
 
 HUME = HUMEBenchmark(
     name="HUME(v1)",

mteb/tasks/Image/Any2AnyRetrieval/__init__.py

@@ -29,6 +29,7 @@
 from .eng.OKVQAIT2TRetrieval import *
 from .eng.OVENIT2ITRetrieval import *
 from .eng.OVENIT2TRetrieval import *
+from .eng.RealMMRagBenchRetrieval import *
 from .eng.ReMuQIT2TRetrieval import *
 from .eng.ROxfordI2IRetrieval import *
 from .eng.RP2kI2IRetrieval import *

mteb/tasks/Image/Any2AnyRetrieval/eng/RealMMRagBenchRetrieval.py

@@ -0,0 +1,309 @@
+from __future__ import annotations
+
+from datasets import load_dataset
+
+from mteb.abstasks.Image.AbsTaskAny2AnyRetrieval import AbsTaskAny2AnyRetrieval
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+
+def _load_data(
+    path: str,
+    splits: str,
+    cache_dir: str | None = None,
+    revision: str | None = None,
+    text_col: str = "query",
+):
+    corpus = {}
+    queries = {}
+    relevant_docs = {}
+
+    for split in splits:
+        query_ds = load_dataset(
+            path,
+            "queries",
+            split=split,
+            cache_dir=cache_dir,
+            revision=revision,
+        )
+        query_ds = query_ds.map(
+            lambda x: {
+                "id": f"query-{split}-{x['query-id']}",
+                "text": x[text_col],
+                "image": None,
+                "modality": "text",
+            },
+            remove_columns=["query-id", "query"],
+        )
+
+        corpus_ds = load_dataset(
+            path,
+            "corpus",
+            split=split,
+            cache_dir=cache_dir,
+            revision=revision,
+        )
+        corpus_ds = corpus_ds.map(
+            lambda x: {
+                "id": f"corpus-{split}-{x['corpus-id']}",
+                "text": None,
+                "modality": "image",
+            },
+            remove_columns=["corpus-id"],
+        )
+
+        qrels_ds = load_dataset(
+            path,
+            "qrels",
+            split=split,
+            cache_dir=cache_dir,
+            revision=revision,
+        )
+
+        queries[split] = query_ds
+        corpus[split] = corpus_ds
+        relevant_docs[split] = {}
+        for row in qrels_ds:
+            qid = f"query-{split}-{row['query-id']}"
+            did = f"corpus-{split}-{row['corpus-id']}"
+            if qid not in relevant_docs[split]:
+                relevant_docs[split][qid] = {}
+            relevant_docs[split][qid][did] = int(row["score"])
+
+    return corpus, queries, relevant_docs
+
+
+class RealMMRagFinReportRetrieval(AbsTaskAny2AnyRetrieval):
+    metadata = TaskMetadata(
+        name="RealMMRagFinReportRetrieval",
+        description="""Contains annual financial reports rich in text, tables, and figures from IBM’s public filings.
+                    Queries ask about financial results, trends, or statements across multiple years.
+                    Retrieval goal: find the specific report page containing the relevant financial information.""",
+        reference="https://arxiv.org/abs/2502.12342",
+        dataset={
+            "path": "ibm-research/REAL-MM-RAG_FinReport_BEIR",
+            "revision": "e66ef8cc883d823483db7b5b71065eb7c1dae12c",
+        },
+        type="DocumentUnderstanding",
+        category="t2i",
+        eval_splits=["test"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_5",
+        date=("2025-01-01", "2025-07-01"),
+        domains=["Academic"],
+        task_subtypes=["Image Text Retrieval"],
+        license="cdla-sharing-1.0",
+        annotations_creators="derived",
+        dialect=[],
+        modalities=["text", "image"],
+        sample_creation="found",
+        bibtex_citation=r"""
+@article{wasserman2025real,
+  author = {Wasserman, Navve and Pony, Roi and Naparstek, Oshri and Goldfarb, Adi Raz and Schwartz, Eli and Barzelay, Udi and Karlinsky, Leonid},
+  journal = {arXiv preprint arXiv:2502.12342},
+  title = {REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark},
+  year = {2025},
+}
+""",
+        prompt={"query": "Find a screenshot that relevant to the user's question."},
+        descriptive_stats={
+            "n_samples": None,
+            "avg_character_length": {
+                "test": {
+                    "average_document_length": 141.5,
+                    "num_documents": 19,
+                    "num_queries": 853,
+                    "average_relevant_docs_per_query": 1.0,
+                }
+            },
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus, self.queries, self.relevant_docs = _load_data(
+            path=self.metadata_dict["dataset"]["path"],
+            splits=self.metadata_dict["eval_splits"],
+            cache_dir=kwargs.get("cache_dir", None),
+            revision=self.metadata_dict["dataset"]["revision"],
+        )
+
+        self.data_loaded = True
+
+
+class RealMMRagFinSlidesRetrieval(AbsTaskAny2AnyRetrieval):
+    metadata = TaskMetadata(
+        name="RealMMRagFinSlidesRetrieval",
+        description="""Comprises quarterly investor presentation slides combining tables, charts, and textual highlights.
+                    Queries focus on revenue trends, growth metrics, or business segments.
+                    Retrieval goal: retrieve the slide that presents the requested financial data or insight.""",
+        reference="https://arxiv.org/abs/2502.12342",
+        dataset={
+            "path": "ibm-research/REAL-MM-RAG_FinSlides_BEIR",
+            "revision": "41167605aed3ab0ff342ac8f318163c6e59b8b31",
+        },
+        type="DocumentUnderstanding",
+        category="t2i",
+        eval_splits=["test"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_5",
+        date=("2025-01-01", "2025-07-01"),
+        domains=["Academic"],
+        task_subtypes=["Image Text Retrieval"],
+        license="cdla-sharing-1.0",
+        annotations_creators="derived",
+        dialect=[],
+        modalities=["text", "image"],
+        sample_creation="found",
