Update README.md

README.md

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----
-license: fair-noncommercial-research-license
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-dataset_info:
-  features:
-  - name: video_path
-    dtype: string
-  - name: intent
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 56698
-    num_examples: 1051
-  - name: validation
-    num_bytes: 14792
-    num_examples: 265
-  download_size: 23167
-  dataset_size: 71490
----
+---
+license: fair-noncommercial-research-license
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+dataset_info:
+  features:
+  - name: video_path
+    dtype: string
+  - name: intent
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 56698
+    num_examples: 1051
+  - name: validation
+    num_bytes: 14792
+    num_examples: 265
+  download_size: 23167
+  dataset_size: 71490
+---
+# Basketball Video-Text Dataset
+
+## Overview
+
+A curated subset of the NSVA dataset designed to establish benchmarks for sports video-text models. This dataset addresses the gap in sports-specific evaluation metrics for video understanding models.
+
+## Dataset Structure
+
+- **Train**: 1,051 video-text pairs
+- **Validation**: 265 video-text pairs
+- **Format**: Files with `video_path` and `intent` columns
+- **Domain**: Basketball action descriptions
+
+## Why This Dataset?
+
+Existing video-text datasets focus on abstract-level information but lack sports-specific details. For example, we don't need models to predict generic descriptions like "basketball players are playing basketball" - we need them to understand specific actions like "Three-point 26' jump-shot missed and rebound."
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/669feaf2243a0a91c4bc4251/tTwlouSFlW76UKhziwwly.png)
+
+As shown in the NSVA paper, their approach provides compact, actionable information for statistics counting and game analysis, unlike existing datasets that offer vague descriptions. With the rise of video understanding models, we need standardized benchmarks for sports video analysis. This subset enables practitioners to train and validate their models with consistent evaluation metrics.
+
+## Usage
+
+This dataset contains video filenames and corresponding action descriptions. To access the actual video files, follow the instructions and use the tools provided in the original NSVA repository: https://github.com/jackwu502/NSVA
+
+## Acknowledgement
+
+```bibtex
+@inproceedings{dew2022sports,
+  title={Sports Video Analysis on Large-Scale Data},
+  author={Wu, Dekun and Zhao, He and Bao, Xingce and Wildes, Richard P.},
+  booktitle={ECCV},
+  month = {Oct.},
+  year={2022}
+}
+```
+
+## License
+
+By using this dataset, you must ensure your use falls under fair use as defined by the original authors. All credits go to the original NSVA dataset creators.