Convert dataset to Parquet (#1)

- Convert dataset to Parquet (58ef1a49b4507784ee97b55d872f2f312a44c593)
- Delete data file (6ba55315d4aff8de104eb0f93fe1b77e1c1886c3)
- Delete loading script (4a3c7b36c53e6184b041742f01396c07c4e8ed83)
- Delete data file (f8e10247fd87f183db6ebe28f66972d7d267580c)
- Delete data file (b4a89eef2de99a6dff1cdfafdf005a6caf909e48)
- Delete data file (d32105fc0daf19eeaa6db7aad13775169a734945)
- Delete data file (6942984f64ca5e78dccd010d37dda52556c033fe)
- Delete data file (5d2f808bcb746b931910751e2672610609c97ecf)
- Delete data file (e1ffd9463cc40b5da4366c4b671246bd3345f2b0)
- Delete data file (2b8aad2a27f840952a04a4b160f5fdc074f519e9)

README.md

@@ -1,21 +1,66 @@
 ---
+language:
+- en
 license: cc-by-nc-4.0
+size_categories:
+- 10K<n<100K
 task_categories:
 - image-to-text
 - visual-question-answering
 - image-classification
-language:
-- en
 pretty_name: PatFig
-size_categories:
-- 10K<n<100K
 configs:
 - config_name: default
   data_files:
   - split: train
-    path: "train/*"
+    path: data/train-*
   - split: test
-    path: "test/*"
+    path: data/test-*
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: image_name
+    dtype: string
+  - name: pub_number
+    dtype: string
+  - name: title
+    dtype: string
+  - name: figs_norm
+    sequence: string
+  - name: short_description
+    sequence: string
+  - name: long_description
+    sequence: string
+  - name: short_description_token_count
+    dtype: int64
+  - name: long_description_token_count
+    dtype: int64
+  - name: draft_class
+    dtype: string
+  - name: cpc_class
+    dtype: string
+  - name: relevant_terms
+    list:
+    - name: element_identifier
+      dtype: string
+    - name: terms
+      sequence: string
+  - name: associated_claims
+    dtype: string
+  - name: compound
+    dtype: bool
+  - name: references
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 1998632864.066
+    num_examples: 17386
+  - name: test
+    num_bytes: 118291788.0
+    num_examples: 998
+  download_size: 1735361199
+  dataset_size: 2116924652.066
 ---
 
 # PatFig Dataset

PatFigVQA_example.png → data/test-00000-of-00001.parquet

@@ -1,3 +1,3 @@
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-size 544079
+oid sha256:d9fc5a94199b4f6e957f61a6356b7d962e3a085f402a6c535a27df2f9209ab48
+size 90327493

patfig_funny_logo.png → data/train-00000-of-00004.parquet

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-size 4155552
+oid sha256:79f1ff7130306363c2e6a005052d7146c1d9f217b8724188159a390457f0a0e0
+size 408046427

PatFig_example.png → data/train-00001-of-00004.parquet

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-oid sha256:3c626eeb8727520da886493356c116cc5165a0104fa7a3445bce92cb4117591c
-size 260254
+oid sha256:8c0164569ba8ddffb93d0609a36fcf51d5f25a77cadb231eff3beeb2ed922d0a
+size 408133098

test/annotations_test.zip → data/train-00002-of-00004.parquet

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-oid sha256:c32c89792df8ce01e34fb06ec0e7198972b7863b7bc8ae0447707f715f585ee2
-size 648128
+oid sha256:8cbb82ca759a1a7dfa90cdb3cdcd208f552bcd7ec948f70d4545338cf8e76eaf
+size 427019438

data/train-00003-of-00004.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:84833998f19c27efbac5d61c0bdea5fad757ac8704c46b99057bcfba8be29ff8
+size 401834743

patfig.py

@@ -1,78 +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
-import json
-
-_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.
-"""
-
-_BASE_URL = "https://huggingface.co/datasets/lcolonn/patfig/resolve/main/"
-_METADATA_URLS = {
-    "annotations_train": "train/annotations_train.zip",
-    "annotations_test": "test/annotations_test.zip"
-}
-_IMAGES_URLS = {
-    "test_images": "train/train_images.tar.gz",
-    "train_images": "test/test_images.tar.gz",
-}
-_URLS = {
-    "train_images": "train/train_images.tar.gz",
-    "test_images": "test/test_images.tar.gz",
-    "annotations_train": "train/annotations_train.zip",
-    "annotations_test": "test/annotations_test.zip",
-}
-
-
-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):
-        urls_to_download = {key: _BASE_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": f'{downloaded_files["train_images"]}/train', "annotations_dir": f'{downloaded_files["annotations_train"]}/annotations_train.json'}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"images_dir": f'{downloaded_files["test_images"]}/test', "annotations_dir": f'{downloaded_files["annotations_test"]}/annotations_test.json'}
-            ),
-        ]
-
-    def _generate_examples(self, images_dir: str, annotations_dir: str):
-        with open(annotations_dir, "r") as f:
-            data = json.load(f)
-            for idx, record in data.items():
-                image_path = os.path.join(images_dir, record["pub_number"], record["image_name"])
-                yield idx, {
-                    "image": image_path,
-                    **record,
-                }

test/test_images.tar.gz

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-size 82322544

train/annotations_train.zip

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-size 10674301

train/train_images.tar.gz

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