+        bibtex_citation=r"""
+@article{wasserman2025real,
+  author = {Wasserman, Navve and Pony, Roi and Naparstek, Oshri and Goldfarb, Adi Raz and Schwartz, Eli and Barzelay, Udi and Karlinsky, Leonid},
+  journal = {arXiv preprint arXiv:2502.12342},
+  title = {REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark},
+  year = {2025},
+}
+""",
+        prompt={"query": "Find a screenshot that relevant to the user's question."},
+        descriptive_stats={
+            "n_samples": None,
+            "avg_character_length": {
+                "test": {
+                    "average_document_length": 35,
+                    "num_documents": 65,
+                    "num_queries": 1052,
+                    "average_relevant_docs_per_query": 1.0,
+                }
+            },
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus, self.queries, self.relevant_docs = _load_data(
+            path=self.metadata_dict["dataset"]["path"],
+            splits=self.metadata_dict["eval_splits"],
+            cache_dir=kwargs.get("cache_dir", None),
+            revision=self.metadata_dict["dataset"]["revision"],
+        )
+        self.data_loaded = True
+
+
+class RealMMRagTechReportRetrieval(AbsTaskAny2AnyRetrieval):
+    metadata = TaskMetadata(
+        name="RealMMRagTechReportRetrieval",
+        description="""Includes technical documentation and whitepapers on IBM storage and automation systems with text-heavy content and supporting visuals.
+                    Queries address specific technologies, architectures, or performance aspects.
+                    Retrieval goal: locate the report page providing the technical explanation or result.""",
+        reference="https://arxiv.org/abs/2502.12342",
+        dataset={
+            "path": "ibm-research/REAL-MM-RAG_TechReport_BEIR",
+            "revision": "13642f1f8d39e032757f4d0ee73814452fc76d17",
+        },
+        type="DocumentUnderstanding",
+        category="t2i",
+        eval_splits=["test"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_5",
+        date=("2025-01-01", "2025-07-01"),
+        domains=["Academic"],
+        task_subtypes=["Image Text Retrieval"],
+        license="cdla-sharing-1.0",
+        annotations_creators="derived",
+        dialect=[],
+        modalities=["text", "image"],
+        sample_creation="found",
+        bibtex_citation=r"""
+@article{wasserman2025real,
+  author = {Wasserman, Navve and Pony, Roi and Naparstek, Oshri and Goldfarb, Adi Raz and Schwartz, Eli and Barzelay, Udi and Karlinsky, Leonid},
+  journal = {arXiv preprint arXiv:2502.12342},
+  title = {REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark},
+  year = {2025},
+}
+""",
+        prompt={"query": "Find a screenshot that relevant to the user's question."},
+        descriptive_stats={
+            "n_samples": None,
+            "avg_character_length": {
+                "test": {
+                    "average_document_length": 98.5,
+                    "num_documents": 17,
+                    "num_queries": 1294,
+                    "average_relevant_docs_per_query": 1.0,
+                }
+            },
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus, self.queries, self.relevant_docs = _load_data(
+            path=self.metadata_dict["dataset"]["path"],
+            splits=self.metadata_dict["eval_splits"],
+            cache_dir=kwargs.get("cache_dir", None),
+            revision=self.metadata_dict["dataset"]["revision"],
+        )
+        self.data_loaded = True
+
+
+class RealMMRagTechSlidesRetrieval(AbsTaskAny2AnyRetrieval):
+    metadata = TaskMetadata(
+        name="RealMMRagTechSlidesRetrieval",
+        description="""Features technical presentation slides containing bullet points, flow diagrams, and schematic figures.
+                    Queries reflect realistic information-seeking about system design or AI and automation concepts.
+                    Retrieval goal: retrieve the slide that best answers the technical query through text and visuals.""",
+        reference="https://arxiv.org/abs/2502.12342",
+        dataset={
+            "path": "ibm-research/REAL-MM-RAG_TechSlides_BEIR",
+            "revision": "614ad5cac2edd86756045f04075d335a3825a692",
+        },
+        type="DocumentUnderstanding",
+        category="t2i",
+        eval_splits=["test"],
+        eval_langs=["eng-Latn"],
+        main_score="ndcg_at_5",
+        date=("2025-01-01", "2025-07-01"),
+        domains=["Academic"],
+        task_subtypes=["Image Text Retrieval"],
+        license="cdla-sharing-1.0",
+        annotations_creators="derived",
+        dialect=[],
+        modalities=["text", "image"],
+        sample_creation="found",
+        bibtex_citation=r"""
+@article{wasserman2025real,
+  author = {Wasserman, Navve and Pony, Roi and Naparstek, Oshri and Goldfarb, Adi Raz and Schwartz, Eli and Barzelay, Udi and Karlinsky, Leonid},
+  journal = {arXiv preprint arXiv:2502.12342},
+  title = {REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark},
+  year = {2025},
+}
+""",
+        prompt={"query": "Find a screenshot that relevant to the user's question."},
+        descriptive_stats={
+            "n_samples": None,
+            "avg_character_length": {
+                "test": {
+                    "average_document_length": 31.7,
+                    "num_documents": 62,
+                    "num_queries": 1354,
+                    "average_relevant_docs_per_query": 1.0,
+                }
+            },
+        },
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus, self.queries, self.relevant_docs = _load_data(
+            path=self.metadata_dict["dataset"]["path"],
+            splits=self.metadata_dict["eval_splits"],
+            cache_dir=kwargs.get("cache_dir", None),
+            revision=self.metadata_dict["dataset"]["revision"],
+        )
+        self.data_loaded = True