Delete Generative-VQA-V2-Curated.py

Generative-VQA-V2-Curated.py

@@ -1,95 +0,0 @@
-# Generative-VQA-V2-Curated.py
-"""Custom loading script for Generative VQA V2 Curated dataset."""
-
-import datasets
-import pandas as pd
-import os
-from pathlib import Path
-
-_CITATION = """\
-@misc{devarajan_genvqa_2026,
-  author = {Devarajan},
-  title = {Generative-VQA-V2-Curated: A Balanced Dataset for Open-Ended Generative VQA},
-  year = {2026},
-  publisher = {Hugging Face},
-  howpublished = {\\url{https://huggingface.co/datasets/Deva8/Generative-VQA-V2-Curated}}
-}
-"""
-
-_DESCRIPTION = """\
-A curated, balanced, and cleaned version of the VQA v2 dataset specifically optimized for Generative Visual Question Answering.
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/Deva8/Generative-VQA-V2-Curated"
-
-_LICENSE = "MIT"
-
-_URLS = {
-    "metadata": "main_metadata.csv",
-    "images": "gen_vqa_v2-images.zip",
-}
-
-
-class GenerativeVQAV2Curated(datasets.GeneratorBasedBuilder):
-    """Generative VQA V2 Curated dataset."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "image_id": datasets.Value("int64"),
-                "question_id": datasets.Value("int64"),
-                "question": datasets.Value("string"),
-                "answer": datasets.Value("string"),
-                "image": datasets.Image(),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        urls_to_download = _URLS
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "metadata_path": downloaded_files["metadata"],
-                    "images_dir": downloaded_files["images"],
-                },
-            ),
-        ]
-
-    def _generate_examples(self, metadata_path, images_dir):
-        """Yields examples."""
-        # Read metadata
-        df = pd.read_csv(metadata_path)
-
-        for idx, row in df.iterrows():
-            # Construct image path
-            # file_name format: gen_vqa_v2-images/gen_vqa_v22/images/COCO_train2014_000000429568.jpg
-            image_path = os.path.join(images_dir, row["file_name"])
-            
-            # Handle case where extraction created nested folder
-            if not os.path.exists(image_path):
-                # Try alternative path
-                alt_path = os.path.join(images_dir, "gen_vqa_v22", "images", 
-                                       os.path.basename(row["file_name"]))
-                if os.path.exists(alt_path):
-                    image_path = alt_path
-
-            yield idx, {
-                "image_id": row["image_id"],
-                "question_id": row["question_id"],
-                "question": row["question"],
-                "answer": row["answer"],
-                "image": image_path,
-            }