VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks
Paper • 2410.05160 • Published • 4
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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).
TIGER-Lab_MMEB-train/
└── data/
├── A-OKVQA/
│ ├── train.lance
│ ├── original.lance
│ └── diverse.lance
├── MSCOCO/
│ └── ...
└── images/
├── A-OKVQA.lance
├── MSCOCO.lance
└── ...
{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/{dataset}.lance)
| Field | Type | Description |
|---|---|---|
image_id |
string | Image path identifier |
data |
binary | Image binary data (JPEG) |
| 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).
This dataset is derived from TIGER-Lab/MMEB-train. For evaluation, please refer to TIGER-Lab/MMEB-eval.
@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}
}
Apache-2.0 (same as the original dataset)