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Google_WIT/qrels/qrels.tsv

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MSCOCO/qrels/qrels.tsv

@@ -0,0 +1,985 @@
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OVEN/qrels/qrels.tsv

@@ -0,0 +1,1001 @@
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README.md

@@ -0,0 +1,99 @@
+---
+license: mit
+pretty_name: MixBench
+task_categories:
+  - text-ranking
+task_ids:
+  - document-retrieval
+language:
+  - en
+multilinguality: monolingual
+annotations_creators:
+  - machine-generated
+dataset_creator: Binxu Li et al.
+dataset_info:
+  features:
+    - name: query_id
+      dtype: string
+    - name: query_text
+      dtype: string
+    - name: query_image
+      dtype: string
+    - name: corpus_id
+      dtype: string
+    - name: corpus_text
+      dtype: string
+    - name: corpus_image
+      dtype: string
+    - name: score
+      dtype: int32
+configs:
+  - config_name: MSCOCO
+    data_files:
+      - MSCOCO/*
+  - config_name: Google_WIT
+    data_files:
+      - Google_WIT/*
+  - config_name: VisualNews
+    data_files:
+      - VisualNews/*
+  - config_name: OVEN
+    data_files:
+      - OVEN/*
+  - config_name: MSCOCO_mixed
+    data_files:
+      - MSCOCO/*
+  - config_name: Google_WIT_mixed
+    data_files:
+      - Google_WIT/*
+  - config_name: VisualNews_mixed
+    data_files:
+      - VisualNews/*
+  - config_name: OVEN_mixed
+    data_files:
+      - OVEN/*
+tags:
+  - retrieval
+  - image
+  - text
+  - multimodal
+  - benchmark
+---
+
+# MixBench: A Benchmark for Mixed Modality Retrieval
+
+**MixBench** is a benchmark for evaluating retrieval across text, images, and multimodal documents. It is designed to test how well retrieval models handle queries and documents that span different modalities, such as pure text, pure images, and combined image+text inputs.
+
+MixBench includes **four subsets**, each curated from a different data source:
+
+- **MSCOCO**
+- **Google_WIT**
+- **VisualNews**
+- **OVEN**
+
+Each subset contains:
+
+- `queries.jsonl`: each entry contains a `query_id`, `text`, and/or `image`  
+- `mixed_corpus.jsonl`: each entry contains a `corpus_id`, a `text` or an `image` or a multimodal document (`text` and `image`) 
+- `qrels.tsv`: a tab-separated list of relevant query-document pairs (`query_id`, `corpus_id`, `score=1`)  
+- `corpus.jsonl`: the original corpus
+
+This benchmark supports diverse retrieval settings including unimodal-to-multimodal and cross-modal search.
+
+---
+
+## 🔄 Load Example
+
+You can load a specific subset of MixBench using the `name` argument:
+
+```python
+from datasets import load_dataset
+
+# Load the MSCOCO subset
+ds_query = load_dataset("andy0207/mixbench", name="MSCOCO", split='query')
+ds_corpus = load_dataset("andy0207/mixbench", name="MSCOCO", split='mixed_corpus')
+ds_query = load_dataset("andy0207/mixbench", name="MSCOCO", split='qrel')
+# Load other subsets (corpus)
+ds_gwit = load_dataset("andy0207/mixbench", name="Google_WIT", split='mixed_corpus')
+ds_news = load_dataset("andy0207/mixbench", name="VisualNews",split='mixed_corpus')
+ds_oven = load_dataset("andy0207/mixbench", name="OVEN", split='mixed_corpus')

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mixbench.py

@@ -0,0 +1,87 @@
+import os
+import json
+import csv
+import datasets
+
+_DESCRIPTION = """
+MixBench is a benchmark for mixed-modality retrieval across text, image, and image+text corpora.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/andy0207/mixbench"
+
+class MixBench(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name=name, version=datasets.Version("1.0.0"), description=f"MixBench subset: {name}")
+        for name in ["MSCOCO", "Google_WIT", "VisualNews", "OVEN"]
+    ]
+
+    def _info(self):
+        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"),
+            })
+        )
+
+    def _split_generators(self, dl_manager):
+        # 确保这个方法存在且正确实现
+        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 == "query":
+            with open(path, encoding="utf-8") as f:
+                for idx, line in enumerate(f):
+                    item = json.loads(line)
+                    yield idx, {
+                        "query_id": item.get("query_id", ""),
+                        "corpus_id": "",
+                        "text": item.get("text", ""),
+                        "image": item.get("image", ""),
+                        "score": 0,
+                    }
+        elif split == "corpus" or split == "mixed_corpus":
+            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):
+                    yield idx, {
+                        "query_id": row["query_id"],
+                        "corpus_id": row["corpus_id"],
+                        "text": "",
+                        "image": "",
+                        "score": int(row["score"])
+                    }