Add link to paper and code repository, update task category

This PR ensures the dataset is linked to the paper https://huggingface.co/papers/2412.14133, as well as the code repository. It also updates the task category to `image-text-to-text`.

README.md

@@ -1,26 +1,26 @@
 ---
-license: mit
-task_categories:
-- visual-question-answering
 language:
 - en
-pretty_name: PopVQA
+license: mit
 size_categories:
 - 10K<n<100K
+task_categories:
+- image-text-to-text
+pretty_name: PopVQA
 ---
 
 # PopVQA: Popular Entity Visual Question Answering
 
 PopVQA is a dataset designed to study the performance gap in vision-language models (VLMs) when answering factual questions about entities presented in **images** versus **text**.
 
+Paper: https://huggingface.co/papers/2412.14133
+Code: https://github.com/idocohen/vlm-modality-gap
 
 ![PopVQA Teaser](./popvqa_teaser.png)
 
-
 ## 🔍 Motivation
 <img src="./paper_teaser.png" alt="Motivation" width="700">
 
-
 PopVQA was curated to explore the disparity in model performance when answering factual questions about an entity described in text versus depicted in an image. This is achieved by asking the same questions twice, once with the textual representation (the entity's name), then, with the visual representation (entity image). We include several questions about every entity to allow a more fine grained evaluation.
 This dataset was introduced in the paper:
 
@@ -85,3 +85,4 @@ To build the dataset, run:
 
 ```bash
 python scripts/build_dataset.py --base-df path/to/base_entities.csv
+```