upload dataset script

illustrated_ads.py

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+# 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 ast
+import pandas as pd
+import datasets
+from PIL import Image
+from pathlib import Path
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@dataset{van_strien_daniel_2021_5838410,
+  author       = {van Strien, Daniel},
+  title        = {{19th Century United States Newspaper Advert images 
+                   with 'illustrated' or 'non illustrated' labels}},
+  month        = oct,
+  year         = 2021,
+  publisher    = {Zenodo},
+  version      = {0.0.1},
+  doi          = {10.5281/zenodo.5838410},
+  url          = {https://doi.org/10.5281/zenodo.5838410}}
+"""
+
+
+_DESCRIPTION = """\
+The Dataset contains images derived from the Newspaper Navigator (news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection. 
+"""
+
+_HOMEPAGE = "https://doi.org/10.5281/zenodo.5838410"
+
+_LICENSE = "Public Domain"
+
+
+_URLS = "https://zenodo.org/record/5838410/files/images.zip?download=1"
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class IllustratedAds(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "file": datasets.Value("string"),
+                "image": datasets.Image(),
+                "label": datasets.ClassLabel(names=["text-only", "illustrations"]),
+                "pub_date": datasets.Value("timestamp[ns]"),
+                "page_seq_num": datasets.Value("int64"),
+                "edition_seq_num": datasets.Value("int64"),
+                "batch": datasets.Value("string"),
+                "lccn": datasets.Value("string"),
+                "box": datasets.Sequence(datasets.Value("float32")),
+                "score": datasets.Value("float64"),
+                "ocr": datasets.Value("string"),
+                "place_of_publication": datasets.Value("string"),
+                "geographic_coverage": datasets.Value("string"),
+                "name": datasets.Value("string"),
+                "publisher": datasets.Value("string"),
+                "url": datasets.Value("string"),
+                "page_url": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_dir": Path(data_dir),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dir):
+        dtypes = {
+            "page_seq_num": "int64",
+            "edition_seq_num": "int64",
+            "batch": "string",
+            "lccn": "string",
+            "score": "float64",
+            "place_of_publication": "string",
+            "name": "string",
+            "publisher": "string",
+            "url": "string",
+            "page_url": "string",
+        }
+        df_labels = pd.read_csv(
+            "https://zenodo.org/record/5838410/files/ads.csv?download=1", index_col=0
+        )
+        df_metadata = pd.read_csv(
+            "https://zenodo.org/record/5838410/files/sample.csv?download=1",
+            index_col=0,
+            dtype=dtypes,
+        )
+        df_metadata["file"] = df_metadata.filepath.str.replace("/", "_")
+        df_metadata = df_metadata.set_index("file", drop=True)
+        df = df_labels.join(df_metadata)
+        df = df.reset_index()
+        data = df.to_dict(orient="records")
+        for id_, row in enumerate(data):
+            box = ast.literal_eval(row["box"])
+            row["box"] = box
+            row.pop("filepath")
+            ocr = " ".join(ast.literal_eval(row["ocr"]))
+            row["ocr"] = ocr
+            image = row["file"]
+            row["image"] = Image.open(Path(data_dir / image))
+            yield id_, row