Upload 2 files

FutureQueryEval.py

@@ -0,0 +1,143 @@
+import csv
+import datasets
+
+_CITATION = """\
+@misc{abdallah2025good,
+    title={How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models},
+    author={Abdelrahman Abdallah and Bhawna Piryani and Jamshid Mozafari and Mohammed Ali and Adam Jatowt},
+    year={2025},
+    eprint={2508.16757},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+FutureQueryEval is a novel IR benchmark comprising 148 queries with 2,938 query-document pairs 
+across 7 topical categories, designed to evaluate reranker performance on temporal novelty.
+All queries refer to events after April 2025 to ensure zero contamination with LLM pretraining data.
+"""
+
+_HOMEPAGE = "https://github.com/DataScienceUIBK/llm-reranking-generalization-study"
+
+_LICENSE = "Apache-2.0"
+
+_URLS = {
+    "queries": "queries.csv",
+    "corpus": "corpus.tsv", 
+    "qrels": "qrels.txt",
+}
+
+class FutureQueryEval(datasets.GeneratorBasedBuilder):
+    """FutureQueryEval dataset for temporal IR evaluation."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="queries",
+            version=VERSION,
+            description="Query collection with categories",
+        ),
+        datasets.BuilderConfig(
+            name="corpus", 
+            version=VERSION,
+            description="Document corpus",
+        ),
+        datasets.BuilderConfig(
+            name="qrels",
+            version=VERSION, 
+            description="Relevance judgments",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "queries"
+
+    def _info(self):
+        if self.config.name == "queries":
+            features = datasets.Features({
+                "query_id": datasets.Value("string"),
+                "query_text": datasets.Value("string"), 
+                "category": datasets.Value("string"),
+            })
+        elif self.config.name == "corpus":
+            features = datasets.Features({
+                "doc_id": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "url": datasets.Value("string"),
+            })
+        elif self.config.name == "qrels":
+            features = datasets.Features({
+                "query_id": datasets.Value("string"),
+                "iteration": datasets.Value("int32"),
+                "doc_id": datasets.Value("string"), 
+                "relevance": datasets.Value("int32"),
+            })
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download(_URLS)
+        
+        if self.config.name == "queries":
+            return [
+                datasets.SplitGenerator(
+                    name="queries",
+                    gen_kwargs={"filepath": downloaded_files["queries"]},
+                ),
+            ]
+        elif self.config.name == "corpus":
+            return [
+                datasets.SplitGenerator(
+                    name="corpus", 
+                    gen_kwargs={"filepath": downloaded_files["corpus"]},
+                ),
+            ]
+        elif self.config.name == "qrels":
+            return [
+                datasets.SplitGenerator(
+                    name="qrels",
+                    gen_kwargs={"filepath": downloaded_files["qrels"]},
+                ),
+            ]
+
+    def _generate_examples(self, filepath):
+        if self.config.name == "queries":
+            with open(filepath, encoding="utf-8") as f:
+                reader = csv.DictReader(f, delimiter=",")
+                for key, row in enumerate(reader):
+                    yield key, {
+                        "query_id": row["query_id"],
+                        "query_text": row["query_text"],
+                        "category": row["category"],
+                    }
+                    
+        elif self.config.name == "corpus":
+            with open(filepath, encoding="utf-8") as f:
+                reader = csv.DictReader(f, delimiter="\t")
+                for key, row in enumerate(reader):
+                    yield key, {
+                        "doc_id": row["doc_id"],
+                        "title": row["title"], 
+                        "text": row["text"],
+                        "url": row["url"],
+                    }
+                    
+        elif self.config.name == "qrels":
+            with open(filepath, encoding="utf-8") as f:
+                for key, line in enumerate(f):
+                    parts = line.strip().split()
+                    if len(parts) == 4:
+                        yield key, {
+                            "query_id": parts[0],
+                            "iteration": int(parts[1]),
+                            "doc_id": parts[2], 
+                            "relevance": int(parts[3]),
+                        }

