Datasets:
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license: mit
pretty_name: MixBench
task_categories:
- text-ranking
task_ids:
- document-retrieval
language:
- en
multilinguality: monolingual
annotations_creators:
- machine-generated
dataset_creator: anonymous
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/*
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("mixed-modality-search/MixBench2025", name="MSCOCO", split='query')
ds_corpus = load_dataset("mixed-modality-search/MixBench2025", name="MSCOCO", split='mixed_corpus')
ds_query = load_dataset("mixed-modality-search/MixBench2025", name="MSCOCO", split='qrel')
# Load other subsets (corpus)
ds_gwit = load_dataset("mixed-modality-search/MixBench2025", name="Google_WIT", split='mixed_corpus')
ds_news = load_dataset("mixed-modality-search/MixBench2025", name="VisualNews",split='mixed_corpus')
ds_oven = load_dataset("mixed-modality-search/MixBench2025", name="OVEN", split='mixed_corpus')
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