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--- |
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license: cc-by-nc-sa-4.0 |
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language: ja |
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tags: |
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- advertisement |
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task_categories: |
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- text2text-generation |
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- image-to-text |
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size_categories: 10K<n<100K |
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pretty_name: camera |
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dataset_info: |
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- config_name: with-lp-images |
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features: |
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- name: asset_id |
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dtype: int64 |
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- name: kw |
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dtype: string |
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- name: lp_meta_description |
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dtype: string |
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- name: title_org |
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dtype: string |
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- name: title_ne1 |
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dtype: string |
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- name: title_ne2 |
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dtype: string |
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- name: title_ne3 |
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dtype: string |
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- name: domain |
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dtype: string |
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- name: parsed_full_text_annotation |
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sequence: |
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- name: text |
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dtype: string |
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- name: xmax |
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dtype: int64 |
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- name: xmin |
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dtype: int64 |
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- name: ymax |
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dtype: int64 |
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- name: ymin |
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dtype: int64 |
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- name: lp_image |
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dtype: image |
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splits: |
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- name: test |
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num_bytes: 2528981570 |
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num_examples: 872 |
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- name: validation |
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num_bytes: 13133740369.43 |
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num_examples: 3098 |
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- name: train |
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num_bytes: 51367983297.415 |
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num_examples: 12395 |
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download_size: 65867475365 |
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dataset_size: 67030705236.845 |
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- config_name: without-lp-images |
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features: |
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- name: asset_id |
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dtype: int64 |
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- name: kw |
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dtype: string |
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- name: lp_meta_description |
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dtype: string |
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- name: title_org |
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dtype: string |
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- name: title_ne1 |
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dtype: string |
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- name: title_ne2 |
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dtype: string |
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- name: title_ne3 |
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dtype: string |
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- name: domain |
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dtype: string |
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- name: parsed_full_text_annotation |
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sequence: |
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- name: text |
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dtype: string |
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- name: xmax |
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dtype: int64 |
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- name: xmin |
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dtype: int64 |
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- name: ymax |
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dtype: int64 |
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- name: ymin |
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dtype: int64 |
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splits: |
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- name: test |
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num_bytes: 14634833 |
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num_examples: 872 |
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- name: validation |
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num_bytes: 69170878 |
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num_examples: 3098 |
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- name: train |
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num_bytes: 280633510 |
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num_examples: 12395 |
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download_size: 150489014 |
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dataset_size: 364439221 |
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configs: |
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- config_name: with-lp-images |
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data_files: |
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- split: test |
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path: with-lp-images/test-* |
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- split: validation |
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path: with-lp-images/validation-*s |
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- split: train |
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path: with-lp-images/train-* |
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default: true |
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- config_name: without-lp-images |
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data_files: |
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- split: test |
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path: without-lp-images/test-* |
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- split: validation |
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path: without-lp-images/validation-* |
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- split: train |
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path: without-lp-images/train-* |
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|
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--- |
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|
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# Dataset Card for CAMERA📷: |
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|
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## Table of Contents: |
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- [Dataset Card for Camera](#dataset-card-for-camera) |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Details](#dataset-details) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Sources](#dataset-sources) |
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- [Uses](#uses) |
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- [Direct Use](#direct-use) |
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- [Dataset Information](#datasest-information) |
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- [Data Example](#data-example) |
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- [Dataset Structure](#dataset-structure) |
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- [Citation](#citation) |
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## Dataset Details |
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|
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### Dataset Description |
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CAMERA (CyberAgent Multimodal Evaluation for Ad Text GeneRAtion) is the Japanese ad text generation dataset, which comprises actual data sourced from Japanese search ads and incorporates annotations encompassing multi-modal information such as the LP images. |
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### Dataset Sources |
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- **Homepage:** [Github](https://github.com/CyberAgentAILab/camera) |
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- **Paper:** [Striking Gold in Advertising: Standardization and Exploration of Ad Text |
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Generation](https://arxiv.org/abs/2309.12030) |
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- [NEW!] Our paper has been accepted to [ACL2024](https://2024.aclweb.org/), and we will update the paper information as soon as the proceedings are published. |
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## Uses |
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### Direct Use |
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|
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- Dataset with lp images (with-lp-images) |
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```python |
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import datasets |
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dataset = datasets.load_dataset("cyberagent/camera", name="with-lp-images") |
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``` |
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- Dataset without lp images (without-lp-images) |
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```python |
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import datasets |
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dataset = datasets.load_dataset("cyberagent/camera", name="without-lp-images") |
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``` |
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### Dataset Information |
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|
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- with-lp-images |
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``` |
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DatasetDict({ |
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train: Dataset({ |
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features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'], |
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num_rows: 12395 |
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}) |
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validation: Dataset({ |
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features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'], |
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num_rows: 3098 |
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}) |
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test: Dataset({ |
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features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'], |
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num_rows: 872 |
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}) |
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}) |
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``` |
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|
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- without-lp-images |
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``` |
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DatasetDict({ |
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train: Dataset({ |
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features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'], |
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num_rows: 12395 |
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}) |
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validation: Dataset({ |
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features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'], |
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num_rows: 3098 |
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}) |
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test: Dataset({ |
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features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'], |
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num_rows: 872 |
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}) |
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}) |
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``` |
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### Data Example |
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``` |
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{'asset_id': 6041, |
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'kw': 'GLLARE MARUYAMA', |
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'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', |
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'title_org': '北海道、水の教会で結婚式', |
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'title_ne1': '', |
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'title_ne2': '', |
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'title_ne3': '', |
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'domain': '', |
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'parsed_full_text_annotation': { |
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'text': ['表参道', |
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'名古屋', |
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'梅田', |
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... |
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'成約者様専用ページ', |
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'個人情報保護方針', |
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'星野リゾートトマム'], |
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'xmax': [163, |
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162, |
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157, |
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... |
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1047, |
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1035, |
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1138], |
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'xmin': [125, |
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125, |
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129, |
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... |
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937, |
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936, |
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1027], |
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'ymax': [9652, |
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9791, |
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9928, |
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... |
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17119, |
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17154, |
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17515], |
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'ymin': [9642, |
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9781, |
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9918, |
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... |
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17110, |
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17143, |
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17458]}, |
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'lp_image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1200x17596>} |
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``` |
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### Dataset Structure |
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|
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| Name | Description | |
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| ---- | ---- | |
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| asset_id | ids (associated with LP images) | |
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| kw | search keyword | |
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| lp_meta_description | meta description extracted from LP (i.e., LP Text)| |
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| title_org | ad text (original gold reference) | |
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| title_ne{1-3} | ad text (additonal gold references for multi-reference evaluation | |
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| domain | industry domain (HR, EC, Fin, Edu) for industry-wise evaluation | |
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| parsed_full_text_annotation | OCR result for LP image | |
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| lp_image | LP image | |
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|
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## Citation |
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|
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``` |
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@misc{mita2024striking, |
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title={Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation}, |
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author={Masato Mita and Soichiro Murakami and Akihiko Kato and Peinan Zhang}, |
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year={2024}, |
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eprint={2309.12030}, |
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archivePrefix={arXiv}, |
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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.'} |
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} |
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``` |
