Datasets:
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import ast
import datasets as ds
import pandas as pd
_DESCRIPTION = """\
CAMERA (CyberAgent Multimodal Evaluation for Ad Text GeneRAtion) is the Japanese ad text generation dataset.
"""
_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.'}
}
"""
_HOMEPAGE = "https://github.com/CyberAgentAILab/camera"
_LICENSE = """\
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
"""
_URLS = {
"without-lp-images": "https://storage.googleapis.com/camera-public/camera-v2.2-minimal.tar.gz",
"with-lp-images": "https://storage.googleapis.com/camera-public/camera-v2.2.tar.gz",
}
_DESCRIPTION = {
"without-lp-images": "The CAMERA dataset w/o LP images (ver.2.2.0)",
"with-lp-images": "The CAMERA dataset w/ LP images (ver.2.2.0)",
}
_VERSION = ds.Version("2.2.0", "")
class CameraConfig(ds.BuilderConfig):
def __init__(self, name: str, version: ds.Version = _VERSION, **kwargs):
super().__init__(
name=name,
description=_DESCRIPTION[name],
version=version,
**kwargs,
)
class CameraDataset(ds.GeneratorBasedBuilder):
BUILDER_CONFIGS = [CameraConfig(name="with-lp-images")]
DEFAULT_CONFIG_NAME = "with-lp-images"
def _info(self) -> ds.DatasetInfo:
features = ds.Features(
{
"asset_id": ds.Value("int64"),
"kw": ds.Value("string"),
"lp_meta_description": ds.Value("string"),
"title_org": ds.Value("string"),
"title_ne1": ds.Value("string"),
"title_ne2": ds.Value("string"),
"title_ne3": ds.Value("string"),
"domain": ds.Value("string"),
"parsed_full_text_annotation": ds.Sequence(
{
"text": ds.Value("string"),
"xmax": ds.Value("int64"),
"xmin": ds.Value("int64"),
"ymax": ds.Value("int64"),
"ymin": ds.Value("int64"),
}
),
}
)
if self.config.name == "with-lp-images":
features["lp_image"] = ds.Image()
return ds.DatasetInfo(
description=_DESCRIPTION,
citation=_CITATION,
homepage=_HOMEPAGE,
license=_LICENSE,
features=features,
)
def _split_generators(self, dl_manager: ds.DownloadManager):
base_dir = dl_manager.download_and_extract(_URLS[self.config.name])
lp_image_dir: str | None = None
if self.config.name == "without-lp-images":
data_dir = f"{base_dir}/camera-v2.2-minimal"
elif self.config.name == "with-lp-images":
data_dir = f"{base_dir}/camera-v2.2"
lp_image_dir = f"{data_dir}/lp-screenshot"
else:
raise ValueError(f"Invalid config name: {self.config.name}")
return [
ds.SplitGenerator(
name=ds.Split.TRAIN,
gen_kwargs={
"file": f"{data_dir}/train.csv",
"lp_image_dir": lp_image_dir,
},
),
ds.SplitGenerator(
name=ds.Split.VALIDATION,
gen_kwargs={
"file": f"{data_dir}/dev.csv",
"lp_image_dir": lp_image_dir,
},
),
ds.SplitGenerator(
name=ds.Split.TEST,
gen_kwargs={
"file": f"{data_dir}/test.csv",
"lp_image_dir": lp_image_dir,
},
),
]
def _generate_examples(self, file: str, lp_image_dir: str | None = None):
df = pd.read_csv(file)
for i, data_dict in enumerate(df.to_dict("records")):
asset_id = data_dict["asset_id"]
example_dict = {
"asset_id": asset_id,
"kw": data_dict["kw"],
"lp_meta_description": data_dict["lp_meta_description"],
"title_org": data_dict["title_org"],
"title_ne1": data_dict.get("title_ne1", ""),
"title_ne2": data_dict.get("title_ne2", ""),
"title_ne3": data_dict.get("title_ne3", ""),
"domain": data_dict.get("domain", ""),
"parsed_full_text_annotation": ast.literal_eval(
data_dict["parsed_full_text_annotation"]
),
}
if self.config.name == "with-lp-images" and lp_image_dir is not None:
file_name = f"screen-1200-{asset_id}.png"
example_dict["lp_image"] = f"{lp_image_dir}/{file_name}"
yield i, example_dict
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