init

data/images.tar

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+size 2767959040

images.tar

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multimodal_sarcasm_detection.py

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