Datasets:
Tasks:
Image-to-Text
Formats:
parquet
Languages:
Japanese
Size:
10K - 100K
Tags:
advertisement
License:
metadata
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:
- name: asset_id
dtype: int64
- name: kw
dtype: string
- name: lp_meta_description
dtype: string
- name: title_org
dtype: string
- name: title_ne1
dtype: string
- name: title_ne2
dtype: string
- name: title_ne3
dtype: string
- name: domain
dtype: string
- name: parsed_full_text_annotation
sequence:
- name: text
dtype: string
- name: xmax
dtype: int64
- name: xmin
dtype: int64
- name: ymax
dtype: int64
- name: ymin
dtype: int64
- name: lp_image
dtype: image
splits:
- name: train
num_bytes: 51367983297.415
num_examples: 12395
- name: validation
num_bytes: 13133740369.43
num_examples: 3098
- name: test
num_bytes: 2528981570
num_examples: 872
download_size: 65867475365
dataset_size: 67030705236.845
- config_name: without-lp-images
features:
- name: asset_id
dtype: int64
- name: kw
dtype: string
- name: lp_meta_description
dtype: string
- name: title_org
dtype: string
- name: title_ne1
dtype: string
- name: title_ne2
dtype: string
- name: title_ne3
dtype: string
- name: domain
dtype: string
- name: parsed_full_text_annotation
sequence:
- name: text
dtype: string
- name: xmax
dtype: int64
- name: xmin
dtype: int64
- name: ymax
dtype: int64
- name: ymin
dtype: int64
splits:
- name: train
num_bytes: 280633510
num_examples: 12395
- name: validation
num_bytes: 69170878
num_examples: 3098
- name: test
num_bytes: 14634833
num_examples: 872
download_size: 150489014
dataset_size: 364439221
configs:
- config_name: with-lp-images
data_files:
- split: train
path: with-lp-images/train-*
- split: validation
path: with-lp-images/validation-*
- split: test
path: with-lp-images/test-*
default: true
- config_name: without-lp-images
data_files:
- split: train
path: without-lp-images/train-*
- split: validation
path: without-lp-images/validation-*
- split: test
path: without-lp-images/test-*
Dataset Card for CAMERA📷:
Table of Contents:
Dataset Details
Dataset Description
CAMERA (CyberAgent Multimodal Evaluation for Ad Text GeneRAtion) is the Japanese ad text generation dataset.
Dataset Sources
- Homepage: Github
- Paper: Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation
Uses
Direct Use
- Dataset with lp images (with-lp-images)
import datasets
dataset = datasets.load_dataset("cyberagent/camera", name="with-lp-images")
- Dataset without lp images (without-lp-images)
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.'}
}