Update README.md

README.md

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 ---
+dataset_info:
+- config_name: default
+  features:
+  - name: LPimage
+    dtype: image
+  - name: image1
+    dtype: image
+  - name: image2
+    dtype: image
+  - name: image3
+    dtype: image
+  - name: image4
+    dtype: image
+  - name: image5
+    dtype: image
+  - name: annotator1_ranking
+    sequence: int32
+    length: 5
+  - name: annotator1_best
+    dtype: int32
+  - name: annotator1_worst
+    dtype: int32
+  - name: annotator2_ranking
+    sequence: int32
+    length: 5
+  - name: annotator2_best
+    dtype: int32
+  - name: annotator2_worst
+    dtype: int32
+  - name: annotator3_ranking
+    sequence: int32
+    length: 5
+  - name: annotator3_best
+    dtype: int32
+  - name: annotator3_worst
+    dtype: int32
+  - name: annotator4_ranking
+    sequence: int32
+    length: 5
+  - name: annotator4_best
+    dtype: int32
+  - name: annotator4_worst
+    dtype: int32
+  - name: annotator5_ranking
+    sequence: int32
+    length: 5
+  - name: annotator5_best
+    dtype: int32
+  - name: annotator5_worst
+    dtype: int32
+  - name: best_annotator
+    dtype: string
+  - name: average_rank_correlation
+    dtype: float32
 license: cc-by-nc-sa-4.0
+task_categories:
+- visual-question-answering
+language:
+- ja
+size_categories:
+- n<1K
 ---
+
+# BannerBench: Benchmarking Vision Language Models for Multi-Ad Selection with Human Preferences
+
+### Dataset Summary
+> The BannerBench is designed to evaluate the ability of VLMs to identify the banner that best matches human preferences from a set of candidates.
+
+## Dataset Structure
+The structure of the raw dataset is as follows:
+
+```JSON
+{
+    "train": Dataset({
+        "features": [
+          'LPimage', 'image1', 'image2', 'image3', 'image4', 'image5', 
+          'annotator1_ranking', 'annotator1_best', 'annotator1_worst', 
+          'annotator2_ranking', 'annotator2_best', 'annotator2_worst', 
+          'annotator3_ranking', 'annotator3_best', 'annotator3_worst', 
+          'annotator4_ranking', 'annotator4_best', 'annotator4_worst', 
+          'annotator5_ranking', 'annotator5_best', 'annotator5_worst', 
+          'best_annotator', 'average_rank_correlation'
+        ],
+    })
+}
+```
+
+### Example
+```Python
+from datasets import load_dataset
+
+dataset = load_dataset("cyberagent/BannerBench")
+
+print(dataset)
+# DatasetDict({
+#     train: Dataset({
+#         features: ['LPimage', 'image1', 'image2', 'image3', 'image4', 'image5', 'annotator1_ranking', 'annotator1_best', 'annotator1_worst', 'annotator2_ranking', 'annotator2_best', 'annotator2_worst', 'annotator3_ranking', 'annotator3_best', 'annotator3_worst', 'annotator4_ranking', 'annotator4_best', 'annotator4_worst', 'annotator5_ranking', 'annotator5_best', 'annotator5_worst', 'best_annotator', 'average_rank_correlation'],
+#         num_rows: 900
+#     })
+# })
+```
+
+An example of the dataset is as follows:
+
+```JSON
+{
+  "LPimage": <PIL.PngImagePlugin.PngImageFile image mode=RGB size=1280x5352 at 0x7F09A24675D0>,
+  "image1": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1080x1080 at 0x7F09A1C9B250>,
+  "image2": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1080x1080 at 0x7F09A1CB52D0>,
+  "image3": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1080x1080 at 0x7F09A1CB5810>,
+  "image4": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1080x1080 at 0x7F09A1CB5E50>, 
+  "image5": <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1080x1080 at 0x7F09A1CB6490>, 
+  "annotator1_ranking": [2, 4, 1, 3, 5], 
+  "annotator1_best": 3, 
+  "annotator1_worst": 5, 
+  "annotator2_ranking": [4, 5, 1, 2, 3], 
+  "annotator2_best": 3, 
+  "annotator2_worst": 2, 
+  "annotator3_ranking": [3, 2, 1, 4, 5], 
+  "annotator3_best": 3, 
+  "annotator3_worst": 5, 
+  "annotator4_ranking": [3, 4, 5, 2, 1], 
+  "annotator4_best": 5, 
+  "annotator4_worst": 3, 
+  "annotator5_ranking": [1, 4, 2, 3, 5], 
+  "annotator5_best": 1, 
+  "annotator5_worst": 5, 
+  "best_annotator": "annotator1", 
+  "average_rank_correlation": 0.6534000039100647
+}
+```
+
+### Data Fields
+
+- LPimage: The Landing-Page image related image[1-5].
+- image[1-5]: The Banners derived from a "LPimage".
+- annotator[1-5]_ranking: Ranking of the advertisemental images in most prefered order by annotators 1 to 5.
+- annotator[1-5]_best: The advertisement image is the most preferred one by annotators 1 to 5 in the Best-Choice task.
+- annotator[1-5]_worst: The advertisement image is the least preferred one by annotators 1 to 5 in the Best-Choice task.
+- best_annotator: The annotator whose average rank correlation with the other four annotators is the highest
+- average_rank_correlation: The average of the top half of all possible annotator pairs, selected based on their rank correlation.
+
+## Dataset Creation
+
+> BannerBench construction process consists of the following 3 steps;
+> (1)we collected sets of five banners derived from a single LP (Banner Sets; BSs), (2)we annotated human preference to the BSs, (3)we propose two subtasks: Ranking and Best-Choice.
+
+## Considerations for Using the Data
+> Since BannerBench is intended solely for evaluation purposes, it is not designed for training use; the benchmark focuses on assessing the inductive capabilities of VLMs.
+
+## License
+AdTEC dataset is released under the [CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International license](./LICENSE).
+
+### Citation Information
+To cite this work, please use the following format:
+```
+{
+  author = {Hiroto Otake and Peinan Zhang and Yusuke Sakai and Masato Mita and Hiroki Ouchi and Taro Watanabe},
+  title = {BannerBench: Benchmarking Vision Language Models for Multi-Ad Selection with Human Preferences},
+  year = {2025},
+  url = {}
+}
+```