mixed-modality-search commited on
Commit
b6a826f
·
verified ·
1 Parent(s): c00e148

Add files using upload-large-folder tool

Browse files
Files changed (1) hide show
  1. mixbench.py +17 -29
mixbench.py CHANGED
@@ -1,35 +1,32 @@
1
- #
2
  import os
3
  import json
4
  import csv
5
  import datasets
6
 
7
  _DESCRIPTION = """
8
- MixBench is a benchmark for mixed-modality retrieval.
9
  """
10
 
11
  _HOMEPAGE = "https://huggingface.co/datasets/mixed-modality-search/MixBench25"
12
 
13
- # Define config for each dataset
14
- class MixBenchConfig(datasets.BuilderConfig):
15
- def __init__(self, **kwargs):
16
- super().__init__(**kwargs)
17
-
18
  class MixBench(datasets.GeneratorBasedBuilder):
19
- BUILDER_CONFIG_CLASS = MixBenchConfig
20
  BUILDER_CONFIGS = [
21
- MixBenchConfig(name="MSCOCO", version=datasets.Version("1.0.0"), description="MSCOCO subset"),
22
- MixBenchConfig(name="Google_WIT", version=datasets.Version("1.0.0"), description="Google WIT subset"),
23
- MixBenchConfig(name="VisualNews", version=datasets.Version("1.0.0"), description="VisualNews subset"),
24
- MixBenchConfig(name="OVEN", version=datasets.Version("1.0.0"), description="OVEN subset"),
25
  ]
26
 
27
  def _info(self):
28
- # Return a default placeholder schema
29
  return datasets.DatasetInfo(
30
  description=_DESCRIPTION,
31
  homepage=_HOMEPAGE,
32
- features=datasets.Features({}),
 
 
 
 
 
 
33
  )
34
 
35
  def _split_generators(self, dl_manager):
@@ -56,46 +53,37 @@ class MixBench(datasets.GeneratorBasedBuilder):
56
 
57
  def _generate_examples(self, path, split):
58
  if split == "query":
59
- self.info.features = datasets.Features({
60
- "query_id": datasets.Value("string"),
61
- "text": datasets.Value("string"),
62
- "image": datasets.Value("string"),
63
- })
64
  with open(path, encoding="utf-8") as f:
65
  for idx, line in enumerate(f):
66
  item = json.loads(line)
67
  yield idx, {
68
  "query_id": item["query_id"],
 
69
  "text": item["text"],
70
  "image": item.get("image", ""),
 
71
  }
72
 
73
  elif split in {"corpus", "mixed_corpus"}:
74
- self.info.features = datasets.Features({
75
- "corpus_id": datasets.Value("string"),
76
- "text": datasets.Value("string"),
77
- "image": datasets.Value("string"),
78
- })
79
  with open(path, encoding="utf-8") as f:
80
  for idx, line in enumerate(f):
81
  item = json.loads(line)
82
  yield idx, {
 
83
  "corpus_id": item["corpus_id"],
84
  "text": item["text"],
85
  "image": item["image"],
 
86
  }
87
 
88
  elif split == "qrel":
89
- self.info.features = datasets.Features({
90
- "query_id": datasets.Value("string"),
91
- "corpus_id": datasets.Value("string"),
92
- "score": datasets.Value("int32"),
93
- })
94
  with open(path, encoding="utf-8") as f:
95
  reader = csv.DictReader(f, delimiter="\t")
96
  for idx, row in enumerate(reader):
97
  yield idx, {
98
  "query_id": row["query_id"],
99
  "corpus_id": row["corpus_id"],
 
 
100
  "score": int(row["score"]),
101
  }
 
 
1
  import os
2
  import json
3
  import csv
4
  import datasets
5
 
6
  _DESCRIPTION = """
7
+ MixBench is a benchmark for mixed-modality retrieval across text, image, and image+text corpora.
8
  """
9
 
10
  _HOMEPAGE = "https://huggingface.co/datasets/mixed-modality-search/MixBench25"
11
 
 
 
 
 
 
12
  class MixBench(datasets.GeneratorBasedBuilder):
 
13
  BUILDER_CONFIGS = [
14
+ datasets.BuilderConfig(name=name, version=datasets.Version("1.0.0"), description=f"{name} subset of MixBench")
15
+ for name in ["MSCOCO", "Google_WIT", "VisualNews", "OVEN"]
 
 
16
  ]
17
 
18
  def _info(self):
19
+ # ⚠️ unified superset schema (all splits must use this)
20
  return datasets.DatasetInfo(
21
  description=_DESCRIPTION,
22
  homepage=_HOMEPAGE,
23
+ features=datasets.Features({
24
+ "query_id": datasets.Value("string"),
25
+ "corpus_id": datasets.Value("string"),
26
+ "text": datasets.Value("string"),
27
+ "image": datasets.Value("string"),
28
+ "score": datasets.Value("int32"),
29
+ }),
30
  )
31
 
32
  def _split_generators(self, dl_manager):
 
53
 
54
  def _generate_examples(self, path, split):
55
  if split == "query":
 
 
 
 
 
56
  with open(path, encoding="utf-8") as f:
57
  for idx, line in enumerate(f):
58
  item = json.loads(line)
59
  yield idx, {
60
  "query_id": item["query_id"],
61
+ "corpus_id": "",
62
  "text": item["text"],
63
  "image": item.get("image", ""),
64
+ "score": 0,
65
  }
66
 
67
  elif split in {"corpus", "mixed_corpus"}:
 
 
 
 
 
68
  with open(path, encoding="utf-8") as f:
69
  for idx, line in enumerate(f):
70
  item = json.loads(line)
71
  yield idx, {
72
+ "query_id": "",
73
  "corpus_id": item["corpus_id"],
74
  "text": item["text"],
75
  "image": item["image"],
76
+ "score": 0,
77
  }
78
 
79
  elif split == "qrel":
 
 
 
 
 
80
  with open(path, encoding="utf-8") as f:
81
  reader = csv.DictReader(f, delimiter="\t")
82
  for idx, row in enumerate(reader):
83
  yield idx, {
84
  "query_id": row["query_id"],
85
  "corpus_id": row["corpus_id"],
86
+ "text": "",
87
+ "image": "",
88
  "score": int(row["score"]),
89
  }