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MMEB Training Dataset (Lance Format)

This is a Lance-format version of the TIGER-Lab/MMEB-train dataset, optimized for efficient storage and fast random access.

The original dataset is used for training VLM2Vec models in the paper VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks (ICLR 2025).

Directory Structure

TIGER-Lab_MMEB-train/
└── data/
    ├── A-OKVQA/
    │   ├── train.lance
    │   ├── original.lance
    │   └── diverse.lance
    ├── MSCOCO/
    │   └── ...
    └── images/
        ├── A-OKVQA.lance
        ├── MSCOCO.lance
        └── ...

Schema

Metadata ({dataset}/{variant}.lance)

Field Type Description
qry string Query text (may contain <|image_1|> placeholder)
qry_image_id string Query image path (empty if text-only)
pos_text string Positive sample text
pos_image_id string Positive sample image path
neg_text string Negative sample text (optional)
neg_image_id string Negative sample image path (optional)

Images (images/{dataset}.lance)

Field Type Description
image_id string Image path identifier
data binary Image binary data (JPEG)

Dataset Statistics

Dataset Samples Images
A-OKVQA 17,056 17,056
ChartQA 28,299 28,299
CIRR 26,116 16,640
DocVQA 39,463 39,463
HatefulMemes 8,500 8,500
ImageNet_1K 100,000 100,000
InfographicsVQA 23,946 4,406
MSCOCO 100,000 59,969
MSCOCO_i2t 113,287 113,287
MSCOCO_t2i 100,000 70,414
N24News 48,988 48,988
NIGHTS 15,941 31,882
OK-VQA 9,009 9,009
SUN397 19,850 19,850
VisDial 123,287 123,287
Visual7W 69,817 14,366
VisualNews_i2t 100,000 100,000
VisualNews_t2i 99,903 99,903
VOC2007 7,844 7,844
WebQA 17,166 12,873

Each dataset has 3 variants: train, original, and diverse_instruction (same sample count, different instruction templates).

Original Dataset

This dataset is derived from TIGER-Lab/MMEB-train. For evaluation, please refer to TIGER-Lab/MMEB-eval.

Citation

@article{jiang2024vlm2vec,
  title={VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks},
  author={Jiang, Ziyan and Meng, Rui and Yang, Xinyi and Yavuz, Semih and Zhou, Yingbo and Chen, Wenhu},
  journal={arXiv preprint arXiv:2410.05160},
  year={2024}
}

License

Apache-2.0 (same as the original dataset)

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