Add files using upload-large-folder tool

mixbench.py

@@ -3,87 +3,95 @@ import json
 import csv
 import datasets
 
-_DESCRIPTION = """
-MixBench is a benchmark for mixed-modality retrieval across text, image, and image+text corpora.
-"""
-
+_DESCRIPTION = "MixBench: Mixed-Modality Retrieval Benchmark"
 _HOMEPAGE = "https://huggingface.co/datasets/mixed-modality-search/MixBench25"
 
 class MixBench(datasets.GeneratorBasedBuilder):
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name=name, version=datasets.Version("1.0.0"), description=f"{name} subset of MixBench")
+        datasets.BuilderConfig(name=name, version=datasets.Version("1.0.0"), description=f"Subset = {name}")
         for name in ["MSCOCO", "Google_WIT", "VisualNews", "OVEN"]
     ]
 
     def _info(self):
-        # ⚠️ unified superset schema (all splits must use this)
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
-            homepage=_HOMEPAGE,
             features=datasets.Features({
-                "query_id": datasets.Value("string"),
-                "corpus_id": datasets.Value("string"),
+                "id": datasets.Value("string"),
                 "text": datasets.Value("string"),
-                "image": datasets.Value("string"),
-                "score": datasets.Value("int32"),
             }),
+            homepage=_HOMEPAGE,
         )
 
     def _split_generators(self, dl_manager):
-        subset_dir = os.path.join(dl_manager.manual_dir or dl_manager._base_path, self.config.name)
+        name = self.config.name
+        base_dir = os.path.join(dl_manager.manual_dir or dl_manager.download_and_extract(""), name)
 
         return [
-            datasets.SplitGenerator(
-                name="query",
-                gen_kwargs={"path": os.path.join(subset_dir, "queries.jsonl"), "split": "query"},
-            ),
-            datasets.SplitGenerator(
-                name="corpus",
-                gen_kwargs={"path": os.path.join(subset_dir, "corpus.jsonl"), "split": "corpus"},
-            ),
-            datasets.SplitGenerator(
-                name="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(subset_dir, "qrels", "qrels.tsv"), "split": "qrel"},
-            ),
+            datasets.SplitGenerator(name="query", gen_kwargs={"split": "query", "filepath": os.path.join(base_dir, "queries.jsonl")}),
+            datasets.SplitGenerator(name="corpus", gen_kwargs={"split": "corpus", "filepath": os.path.join(base_dir, "corpus.jsonl")}),
+            datasets.SplitGenerator(name="mixed_corpus", gen_kwargs={"split": "mixed_corpus", "filepath": os.path.join(base_dir, "mixed_corpus.jsonl")}),
+            datasets.SplitGenerator(name="qrel", gen_kwargs={"split": "qrel", "filepath": os.path.join(base_dir, "qrels", "qrels.tsv")}),
         ]
 
-    def _generate_examples(self, path, split):
+    def _generate_examples(self, filepath, split):
         if split == "query":
-            with open(path, encoding="utf-8") as f:
+            with open(filepath, encoding="utf-8") as f:
                 for idx, line in enumerate(f):
                     item = json.loads(line)
                     yield idx, {
                         "query_id": item["query_id"],
-                        "corpus_id": "",
                         "text": item["text"],
                         "image": item.get("image", ""),
-                        "score": 0,
                     }
 
         elif split in {"corpus", "mixed_corpus"}:
-            with open(path, encoding="utf-8") as f:
+            with open(filepath, encoding="utf-8") as f:
                 for idx, line in enumerate(f):
                     item = json.loads(line)
                     yield idx, {
-                        "query_id": "",
                         "corpus_id": item["corpus_id"],
                         "text": item["text"],
-                        "image": item["image"],
-                        "score": 0,
+                        "image": item.get("image", ""),
                     }
 
         elif split == "qrel":
-            with open(path, encoding="utf-8") as f:
+            with open(filepath, encoding="utf-8") as f:
                 reader = csv.DictReader(f, delimiter="\t")
                 for idx, row in enumerate(reader):
                     yield idx, {
                         "query_id": row["query_id"],
                         "corpus_id": row["corpus_id"],
-                        "text": "",
-                        "image": "",
                         "score": int(row["score"]),
                     }
+
+    def _info(self):
+        split = datasets.Split(self.config.data_files["split"]) if hasattr(self.config, "data_files") else None
+        if split == "query":
+            features = datasets.Features({
+                "query_id": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "image": datasets.Value("string"),
+            })
+        elif split in {"corpus", "mixed_corpus"}:
+            features = datasets.Features({
+                "corpus_id": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "image": datasets.Value("string"),
+            })
+        elif split == "qrel":
+            features = datasets.Features({
+                "query_id": datasets.Value("string"),
+                "corpus_id": datasets.Value("string"),
+                "score": datasets.Value("int32"),
+            })
+        else:
+            features = datasets.Features({
+                "id": datasets.Value("string"),
+                "text": datasets.Value("string"),
+            })
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+        )