Improve dataset card: Add task categories, paper link, and GitHub link

This PR improves the dataset card for the `EPFL-Smart-Kitchen: Lemonade benchmark` by:

* Adding `video-text-to-text` to the `task_categories` in the metadata, reflecting the dataset's multi-modal nature (video and text for QA). The existing `question-answering` category is also retained as it accurately describes the benchmark's primary objective.
* Including a direct link to the paper on Hugging Face (https://huggingface.co/papers/2506.01608) at the top of the dataset card for better visibility.
* Adding a clear link to the main GitHub repository (https://github.com/amathislab/EPFL-Smart-Kitchen) at the top. The existing internal link in the "Content" section has also been cleaned up by removing the trailing `#`.

README.md

@@ -1,7 +1,12 @@
 ---
-license: mit
 language:
 - en
+license: mit
+size_categories:
+- 10K<n<100K
+task_categories:
+- question-answering
+- video-text-to-text
 tags:
 - behavior
 - motion
@@ -11,14 +16,12 @@ tags:
 - llm
 - vlm
 - esk
-size_categories:
-- 10K<n<100K
-task_categories:
-- question-answering
 ---
 
 # 🍋 EPFL-Smart-Kitchen: Lemonade benchmark
 
+[Paper](https://huggingface.co/papers/2506.01608) | [GitHub](https://github.com/amathislab/EPFL-Smart-Kitchen)
+
 ![title](media/title.svg)
 
 ## 📚 Introduction
@@ -26,7 +29,7 @@ task_categories:
 Lemonade consists of <span style="color: orange;">36,521</span> closed-ended QA pairs linked to egocentric video clips, categorized in three groups and six subcategories. <span style="color: orange;">18,857</span> QAs focus on behavior understanding, leveraging the rich ground truth behavior annotations of the EPFL-Smart Kitchen to interrogate models about perceived actions <span style="color: tomato;">(Perception)</span> and reason over unseen behaviors <span style="color: tomato;">(Reasoning)</span>. <span style="color: orange;">8,210</span> QAs involve longer video clips, challenging models in summarization <span style="color: gold;">(Summarization)</span> and session-level inference <span style="color: gold;">(Session properties)</span>. The remaining <span style="color: orange;">9,463</span> QAs leverage the 3D pose estimation data to infer hand shapes, joint angles <span style="color: skyblue;">(Physical attributes)</span>, or trajectory velocities <span style="color: skyblue;">(Kinematics)</span> from visual information.
 
 ## 💾 Content
-The current repository contains all egocentric videos recorded in the EPFL-Smart-Kitchen-30 dataset and the question answer pairs of the Lemonade benchmark. Please refer to the [main GitHub repository](https://github.com/amathislab/EPFL-Smart-Kitchen#) to find the other benchmarks and links to download other modalities of the EPFL-Smart-Kitchen-30 dataset.
+The current repository contains all egocentric videos recorded in the EPFL-Smart-Kitchen-30 dataset and the question answer pairs of the Lemonade benchmark. Please refer to the [main GitHub repository](https://github.com/amathislab/EPFL-Smart-Kitchen) to find the other benchmarks and links to download other modalities of the EPFL-Smart-Kitchen-30 dataset.
 
 ### 🗃️ Repository structure
 
@@ -63,8 +66,6 @@ Lemonade
 ## 🌈 Usage
 The evaluation of the benchmark can be done through the following github repository: [https://github.com/amathislab/lmms-eval-lemonade](https://github.com/amathislab/lmms-eval-lemonade)
 
-
-
 ## 🌟 Citations
 Please cite our work!
 ```
@@ -81,4 +82,4 @@ Please cite our work!
 
 ## ❤️ Acknowledgments
 
-Our work was funded by EPFL, Swiss SNF grant (320030-227871), Microsoft Swiss Joint Research Center and a Boehringer Ingelheim Fonds PhD stipend (H.Q.). We are grateful to the Brain Mind Institute for providing funds for hardware and to the Neuro-X Institute for providing funds for services.
+Our work was funded by EPFL, Swiss SNF grant (320030-227871), Microsoft Swiss Joint Research Center and a Boehringer Ingelheim Fonds PhD stipend (H.Q.). We are grateful to the Brain Mind Institute for providing funds for hardware and to the Neuro-X Institute for providing funds for services.