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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}
+}
+```