dataset_infos.json

@@ -0,0 +1,111 @@
+{
+  "queries": {
+    "description": "FutureQueryEval query collection with 148 queries across 7 categories",
+    "citation": "@misc{abdallah2025good, title={How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models}, author={Abdelrahman Abdallah and Bhawna Piryani and Jamshid Mozafari and Mohammed Ali and Adam Jatowt}, year={2025}, eprint={2508.16757}, archivePrefix={arXiv}, primaryClass={cs.CL}}",
+    "homepage": "https://github.com/DataScienceUIBK/llm-reranking-generalization-study",
+    "license": "Apache-2.0",
+    "features": {
+      "query_id": {
+        "dtype": "string",
+        "id": null,
+        "_type": "Value"
+      },
+      "query_text": {
+        "dtype": "string", 
+        "id": null,
+        "_type": "Value"
+      },
+      "category": {
+        "dtype": "string",
+        "id": null,
+        "_type": "Value"
+      }
+    },
+    "splits": {
+      "queries": {
+        "name": "queries",
+        "num_bytes": 45056,
+        "num_examples": 148,
+        "dataset_name": "future_query_eval"
+      }
+    },
+    "download_size": 45056,
+    "dataset_size": 45056
+  },
+  "corpus": {
+    "description": "FutureQueryEval document corpus with 2,787 documents",
+    "citation": "@misc{abdallah2025good, title={How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models}, author={Abdelrahman Abdallah and Bhawna Piryani and Jamshid Mozafari and Mohammed Ali and Adam Jatowt}, year={2025}, eprint={2508.16757}, archivePrefix={arXiv}, primaryClass={cs.CL}}",
+    "homepage": "https://github.com/DataScienceUIBK/llm-reranking-generalization-study",
+    "license": "Apache-2.0",
+    "features": {
+      "doc_id": {
+        "dtype": "string",
+        "id": null,
+        "_type": "Value"
+      },
+      "title": {
+        "dtype": "string",
+        "id": null, 
+        "_type": "Value"
+      },
+      "text": {
+        "dtype": "string",
+        "id": null,
+        "_type": "Value"
+      },
+      "url": {
+        "dtype": "string",
+        "id": null,
+        "_type": "Value"
+      }
+    },
+    "splits": {
+      "corpus": {
+        "name": "corpus",
+        "num_bytes": 964608,
+        "num_examples": 2787,
+        "dataset_name": "future_query_eval"
+      }
+    },
+    "download_size": 964608,
+    "dataset_size": 964608
+  },
+  "qrels": {
+    "description": "FutureQueryEval relevance judgments with 2,938 query-document pairs",
+    "citation": "@misc{abdallah2025good, title={How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models}, author={Abdelrahman Abdallah and Bhawna Piryani and Jamshid Mozafari and Mohammed Ali and Adam Jatowt}, year={2025}, eprint={2508.16757}, archivePrefix={arXiv}, primaryClass={cs.CL}}",
+    "homepage": "https://github.com/DataScienceUIBK/llm-reranking-generalization-study", 
+    "license": "Apache-2.0",
+    "features": {
+      "query_id": {
+        "dtype": "string",
+        "id": null,
+        "_type": "Value"
+      },
+      "iteration": {
+        "dtype": "int32",
+        "id": null,
+        "_type": "Value"
+      },
+      "doc_id": {
+        "dtype": "string", 
+        "id": null,
+        "_type": "Value"
+      },
+      "relevance": {
+        "dtype": "int32",
+        "id": null,
+        "_type": "Value"
+      }
+    },
+    "splits": {
+      "qrels": {
+        "name": "qrels",
+        "num_bytes": 99328,
+        "num_examples": 2938,
+        "dataset_name": "future_query_eval"
+      }
+    },
+    "download_size": 99328,
+    "dataset_size": 99328
+  }
+}