Create HuffPost.py

HuffPost.py

@@ -0,0 +1,147 @@
+# 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.
+
+"""HuffPost Dataset."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+
+_CITATION = """\
+@book{book,
+  author = {Misra, Rishabh and Grover, Jigyasa},
+  year = {2021},
+  month = {01},
+  pages = {},
+  title = {Sculpting Data for ML: The first act of Machine Learning},
+  isbn = {978-0-578-83125-1}
+}
+
+@dataset{dataset,
+  author = {Misra, Rishabh},
+  year = {2018},
+  month = {06},
+  pages = {},
+  title = {News Category Dataset},
+  doi = {10.13140/RG.2.2.20331.18729}
+}
+"""
+
+
+_DESCRIPTION = """\
+A dataset of approximately 200K news headlines from the year 2012 to 2018 collected from HuffPost."""
+
+_HOMEPAGE = "https://www.kaggle.com/datasets/rmisra/news-category-dataset"
+
+_LICENSE = "CC0: Public Domain"
+
+_URLS = { "train":"https://huggingface.co/datasets/khalidalt/HuffPost/resolve/main/News_Category_Dataset_v2.json",
+}
+
+
+
+class HuffPost(datasets.GeneratorBasedBuilder):
+    """HuffPost Dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="text", version=VERSION, description="text of the data"),
+    ]
+
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "category": datasets.Value("string"),
+                "headline": datasets.Value("string"),
+                "authors": datasets.Value("string"),
+                "link": datasets.Value("string"),
+                "short_description": datasets.Value("string"),
+                "date": dataset.Value("string"),
+                # These are the features of your dataset like images, labels ...
+            }
+            )
+
+        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):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "train.json"),
+                    "split": "train",
+                },
+            ),
+
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+
+                # Yields examples as (key, example) tuples
+                yield key, {
+                    "category": data["category"],
+                    "headline": data["headline"],
+                    "authors": data["authors"],
+                    "link": data["link"],
+                    "short_description": data["short_description"],
+                    "date": data["date"],
+                }