Remove custom dataset script

FutureQueryEval.py

@@ -1,143 +0,0 @@
-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]),
-                        }