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-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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comedy-scripts.py

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-# coding=utf-8
-# 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.
-"""Lyrics dataset parsed from Genius"""
-
-
-import csv
-import json
-import os
-import gzip
-
-import datasets
-
-
-_CITATION = """\
-@InProceedings{huggingartists:dataset,
-title = {Lyrics dataset},
-author={Aleksey Korshuk
-},
-year={2021}
-}
-"""
-
-
-_DESCRIPTION = """\
-This dataset is designed to generate lyrics with HuggingArtists.
-"""
-
-# Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://github.com/AlekseyKorshuk/huggingartists"
-
-# Add the licence for the dataset here if you can find it
-_LICENSE = "All rights belong to copyright holders"
-
-_URL = "https://huggingface.co/datasets/AlekseyKorshuk/comedy-scripts/resolve/main/comedy-scripts.json"
-
-# Name of the dataset
-class LyricsDataset(datasets.GeneratorBasedBuilder):
-    """Lyrics dataset"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        features = datasets.Features(
-                {
-                    "text": 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,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # 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):
-        """Returns SplitGenerators."""
-        # 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
-
-        data_dir = dl_manager.download_and_extract(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_dir,
-                    "split": "train",
-                },
-            ),
-        ]
-        
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples as (key, example) tuples."""
-        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for id, pred in enumerate(data[split]):
-                yield id, {"text": pred}

default/comedy-scripts-train.parquet

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