Delete goodwiki_long_toy.py
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goodwiki_long_toy.py
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# coding=utf-8
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# Copyright 2023 Devrim Cavusoglu and the HuggingFace Datasets Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""Goodwiki Long Subset."""
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import json
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import datasets
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logger = datasets.logging.get_logger(__name__)
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_DESCRIPTION = """\
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Dataset consisting of long wikipedia articles in markdown format.
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"""
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_URLS = {
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"train": [
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"data/train.jsonl",
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],
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"test": [
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"data/test.jsonl",
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]
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}
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class GoodWikiLongDatasetConfig(datasets.BuilderConfig):
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"""BuilderConfig for Dataset."""
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def __init__(self, **kwargs):
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"""BuilderConfig for Dataset.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(GoodWikiLongDatasetConfig, self).__init__(**kwargs)
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@property
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def features(self):
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return {
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"id": datasets.Value("string"),
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"url": datasets.Value("null"),
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"title": datasets.Value("string"),
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"text": datasets.Value("string"),
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"revid": datasets.Value("string"),
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"description": datasets.Value("string"),
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"categories": datasets.Sequence(datasets.Value("string")),
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}
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class GoodWikiLongDataset(datasets.GeneratorBasedBuilder):
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"""WikiLongDataset Classification dataset. Version 1.0."""
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BUILDER_CONFIGS = [
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GoodWikiLongDatasetConfig(
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version=datasets.Version("1.0.0", ""), description="Goodwiki Long Articles"
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)
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]
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BUILDER_CONFIG_CLASS = GoodWikiLongDatasetConfig
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(self.config.features),
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)
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def _split_generators(self, dl_manager):
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data_dir = dl_manager.download_and_extract(_URLS)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir["train"]}
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),
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]
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def _generate_examples(self, filepath):
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"""This function returns the examples in the raw (text) form."""
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logger.info("generating examples from = %s", filepath)
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if isinstance(filepath, str):
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filepath = [filepath]
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key = 0
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for path in filepath:
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with open(path, encoding="utf-8") as data:
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for article_data in data:
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article = json.loads(article_data)
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article["id"] = article.pop("pageid")
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article["text"] = "# " + article["title"] + "\n\n" + article.pop("markdown")
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article["url"] = None
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yield key, article
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key += 1
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