Add task categories, link to paper.

This PR ensures the dataset is linked to (and can be found at) https://huggingface.co/papers/2502.08468.
It also adds the `task_categories` tag.

README.md

@@ -2,6 +2,8 @@
 license: mit
 language:
 - en
+task_categories:
+- image-text-to-text
 tags:
 - embedding
 - multimodal
@@ -42,7 +44,6 @@ configs:
 
 # XTD Multimodal Multilingual Data With Instruction
 
-
 This dataset contains datasets (**with English instruction**) used for evaluating the multilingual capability of a multimodal embedding model, including seven languages:
 - **it**, **es**, **ru**, **zh**, **pl**, **tr**, **ko**
 
@@ -52,7 +53,6 @@ This dataset contains datasets (**with English instruction**) used for evaluatin
 - The instruction on the document side is: "Represent the given image."
 - Each example contains a query and a set of targets. The first one in the candidate list is the groundtruth target.
 
-
 ## Image Preparation
 
 First, you should prepare the images used for evaluation:
@@ -67,7 +67,6 @@ wget https://huggingface.co/datasets/Haon-Chen/XTD-10/resolve/main/XTD10_dataset
 tar -I "pigz -d -p 8" -xf XTD10_dataset.tar.gz
 ```
 
-
 ### Image Organization
 
 ```
@@ -80,10 +79,12 @@ 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, feel free to cite the original paper of XTD and mmE5 paper.
+If you use this dataset in your research, feel free to cite the original paper of XTD and the mmE5 paper.
+
+[mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data](https://huggingface.co/papers/2502.08468)
+
 ```
 @article{chen2025mmE5,
   title={mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data},