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.

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🔄 Load Example

You can load a specific subset of MixBench using the name argument:

from datasets import load_dataset

# Load the MSCOCO subset
ds_query = load_dataset("mixed-modality-search/MixBench", name="MSCOCO", split='query')
ds_corpus = load_dataset("mixed-modality-search/MixBench", name="MSCOCO", split='mixed_corpus')
ds_query = load_dataset("mixed-modality-search/MixBench", name="MSCOCO", split='qrel')
# Load other subsets (corpus)
ds_gwit = load_dataset("mixed-modality-search/MixBench", name="Google_WIT", split='mixed_corpus')
ds_news = load_dataset("mixed-modality-search/MixBench", name="VisualNews",split='mixed_corpus')
ds_oven = load_dataset("mixed-modality-search/MixBench", name="OVEN", split='mixed_corpus')