update readme.md

README.md

@@ -1,4 +1,13 @@
 ---
+license: cc-by-nc-sa-4.0
+language: ja
+tags:
+- advertisement
+task_categories:
+- text2text-generation
+- image-to-text
+size_categories: 10K<n<100K
+pretty_name: camera
 dataset_info:
 - config_name: with-lp-images
   features:
@@ -104,16 +113,161 @@ configs:
     path: without-lp-images/validation-*
   - split: test
     path: without-lp-images/test-*
-license: cc-by-nc-sa-4.0
-language: ja
-tags:
-- e-commerce
-- education
-- finance
-- hr
-task_categories:
-- text2text-generation
-- image-to-text
-size_categories: 10K<n<100K
-pretty_name: camera
----
+---
+
+# Dataset Card for CAMERA📷:
+
+## Table of Contents:
+- [Dataset Card for Camera](#dataset-card-for-camera)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Details](#dataset-details)
+    - [Dataset Description](#dataset-description)
+    - [Dataset Sources](#dataset-sources)
+  - [Uses](#uses)
+    - [Direct Use](#direct-use)
+    - [Dataset Information](#datasest-information)
+    - [Data Example](#data-example)
+    - [Dataset Structure](#dataset-structure)
+  - [Citation](#citation)
+    
+
+## Dataset Details
+
+### Dataset Description
+CAMERA (CyberAgent Multimodal Evaluation for Ad Text GeneRAtion) is the Japanese ad text generation dataset.
+
+### Dataset Sources
+
+- **Homepage:** [Github](https://github.com/CyberAgentAILab/camera)
+- **Paper:** [Striking Gold in Advertising: Standardization and Exploration of Ad Text
+Generation](https://arxiv.org/abs/2309.12030)
+
+## Uses
+
+### Direct Use
+
+- Dataset with lp images (with-lp-images)
+```python
+import datasets
+dataset = datasets.load_dataset("cyberagent/camera", name="with-lp-images")
+```
+
+- Dataset without lp images (without-lp-images)
+```python
+import datasets
+dataset = datasets.load_dataset("cyberagent/camera", name="without-lp-images")
+```
+
+### Dataset Information
+
+- with-lp-images
+```
+DatasetDict({
+    train: Dataset({
+        features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'],
+        num_rows: 12395
+    })
+    validation: Dataset({
+        features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'],
+        num_rows: 3098
+    })
+    test: Dataset({
+        features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'],
+        num_rows: 872
+    })
+})
+```
+
+- without-lp-images
+```
+DatasetDict({
+    train: Dataset({
+        features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'],
+        num_rows: 12395
+    })
+    validation: Dataset({
+        features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'],
+        num_rows: 3098
+    })
+    test: Dataset({
+        features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'],
+        num_rows: 872
+    })
+})
+```
+
+### Data Example
+
+```
+{'asset_id': 6041,
+ 'kw': 'GLLARE MARUYAMA',
+ 'lp_meta_description': '美容サロン ブルーヘアー 札幌市 西区 琴似 創業34年 かゆみ、かぶれを防ぎ、美しい髪へ',
+ 'title_org': '北海道、水の教会で結婚式',
+ 'title_ne1': '',
+ 'title_ne2': '',
+ 'title_ne3': '',
+ 'domain': '',
+ 'parsed_full_text_annotation': {
+  'text': ['表参道',
+   '名古屋',
+   '梅田',
+    ...
+   '成約者様専用ページ',
+   '個人情報保護方針',
+   '星野リゾートトマム'],
+  'xmax': [163,
+   162,
+   157,
+    ...
+   1047,
+   1035,
+   1138],
+  'xmin': [125,
+   125,
+   129,
+    ...
+   937,
+   936,
+   1027],
+  'ymax': [9652,
+   9791,
+   9928,
+    ...
+   17119,
+   17154,
+   17515],
+  'ymin': [9642,
+   9781,
+   9918,
+    ...
+   17110,
+   17143,
+   17458]},
+ 'lp_image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1200x17596>}
+```
+
+### Dataset Structure
+
+| Name | Description | 
+| ---- | ---- |
+| asset_id | ids (associated with LP images) |
+| kw | search keyword |
+| lp_meta_description | meta description extracted from LP (i.e., LP Text)|
+| title_org  |  ad text (original gold reference) |
+| title_ne{1-3}  |  ad text (additonal gold references for multi-reference evaluation |
+| domain |  industry domain (HR, EC, Fin, Edu) for industry-wise evaluation |
+| parsed_full_text_annotation | OCR result for LP image |
+| lp_image | LP image |
+
+## Citation
+
+```
+@misc{mita2024striking,
+      title={Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation}, 
+      author={Masato Mita and Soichiro Murakami and Akihiko Kato and Peinan Zhang},
+      year={2024},
+      eprint={2309.12030},
+      archivePrefix={arXiv},
+      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
+}
+```