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README.md

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----
-license: mit
-language:
-- en
-tags:
-- embedding
-- multimodal
-pretty_name: MMEB with hard negative
-size_categories:
-- 1M<n<10M
-configs:
-- config_name: TAT-DQA
-  data_files:
-    - split: train
-      path: "TAT-DQA/TAT-DQA.parquet"
-- config_name: ArxivQA
-  data_files:
-    - split: train
-      path: "ArxivQA/ArxivQA.parquet"
-- config_name: InfoSeek_it2t
-  data_files:
-    - split: train
-      path: "InfoSeek_it2t/InfoSeek_it2t.parquet"
-- config_name: InfoSeek_it2it
-  data_files:
-    - split: train
-      path: "InfoSeek_it2it/InfoSeek_it2it.parquet"
-- config_name: ImageNet_1K
-  data_files:
-    - split: train
-      path: "ImageNet_1K/ImageNet_1K.parquet"
-- config_name: N24News
-  data_files:
-    - split: train
-      path: "N24News/N24News.parquet"
-- config_name: HatefulMemes
-  data_files:
-    - split: train
-      path: "HatefulMemes/HatefulMemes.parquet"
-- config_name: SUN397
-  data_files:
-    - split: train
-      path: "SUN397/SUN397.parquet"
-- config_name: VOC2007
-  data_files:
-    - split: train
-      path: "VOC2007/VOC2007.parquet"
-- config_name: InfographicsVQA
-  data_files:
-    - split: train
-      path: "InfographicsVQA/InfographicsVQA.parquet"
-- config_name: ChartQA
-  data_files:
-    - split: train
-      path: "ChartQA/ChartQA.parquet"
-- config_name: A-OKVQA
-  data_files:
-    - split: train
-      path: "A-OKVQA/A-OKVQA.parquet"
-- config_name: DocVQA
-  data_files:
-    - split: train
-      path: "DocVQA/DocVQA.parquet"
-- config_name: OK-VQA
-  data_files:
-    - split: train
-      path: "OK-VQA/OK-VQA.parquet"
-- config_name: Visual7W
-  data_files:
-    - split: train
-      path: "Visual7W/Visual7W.parquet"
-- config_name: VisDial
-  data_files:
-    - split: train
-      path: "VisDial/VisDial.parquet"
-- config_name: CIRR
-  data_files:
-    - split: train
-      path: "CIRR/CIRR.parquet"
-- config_name: NIGHTS
-  data_files:
-    - split: train
-      path: "NIGHTS/NIGHTS.parquet"
-- config_name: WebQA
-  data_files:
-    - split: train
-      path: "WebQA/WebQA.parquet"
-- config_name: VisualNews_i2t
-  data_files:
-    - split: train
-      path: "VisualNews_i2t/VisualNews_i2t.parquet"
-- config_name: VisualNews_t2i
-  data_files:
-    - split: train
-      path: "VisualNews_t2i/VisualNews_t2i.parquet"
-- config_name: MSCOCO_i2t
-  data_files:
-    - split: train
-      path: "MSCOCO_i2t/MSCOCO_i2t.parquet"
-- config_name: MSCOCO_t2i
-  data_files:
-    - split: train
-      path: "MSCOCO_t2i/MSCOCO_t2i.parquet"
-- config_name: MSCOCO
-  data_files:
-    - split: train
-      path: "MSCOCO/MSCOCO.parquet"
----
-# mmE5 Labeled Data
-
-
-
-
-This dataset contains datasets used for the supervised finetuning of mmE5 ([mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data](https://arxiv.org/abs/2502.08468)):
-- **MMEB** (with hard negative)
-- **InfoSeek** (from M-BEIR)
-- **TAT-DQA**
-- **ArxivQA**
-
-[Github](https://github.com/haon-chen/mmE5)
-
-## Image Preparation
-
-First, you should prepare the images used for training:
-
-### Image Downloads
-
-- **Download Links**: Download image resources for each dataset via the following links:
-  - [**MMEB**](https://huggingface.co/datasets/TIGER-Lab/MMEB-train)
-  - [**InfoSeek**](https://huggingface.co/datasets/TIGER-Lab/M-BEIR)
-  - [**ArxivQA**](https://huggingface.co/datasets/MMInstruction/ArxivQA)
-  - [**TAT-DQA**](https://huggingface.co/datasets/vidore/tatdqa_train/tree/main)
-
-For TAT-DQA, you need to first save images into the overall image folder to align usage:
-
-```
-dataset = load_dataset(
-    "vidore/tatdqa_train",
-    split="train"
-)
-image_out_dir = "images/TAT-DQA"
-os.makedirs(image_out_dir, exist_ok=True)
-for i, sample in enumerate(dataset):
-  save_path = os.path.join(image_out_dir, f"tatdqa_{i}.png")
-  if os.path.exists(save_path):
-    continue
-  image = sample["image"]
-  image.save(save_path, format="PNG")
-```
-
-### Image Organization
-
-```
-  images/
-  ├── mbeir_images/
-  │     └── oven_images/
-  │           └── ... .jpg (InfoSeek)
-  ├── ArxivQA/
-  │     └── images/
-  │           └── ... .jpg (ArxivQA)
-  └── TAT-DQA/
-  │     └── ... .png (TAT-DQA)
-  └── A-OKVQA/
-        └── Train/
-  │           └── ... .jpg (A-OKVQA)
-  │
-  ... (MMEB Training images)
-```
-
-You can refer to the image paths in each subset to view the image organization.
-
-You can also customize your image paths by altering the image_path fields.
-## Citation
-If you use this dataset in your research, please cite the associated paper.
-```
-@article{chen2025mmE5,
-  title={mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data},
-  author={Chen, Haonan and Wang, Liang and Yang, Nan and Zhu, Yutao and Zhao, Ziliang and Wei, Furu and Dou, Zhicheng},
-  journal={arXiv preprint arXiv:2502.08468},
-  year={2025}
-}
-```