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import datasets |
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from datasets.features import ClassLabel, Features, Image |
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import copy |
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from dataclasses import dataclass, field |
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from typing import ClassVar, Dict |
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from datasets import TaskTemplate, ImageClassification |
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_DESCRIPTION = "none" |
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_NAMES = ["non-sarcastic", "sarcastic"] |
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_HOMEPAGE = "https://github.com/headacheboy/data-of-multimodal-sarcasm-detection" |
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_CITATION = "none" |
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_LICENSE = "none" |
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_BASE_URL = "data/images.tar" |
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_METADATA_URLS = {"train": "data/text/train.txt", |
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"test": "data/text/test2.txt", |
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"valid": "data/text/valid2.txt"} |
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_IMAGES_DIR = "images/" |
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@dataclass(frozen=True) |
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class ImageTextClassification(TaskTemplate): |
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task: str = field(default="image-text-classification", metadata={"include_in_asdict_even_if_is_default": True}) |
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input_schema: ClassVar[Features] = Features({"image": Image()}) |
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text_schema: ClassVar[Features] = Features({"text": datasets.Value("string")}) |
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label_schema: ClassVar[Features] = Features({"labels": ClassLabel}) |
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image_column: str = "image" |
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text_column: str = "text" |
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label_column: str = "labels" |
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def align_with_features(self, features): |
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if self.label_column not in features: |
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raise ValueError(f"Column {self.label_column} is not present in features.") |
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if not isinstance(features[self.label_column], ClassLabel): |
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raise ValueError(f"Column {self.label_column} is not a ClassLabel.") |
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task_template = copy.deepcopy(self) |
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label_schema = self.label_schema.copy() |
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label_schema["labels"] = features[self.label_column] |
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task_template.__dict__["label_schema"] = label_schema |
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return task_template |
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@property |
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def column_mapping(self) -> Dict[str, str]: |
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return { |
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self.image_column: "image", |
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self.text_column: "text", |
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self.label_column: "labels", |
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} |
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class MultimodalSarcasmDetection(datasets.GeneratorBasedBuilder): |
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"""MultimodalSarcasmDetection Images dataset""" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"image": datasets.Image(), |
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"text": datasets.Value("string"), |
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"label": datasets.ClassLabel(names=_NAMES), |
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} |
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), |
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supervised_keys=(("image", "text"), "label"), |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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license=_LICENSE, |
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task_templates=[ImageTextClassification(image_column="image", text_column="text", label_column="label")], |
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) |
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def _split_generators(self, dl_manager): |
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archive_path = dl_manager.download(_BASE_URL) |
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split_metadata_paths = dl_manager.download(_METADATA_URLS) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"images": dl_manager.iter_archive(archive_path), |
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"metadata_path": split_metadata_paths["train"], |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"images": dl_manager.iter_archive(archive_path), |
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"metadata_path": split_metadata_paths["test"], |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"images": dl_manager.iter_archive(archive_path), |
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"metadata_path": split_metadata_paths["valid"], |
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}, |
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), |
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] |
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def _generate_examples(self, images, metadata_path): |
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"""Generate images and labels for splits.""" |
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lines = {} |
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files_to_keep = set() |
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with open(metadata_path, encoding="utf-8") as f: |
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for line in f: |
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line = line.strip() |
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if line: |
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line = eval(line) |
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lines[line[0]] = line[1:] |
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files_to_keep.add(line[0]) |
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for file_path, file_obj in images: |
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if file_path.startswith(_IMAGES_DIR): |
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image_id = file_path[len(_IMAGES_DIR): -len(".jpg")] |
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if image_id in files_to_keep: |
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line = lines[image_id] |
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yield file_path, { |
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"image": {"path": file_path, "bytes": file_obj.read()}, |
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"text": line[0], |
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"label": line[-1] |
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} |
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