Update dataset card with paper, project, and GitHub links

Hi! I'm Niels from the Hugging Face community science team. I've updated the dataset card for PushUpBench to include links to the associated research paper, the project website, and the official `lmms-eval` GitHub repository. I also updated the description to better reflect the benchmark's scope as described in the paper abstract.

README.md

@@ -1,49 +1,51 @@
 ---
+license: cc-by-4.0
+size_categories:
+- n<1K
+task_categories:
+- video-classification
+- visual-question-answering
+pretty_name: PushUpBench
 dataset_info:
   features:
-    - name: id
+  - name: id
+    dtype: int64
+  - name: name
+    dtype: string
+  - name: video_path
+    dtype: string
+  - name: count
+    sequence:
       dtype: int64
-    - name: name
-      dtype: string
-    - name: video_path
-      dtype: string
-    - name: count
-      sequence:
-        dtype: int64
-    - name: fuzzy_action
-      dtype: bool
-    - name: complex_action
-      dtype: bool
+  - name: fuzzy_action
+    dtype: bool
+  - name: complex_action
+    dtype: bool
   splits:
-    - name: test
-      num_examples: 227
+  - name: test
+    num_examples: 227
 configs:
-  - config_name: default
-    data_files:
-      - split: test
-        path: data/test.jsonl
-license: cc-by-4.0
-task_categories:
-  - video-classification
-  - visual-question-answering
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test.jsonl
 tags:
-  - video
-  - counting
-  - repetition-counting
-  - exercise
-  - benchmark
-pretty_name: PushUpBench
-size_categories:
-  - n<1K
+- video
+- counting
+- repetition-counting
+- exercise
+- benchmark
 ---
 
 # PushUpBench: Video Repetition Counting Benchmark
 
-PushUpBench is a benchmark for evaluating vision-language models on their ability to count exercise repetitions in videos.
+[**Project Page**](https://pushupbench.com) | [**Paper**](https://huggingface.co/papers/2604.23407) | [**GitHub**](https://github.com/EvolvingLMMs-Lab/lmms-eval)
+
+PushUpBench is a benchmark for evaluating vision-language models (VLMs) on their ability to count exercise repetitions in videos. It was introduced in the paper ["PushupBench: Your VLM is not good at counting pushups"](https://huggingface.co/papers/2604.23407). The dataset consists of 446 long-form clips (averaging 36.7s) designed to test temporal reasoning and repetition counting beyond simple pattern recognition.
 
 ## Dataset Description
 
-- **Total samples**: 227
+- **Total samples**: 446 clips (227 in the test split)
 - **Video format**: MP4
 - **Task**: Count the number of repetitions of a specified exercise in a video
 
@@ -58,6 +60,8 @@ Each sample contains:
 
 ## Usage with lmms-eval
 
+PushUpBench is incorporated in the [`lmms-eval`](https://github.com/EvolvingLMMs-Lab/lmms-eval) toolkit.
+
 ```bash
 # Set the video directory
 export PUSHUPBENCH_VIDEO_DIR=/path/to/videos
@@ -72,6 +76,6 @@ python -m lmms_eval \
 
 ## Metrics
 
-- **Exact Match**: Prediction matches any value in the ground truth count list
-- **MAE**: Mean Absolute Error between prediction and primary ground truth
-- **OBO**: Off-By-One accuracy (prediction within 1 of any ground truth)
+- **Exact Match**: Prediction matches any value in the ground truth count list.
+- **MAE**: Mean Absolute Error between prediction and primary ground truth.
+- **OBO**: Off-By-One accuracy (prediction within 1 of any ground truth).