Add files using upload-large-folder tool

MixBench25.py

@@ -1,96 +1,80 @@
 import os
 import json
-import csv
 import datasets
 
-_DESCRIPTION = """
-MixBench is a benchmark for mixed-modality retrieval across text, image, and image+text corpora.
-Each config corresponds to a dataset (e.g., MSCOCO), and defines splits: query, corpus, mixed_corpus, and qrel.
-"""
-
 _HOMEPAGE = "https://huggingface.co/datasets/mixed-modality-search/MixBench25"
 
+
 _SUBSETS = ["MSCOCO", "Google_WIT", "VisualNews", "OVEN"]
 
+class MixBenchConfig(datasets.BuilderConfig):
+    def __init__(self, name, **kwargs):
+        if name not in _SUBSETS:
+            raise ValueError(f"Unknown subset: {name}. Choose from {_SUBSETS}")
+        super().__init__(name=name, version=datasets.Version("1.0.0"), **kwargs)
+
+
 class MixBench(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name=subset,
-            version=datasets.Version("1.0.0"),
-            description=f"MixBench subset: {subset}"
-        ) for subset in _SUBSETS
-    ]
+    BUILDER_CONFIGS = [MixBenchConfig(name=subset) for subset in _SUBSETS]
 
     def _info(self):
+        features = datasets.Features({
+            "query_id": datasets.Value("string"),
+            "corpus_id": datasets.Value("string"),
+            "text": datasets.Value("string"),
+            "image": datasets.Value("string"),
+            "score": datasets.Value("int32"),
+        })
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
+            features=features,
             homepage=_HOMEPAGE,
-            features=datasets.Features({
-                "query_id": datasets.Value("string"),
-                "corpus_id": datasets.Value("string"),
-                "text": datasets.Value("string"),
-                "image": datasets.Value("string"),
-                "score": datasets.Value("int32"),
-            }),
         )
 
     def _split_generators(self, dl_manager):
-        subset = self.config.name
-
-        # 自动解析 repo 内路径
-        data_dir = os.path.join(dl_manager.download_and_extract(f"{subset}"))
+        # download entire repo root and go to current subset folder
+        data_dir = dl_manager.download_and_extract(".")
+        subset_dir = os.path.join(data_dir, self.config.name)
 
         return [
             datasets.SplitGenerator(
                 name="query",
-                gen_kwargs={"path": os.path.join(data_dir, "queries.jsonl"), "split": "query"},
+                gen_kwargs={"path": os.path.join(subset_dir, "queries.jsonl"), "split": "query"},
             ),
             datasets.SplitGenerator(
                 name="corpus",
-                gen_kwargs={"path": os.path.join(data_dir, "corpus.jsonl"), "split": "corpus"},
+                gen_kwargs={"path": os.path.join(subset_dir, "corpus.jsonl"), "split": "corpus"},
             ),
             datasets.SplitGenerator(
                 name="mixed_corpus",
-                gen_kwargs={"path": os.path.join(data_dir, "mixed_corpus.jsonl"), "split": "mixed_corpus"},
+                gen_kwargs={"path": os.path.join(subset_dir, "mixed_corpus.jsonl"), "split": "mixed_corpus"},
             ),
             datasets.SplitGenerator(
                 name="qrel",
-                gen_kwargs={"path": os.path.join(data_dir, "qrels", "qrels.tsv"), "split": "qrel"},
+                gen_kwargs={"path": os.path.join(subset_dir, "qrels", "qrels.tsv"), "split": "qrel"},
             ),
         ]
 
-
     def _generate_examples(self, path, split):
-        if split == "query":
+        if split == "qrel":
             with open(path, encoding="utf-8") as f:
                 for idx, line in enumerate(f):
-                    item = json.loads(line)
+                    qid, _, did, score = line.strip().split()
                     yield idx, {
-                        "query_id": item.get("query_id", ""),
-                        "corpus_id": "",
-                        "text": item.get("text", ""),
-                        "image": item.get("image", ""),
-                        "score": 0,
+                        "query_id": qid,
+                        "corpus_id": did,
+                        "text": "",
+                        "image": "",
+                        "score": int(score),
                     }
-        elif split in {"corpus", "mixed_corpus"}:
+        else:
             with open(path, encoding="utf-8") as f:
                 for idx, line in enumerate(f):
-                    item = json.loads(line)
-                    yield idx, {
-                        "query_id": "",
-                        "corpus_id": item.get("corpus_id", ""),
-                        "text": item.get("text", ""),
-                        "image": item.get("image", ""),
-                        "score": 0,
-                    }
-        elif split == "qrel":
-            with open(path, encoding="utf-8") as f:
-                reader = csv.DictReader(f, delimiter="\t")
-                for idx, row in enumerate(reader):
+                    row = json.loads(line)
                     yield idx, {
                         "query_id": row.get("query_id", ""),
                         "corpus_id": row.get("corpus_id", ""),
-                        "text": "",
-                        "image": "",
-                        "score": int(row.get("score", 0)),
+                        "text": row.get("text", ""),
+                        "image": row.get("image", ""),
+                        "score": 0,
                     }