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short-jokes.py

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+"""News headlines and categories dataset."""
+
+from __future__ import absolute_import, division, print_function
+
+import datasets
+
+
+_DESCRIPTION = """\
+Copy of [Kaggle dataset](https://www.kaggle.com/abhinavmoudgil95/short-jokes), adding to Huggingface for ease of use.
+
+Description from Kaggle:
+
+Context
+
+Generating humor is a complex task in the domain of machine learning, and it requires the models to understand the deep semantic meaning of a joke in order to generate new ones. Such problems, however, are difficult to solve due to a number of reasons, one of which is the lack of a database that gives an elaborate list of jokes. Thus, a large corpus of over 0.2 million jokes has been collected by scraping several websites containing funny and short jokes.
+
+Visit my Github repository for more information regarding collection of data and the scripts used.
+
+Content
+
+This dataset is in the form of a csv file containing 231,657 jokes. Length of jokes ranges from 10 to 200 characters. Each line in the file contains a unique ID and joke.
+
+Disclaimer
+
+It has been attempted to keep the jokes as clean as possible. Since the data has been collected by scraping websites, it is possible that there may be a few jokes that are inappropriate or offensive to some people.
+"""
+
+_CITATION = None
+
+_TRAIN_DOWNLOAD_URL = "https://raw.githubusercontent.com/Fraser-Greenlee/my-huggingface-datasets/master/data/short-jokes/train.json"
+
+
+class ShortJokes(datasets.GeneratorBasedBuilder):
+    """Short jokes dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    'text': datasets.Value("string"),
+                }
+            ),
+            homepage="https://github.com/Fraser-Greenlee/my-huggingface-datasets",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate examples."""
+        with open(filepath, encoding="utf-8") as txt_lines_file:
+            data = []
+            for line in txt_lines_file:
+                data.append({'text': line})
+
+            for id_, row in enumerate(data):
+                yield id_, row