Add paper and GitHub links, update task categories

README.md

@@ -1,12 +1,13 @@
 ---
 license: cc-by-4.0
 task_categories:
-- video-classification
-- question-answering
+- video-text-to-text
 ---
 
 # SYNCR
 
+[**Paper**](https://huggingface.co/papers/2605.08412) | [**GitHub**](https://github.com/SaraGhazanfari/SYNCR)
+
 SYNCR is a synthetic cross-video reasoning benchmark for evaluating multimodal large language models on questions that require reasoning across multiple independent videos.
 
 The benchmark contains 8,163 multiple-choice question-answer examples spanning 4 reasoning categories and 8 tasks. Each example is programmatically grounded in synthetic video data generated from CLEVRER, Kubric, and Habitat-based environments.
@@ -39,6 +40,18 @@ SYNCR covers four reasoning categories:
    - Habitat object counting
    - Habitat route planning
 
+## Sample usage
+
+To generate SYNCR benchmark data, use the unified `generate_data.py` script provided in the GitHub repository:
+
+```bash
+# Example: Generate Temporal Alignment data (Multi-angle synchronization)
+python generate_data.py --dataset kubric_sync --path /path/to/kubric --total-num 1000 --save
+
+# Example: Generate Temporal Alignment data (Sequential ordering)
+python generate_data.py --dataset clevrer_temporal --root-path /path/to/clevrer --total-num 1000 --save
+```
+
 ## Splits
 
 The current release provides one split: