Add the remaining shards, full card, and drop the loading script

Consolidates the split-across-PRs upload: the remaining train shards and the test split are copied in from the sibling PR so this branch holds the complete dataset.

README.md

@@ -4,30 +4,30 @@ dataset_info:
   - name: image
     dtype: image
   - name: labels
-    sequence:
+    list:
       class_label:
         names:
-          0: greek
-          1: antiqua
-          2: other_font
-          3: not_a_font
-          4: italic
-          5: rotunda
-          6: textura
-          7: fraktur
-          8: schwabacher
-          9: hebrew
-          10: bastarda
-          11: gotico_antiqua
+          '0': greek
+          '1': antiqua
+          '2': other_font
+          '3': not_a_font
+          '4': italic
+          '5': rotunda
+          '6': textura
+          '7': fraktur
+          '8': schwabacher
+          '9': hebrew
+          '10': bastarda
+          '11': gotico_antiqua
   splits:
-  - name: test
-    num_bytes: 2345451
-    num_examples: 10757
   - name: train
-    num_bytes: 5430875
+    num_bytes: 31799485655
     num_examples: 24866
-  download_size: 44212934313
-  dataset_size: 7776326
+  - name: test
+    num_bytes: 12966068036
+    num_examples: 10757
+  download_size: 44768768396
+  dataset_size: 44765553691
 annotations_creators:
 - expert-generated
 language: []
@@ -44,6 +44,13 @@ task_categories:
 - image-classification
 task_ids:
 - multi-label-image-classification
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
 ---
 
 # Early Printed Books Font Detection
@@ -106,7 +113,18 @@ Counted from `labels-training.csv` and `labels-test.csv` in the Zenodo deposit.
 
 **`not_a_font` is not simply "no text".** It co-occurs with a font label on 567 training and 97 test pages, so it marks non-textual content on a page rather than the absence of type. Two training rows (`bsb00031003_15.jpg`, `bsb10199589_13.jpg`) have no label at all and load with an empty `labels` sequence.
 
-**Loading is currently broken.** This repo still ships a legacy loading script, so `load_dataset("biglam/early_printed_books_font_detection")` raises `RuntimeError: Dataset scripts are no longer supported` on `datasets` v4 and later. Until it is converted, the images are available from the [Zenodo deposit](https://doi.org/10.5281/zenodo.3366686) (seven zip files, roughly 44 GB in total, plus a 292 KB labels zip).
+```python
+from datasets import load_dataset
+
+ds = load_dataset("biglam/early_printed_books_font_detection", split="train")
+row = ds[0]
+row["image"]      # the page scan
+row["labels"]     # one to five font groups, e.g. [1, 5]
+ds.features["labels"].feature.int2str(row["labels"])
+```
+
+Pages are high-resolution scans, so stream the dataset if you do not want the whole
+thing on disk: `load_dataset(..., streaming=True)`.
 
 ## Licence
 

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early_printed_books_font_detection.py

@@ -1,131 +0,0 @@
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Dataset of illustrated and non illustrated 19th Century newspaper ads."""
-
-import zipfile
-from pathlib import Path
-
-import datasets
-import requests
-from PIL import Image
-
-# Some of the images are large so we set this to avoid Decompression bomb warnings
-
-Image.MAX_IMAGE_PIXELS = None
-
-_CITATION = """\
-@dataset{seuret_mathias_2019_3366686,
-  author       = {Seuret, Mathias and
-                  Limbach, Saskia and
-                  Weichselbaumer, Nikolaus and
-                  Maier, Andreas and
-                  Christlein, Vincent},
-  title        = {{Dataset of Pages from Early Printed Books with 
-                   Multiple Font Groups}},
-  month        = aug,
-  year         = 2019,
-  publisher    = {Zenodo},
-  version      = {1.0.0},
-  doi          = {10.5281/zenodo.3366686},
-  url          = {https://doi.org/10.5281/zenodo.3366686}
-}
-"""
-
-_DESCRIPTION = """\
-This dataset is composed of photos of various resolution of 35'623 pages of printed books dating from the 15th to the 18th century. Each page has been attributed by experts from one to five labels corresponding to the font groups used in the text, with two extra-classes for non-textual content and fonts not present in the following list:  Antiqua, Bastarda, Fraktur, Gotico Antiqua, Greek, Hebrew, Italic, Rotunda, Schwabacher, and Textura.
-"""
-
-_HOMEPAGE = "https://doi.org/10.5281/zenodo.3366686"
-_LICENSE = "Creative Commons Attribution Non Commercial Share Alike 4.0 International"
-
-ZENDO_REPO_ID = 3366686
-ZENODO_API_URL = f"https://zenodo.org/api/records/{ZENDO_REPO_ID}"
-
-
-class EarlyBookFonts(datasets.GeneratorBasedBuilder):
-    """Early printed book fonts detection dataset"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "image": datasets.Image(),
-                "labels": datasets.Sequence(
-                    datasets.ClassLabel(
-                        names=[
-                            "greek",
-                            "antiqua",
-                            "other_font",
-                            "not_a_font",
-                            "italic",
-                            "rotunda",
-                            "textura",
-                            "fraktur",
-                            "schwabacher",
-                            "hebrew",
-                            "bastarda",
-                            "gotico_antiqua",
-                        ]
-                    )
-                ),
-            }
-        )
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        zenodo_record = requests.get(ZENODO_API_URL).json()
-        urls = sorted(
-            file["links"]["self"]
-            for file in zenodo_record["files"]
-            if file["type"] == "zip"
-        )
-        *image_urls, label_url = urls
-        labels = dl_manager.download(label_url)
-        images = dl_manager.download_and_extract(image_urls)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"images": images, "labels": labels, "split": "training"},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"images": images, "labels": labels, "split": "test"},
-            ),
-        ]
-
-    def _generate_examples(self, images, labels, split):
-        mapping = {}
-        for directory in images:
-            for file in Path(directory).rglob("*"):
-                mapping["/".join(file.parts[-2:])] = file
-        with zipfile.ZipFile(labels) as labelzip:
-            with labelzip.open(f"labels-{split}.csv") as label_csv:
-                for id_, row in enumerate(label_csv.readlines()):
-                    row = row.decode("utf-8")
-                    filename, *labels = row.split(",")
-                    labels = [label.strip("\n") for label in labels]
-                    labels = [label for label in labels if label != "-"]
-                    filename = mapping[filename]
-
-                    image = Image.open(filename)
-                    yield id_, {"image": image, "labels": labels}