Convert dataset to Parquet (part 00002-of-00003) (#4)

- Convert dataset to Parquet (part 00002-of-00003) (9ca1529966933dc388fb90eb495a19262bd249f9)
- Delete loading script (9c62b4161f4711d585ad8e6f79bf453994b4091d)

README.md

@@ -1,3 +1,58 @@
 ---
 license: cc-by-nc-sa-4.0
+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: 2528981570.0
+    num_examples: 872
+  download_size: 65867475365
+  dataset_size: 67030705236.845
+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
 ---

camera.py

@@ -1,147 +0,0 @@
-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

with-lp-images/test-00000-of-00006.parquet

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