refactor: structure

annotations_train.csv

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:57df281915f932015eda3ad263b791c9459fad1918b57765fa33411d5e12f818
-size 69511671

patfig.py

@@ -1,70 +0,0 @@
-import datasets
-from datasets import load_dataset, Dataset, Value, Sequence, Features, DatasetInfo, GeneratorBasedBuilder, Image
-
-from pathlib import Path
-import os
-import pandas as pd
-
-_DESCRIPTION = """\ The PatFig Dataset is a curated collection of over 18,000 patent images from more than 7,
-000 European patent applications, spanning the year 2020. It aims to provide a comprehensive resource for research 
-and applications in image captioning, abstract reasoning, patent analysis, and automated documentprocessing. The 
-overarching goal of this dataset is to advance the research in visually situated language understanding towards more 
-hollistic consumption of the visual and textual data.
-"""
-
-_URL = "https://huggingface.co/datasets/lcolonn/patfig/resolve/main/"
-_URLS = {
-    "train_images": "train_images.tar.gz",
-    "test_images": "test_images.tar.gz",
-    "annotations_train": "annotations_train.csv",
-    "annotations_test": "annotations_test.csv",
-}
-
-
-class PatFig(GeneratorBasedBuilder):
-    """DatasetBuilder for patfig dataset."""
-
-    def _info(self):
-        return DatasetInfo(
-            description=_DESCRIPTION,
-            features=Features({
-                "image": Image(),
-                "image_name": Value("string"),
-                "pub_number": Value("string"),
-                "title": Value("string"),
-                "figs_norm": Sequence(feature=Value("string"), length=-1),
-                "short_description": Sequence(feature=Value("string"), length=-1),
-                "long_description": Sequence(feature=Value("string"), length=-1),
-                "short_description_token_count": Value("int64"),
-                "long_description_token_count": Value("int64"),
-                "draft_class": Value("string"),
-                "cpc_class": Value("string"),
-                "relevant_terms": [{'element_identifier': Value("string"), "terms": Sequence(feature=Value("string"), length=-1)}],
-                "associated_claims": Value("string"),
-                "compound": Value("bool"),
-                "references": Sequence(feature=Value(dtype='string'), length=-1),
-            }),
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager):
-        # FIXME: Currently downloads all the files regardless of the split
-        urls_to_download = {key: _URL + fname for key, fname in _URLS.items()}
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"images_dir": downloaded_files["train_images"], "annotations_dir": downloaded_files["annotations_train"]}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"images_dir": f'{downloaded_files["test_images"]}/test', "annotations_dir": downloaded_files["annotations_test"]}
-            ),
-        ]
-
-    def _generate_examples(self, images_dir: str, annotations_dir: str):
-        df = pd.read_csv(annotations_dir)
-
-        for idx, row in df.iterrows():
-            image_path = os.path.join(images_dir, row["pub_number"], row["image_name"])
-            yield idx, {
-                "image": image_path,
-                **row.to_dict(),
-            }