Add files using upload-large-folder tool

MixBench25.py

@@ -1,113 +1,17 @@
-# import os
-# import json
-# import datasets
-# import csv 
-
-# _DESCRIPTION = """\
-# MixBench is a benchmark for evaluating mixed-modality retrieval. It contains queries and corpora from four datasets: MSCOCO, Google_WIT, VisualNews, and OVEN. \
-# Each subset provides: query, corpus, mixed_corpus, and qrel splits.
-# """
-
-
-# _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 = [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,
-#         )
-
-#     def _split_generators(self, dl_manager):
-#         # 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)
-#         # repo_root = dl_manager.manual_dir or os.path.join(os.path.dirname(__file__), self.config.name)
-#         # subset_dir = os.path.join(repo_root, self.config.name)
-#         subset_dir = os.path.join(dl_manager.manual_dir or dl_manager._base_path, self.config.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"},
-#             ),
-#         ]
-
-#     def _generate_examples(self, path, split):
-#         if split == "qrel":
-#             # with open(path, encoding="utf-8") as f:
-#             #     for idx, line in enumerate(f):
-#             #         qid, did, score = line.strip().split()
-#             #         yield idx, {
-#             #             "query_id": qid,
-#             #             "corpus_id": did,
-#             #             "text": "",
-#             #             "image": "",
-#             #             "score": int(score),
-#             #         }
-#             with open(path, encoding="utf-8") as f:
-#                 reader = csv.DictReader(f, delimiter="\t")  # 使用 DictReader 读取有表头的 tsv
-#                 for idx, row in enumerate(reader):
-#                     yield idx, {
-#                         "query_id": row["query_id"],
-#                         "corpus_id": row["corpus_id"],
-#                         "score": int(row["score"]),
-#                     }
-#         else:
-#             with open(path, encoding="utf-8") as f:
-#                 for idx, line in enumerate(f):
-#                     row = json.loads(line)
-#                     yield idx, {
-#                         "query_id": row.get("query_id", ""),
-#                         "corpus_id": row.get("corpus_id", ""),
-#                         "text": row.get("text", ""),
-#                         "image": row.get("image", ""),
-#                         "score": 0,
-#                     }
-
 import os
 import json
 import datasets
-import csv
+import csv 
 
-_DESCRIPTION = """
-MixBench is a benchmark for evaluating mixed-modality retrieval. It contains queries and corpora from four datasets: MSCOCO, Google_WIT, VisualNews, and OVEN.
+_DESCRIPTION = """\
+MixBench is a benchmark for evaluating mixed-modality retrieval. It contains queries and corpora from four datasets: MSCOCO, Google_WIT, VisualNews, and OVEN. \
 Each subset provides: query, corpus, mixed_corpus, and qrel splits.
 """
 
+
 _HOMEPAGE = "https://huggingface.co/datasets/mixed-modality-search/MixBench25"
 
+
 _SUBSETS = ["MSCOCO", "Google_WIT", "VisualNews", "OVEN"]
 
 class MixBenchConfig(datasets.BuilderConfig):
@@ -116,22 +20,64 @@ class MixBenchConfig(datasets.BuilderConfig):
             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 = [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,
+        )
+
     def _split_generators(self, dl_manager):
+        # 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)
+        # repo_root = dl_manager.manual_dir or os.path.join(os.path.dirname(__file__), self.config.name)
+        # subset_dir = os.path.join(repo_root, self.config.name)
         subset_dir = os.path.join(dl_manager.manual_dir or dl_manager._base_path, self.config.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={"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"},
+            ),
         ]
 
     def _generate_examples(self, path, split):
         if split == "qrel":
+            # with open(path, encoding="utf-8") as f:
+            #     for idx, line in enumerate(f):
+            #         qid, did, score = line.strip().split()
+            #         yield idx, {
+            #             "query_id": qid,
+            #             "corpus_id": did,
+            #             "text": "",
+            #             "image": "",
+            #             "score": int(score),
+            #         }
             with open(path, encoding="utf-8") as f:
-                reader = csv.DictReader(f, delimiter="\t")
+                reader = csv.DictReader(f, delimiter="\t")  # 使用 DictReader 读取有表头的 tsv
                 for idx, row in enumerate(reader):
                     yield idx, {
                         "query_id": row["query_id"],
@@ -142,29 +88,10 @@ class MixBench(datasets.GeneratorBasedBuilder):
             with open(path, encoding="utf-8") as f:
                 for idx, line in enumerate(f):
                     row = json.loads(line)
-                    if split == "query":
-                        yield idx, {
-                            "query_id": row["query_id"],
-                            "text": row.get("text", ""),
-                            "image": row.get("image", ""),
-                        }
-                    else:  # corpus or mixed_corpus
-                        yield idx, {
-                            "corpus_id": row["corpus_id"],
-                            "text": row.get("text", ""),
-                            "image": row.get("image", ""),
-                        }
-
-    def _info(self):
-        # Provide all possible features but only used in relevant splits
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            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"),
-            })
-        )
+                    yield idx, {
+                        "query_id": row.get("query_id", ""),
+                        "corpus_id": row.get("corpus_id", ""),
+                        "text": row.get("text", ""),
+                        "image": row.get("image", ""),
+                        "score": 0,
+                    }