Convert to parquet (loading scripts are no longer supported)

README.md

@@ -18,6 +18,57 @@ task_categories:
 - image-classification
 task_ids:
 - multi-class-image-classification
+dataset_info:
+  features:
+  - name: file
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': text-only
+          '1': illustrations
+  - name: pub_date
+    dtype: timestamp[ns]
+  - name: page_seq_num
+    dtype: int64
+  - name: edition_seq_num
+    dtype: int64
+  - name: batch
+    dtype: string
+  - name: lccn
+    dtype: string
+  - name: box
+    list: float32
+  - name: score
+    dtype: float64
+  - name: ocr
+    dtype: string
+  - name: place_of_publication
+    dtype: string
+  - name: geographic_coverage
+    dtype: string
+  - name: name
+    dtype: string
+  - name: publisher
+    dtype: string
+  - name: url
+    dtype: string
+  - name: page_url
+    dtype: string
+  - name: image
+    dtype: image
+  splits:
+  - name: train
+    num_bytes: 48233897
+    num_examples: 549
+  download_size: 48045363
+  dataset_size: 48233897
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
 
 

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2e3278cf7eb2744ab80ad1576df7853a12217a8289b47812f395f3c56330bcdf
+size 48045363

dataset_infos.json

@@ -1 +1 @@
-{"default": {"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. \n", "citation": "@dataset{van_strien_daniel_2021_5838410,\n  author       = {van Strien, Daniel},\n  title        = {{19th Century United States Newspaper Advert images \n                   with 'illustrated' or 'non illustrated' labels}},\n  month        = oct,\n  year         = 2021,\n  publisher    = {Zenodo},\n  version      = {0.0.1},\n  doi          = {10.5281/zenodo.5838410},\n  url          = {https://doi.org/10.5281/zenodo.5838410}}\n", "homepage": "https://doi.org/10.5281/zenodo.5838410", "license": "Public Domain", "features": {"file": {"dtype": "string", "id": null, "_type": "Value"}, "image": {"decode": true, "id": null, "_type": "Image"}, "label": {"num_classes": 2, "names": ["text-only", "illustrations"], "id": null, "_type": "ClassLabel"}, "pub_date": {"dtype": "timestamp[ns]", "id": null, "_type": "Value"}, "page_seq_num": {"dtype": "int64", "id": null, "_type": "Value"}, "edition_seq_num": {"dtype": "int64", "id": null, "_type": "Value"}, "batch": {"dtype": "string", "id": null, "_type": "Value"}, "lccn": {"dtype": "string", "id": null, "_type": "Value"}, "box": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "score": {"dtype": "float64", "id": null, "_type": "Value"}, "ocr": {"dtype": "string", "id": null, "_type": "Value"}, "place_of_publication": {"dtype": "string", "id": null, "_type": "Value"}, "geographic_coverage": {"dtype": "string", "id": null, "_type": "Value"}, "name": {"dtype": "string", "id": null, "_type": "Value"}, "publisher": {"dtype": "string", "id": null, "_type": "Value"}, "url": {"dtype": "string", "id": null, "_type": "Value"}, "page_url": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "illustrated_ads", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 773969, "num_examples": 549, "dataset_name": "illustrated_ads"}}, "download_checksums": {"https://zenodo.org/record/5838410/files/images.zip?download=1": {"num_bytes": 48063000, "checksum": "05f1562bca55a44ff00ad52aec19b59ce10566d6d3d169ef75e0224e4a514295"}, "https://zenodo.org/record/5838410/files/ads.csv?download=1": {"num_bytes": 47362, "checksum": "001098f61be7630d985ffc61713c620a9a78518bda6f7ae569c43d37e032cccb"}, "https://zenodo.org/record/5838410/files/sample.csv?download=1": {"num_bytes": 876387, "checksum": "74f585434b9b644c30fbafe9ff400d52dbfb51d620551a23194ecf961db5d697"}}, "download_size": 48986749, "post_processing_size": null, "dataset_size": 773969, "size_in_bytes": 49760718}}
+{"default": {"description": "", "citation": "", "homepage": "", "license": "", "features": {"file": {"dtype": "string", "_type": "Value"}, "label": {"names": ["text-only", "illustrations"], "_type": "ClassLabel"}, "pub_date": {"dtype": "timestamp[ns]", "_type": "Value"}, "page_seq_num": {"dtype": "int64", "_type": "Value"}, "edition_seq_num": {"dtype": "int64", "_type": "Value"}, "batch": {"dtype": "string", "_type": "Value"}, "lccn": {"dtype": "string", "_type": "Value"}, "box": {"feature": {"dtype": "float32", "_type": "Value"}, "_type": "List"}, "score": {"dtype": "float64", "_type": "Value"}, "ocr": {"dtype": "string", "_type": "Value"}, "place_of_publication": {"dtype": "string", "_type": "Value"}, "geographic_coverage": {"dtype": "string", "_type": "Value"}, "name": {"dtype": "string", "_type": "Value"}, "publisher": {"dtype": "string", "_type": "Value"}, "url": {"dtype": "string", "_type": "Value"}, "page_url": {"dtype": "string", "_type": "Value"}, "image": {"_type": "Image"}}, "config_name": "default", "splits": {"train": {"name": "train", "num_bytes": 48233897, "num_examples": 549, "dataset_name": null}}, "download_size": 48045363, "dataset_size": 48233897, "size_in_bytes": 96279260}}

illustrated_ads.py

@@ -1,135 +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 ast
-import os
-import pandas as pd
-import datasets
-
-_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"
-
-_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",
-}
-
-
-class IllustratedAds(datasets.GeneratorBasedBuilder):
-    """Illustated Historic Newspaper Ads datasets"""
-
-    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(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        images = dl_manager.download_and_extract(_URLS)
-        annotations = dl_manager.download(
-            [
-                "https://zenodo.org/record/5838410/files/ads.csv?download=1",
-                "https://zenodo.org/record/5838410/files/sample.csv?download=1",
-            ]
-        )
-        df_labels = pd.read_csv(annotations[0], index_col=0)
-        df_metadata = pd.read_csv(
-            annotations[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()
-        annotations = df.to_dict(orient="records")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "images": images,
-                    "annotations": annotations,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, images, annotations):
-        for id_, row in enumerate(annotations):
-            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"] = os.path.join(images, image)
-            yield id_, row