Update README.md

README.md

@@ -7,13 +7,55 @@ dataset_info:
     sequence: binary
   splits:
   - name: train
-    num_bytes: 93686373572
-    num_examples: 1448799
-  download_size: 63925876676
-  dataset_size: 93686373572
+    num_bytes: 94020556918
+    num_examples: 1465964
+  download_size: 73033984223
+  dataset_size: 94020556918
 configs:
 - config_name: default
   data_files:
   - split: train
     path: data/train-*
 ---
+# MMEB train split used in MoCa Continual Pre-training
+
+[🏠 Homepage](https://haon-chen.github.io/MoCa/) | [💻 Code](https://github.com/haon-chen/MoCa) | [🤖 MoCa-Qwen25VL-7B](https://huggingface.co/moca-embed/MoCa-Qwen25VL-7B) | [🤖 MoCa-Qwen25VL-3B](https://huggingface.co/moca-embed/MoCa-Qwen25VL-3B) | [📚 Datasets](https://huggingface.co/moca-embed/datasets) | [📄 Paper](https://arxiv.org/abs/2506.23115)
+
+## Introduction
+
+This is a interleaved multimodal pre-training dataset used in the modality-aware continual pre-training of MoCa models. It is adapted from the train split of [
+MMEB](https://huggingface.co/datasets/TIGER-Lab/MMEB-train) by concatenating queries and positive documents.
+
+The dataset consists of interleaved multimodal examples. text is a string containing text while images are image binaries that can be loaded with the following code snippet:
+
+```python
+import PIL.Image
+from io import BytesIO
+
+image_bytes = example['images'][0]
+image = PIL.Image.open(BytesIO(image_bytes))
+```
+
+
+## Citation
+MoCa
+
+```bibtex
+@article{chen2025moca,
+  title={MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings},
+  author={Chen, Haonan and Liu, Hong and Luo, Yuping and Wang, Liang and Yang, Nan and Wei, Furu and Dou, Zhicheng},
+  journal={arXiv preprint arXiv:2506.23115},
+  year={2025}
+}
+```
+
+MMEB
+
+```bibtex
+@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}
+}
+```