add data script and README
Browse files- README.md +7 -3
- bart_cnndm.py +275 -0
README.md
CHANGED
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Modification of the cnn_dailymail dataset in Hugging Face. The main goal is to reproduce the results on BART.
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References: https://github.com/facebookresearch/fairseq/issues/1401
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Major changes:
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1. remove the space in " ." in fix_missing_period.
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2. remove "(CNN)" in article.
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bart_cnndm.py
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# coding=utf-8
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# Copyright 2020 The TensorFlow Datasets Authors 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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"""CNN/DailyMail Summarization dataset, non-anonymized version for BART model.
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Major changes:
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1. remove the space in " ." in fix_missing_period.
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2. remove "(CNN)" in article.
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The goal is to reproduce the results in BART.
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References: https://github.com/facebookresearch/fairseq/issues/1401
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"""
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import hashlib
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import os
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import datasets
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logger = datasets.logging.get_logger(__name__)
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_HOMEPAGE = "https://github.com/abisee/cnn-dailymail"
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_DESCRIPTION = """\
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CNN/DailyMail non-anonymized summarization dataset.
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There are two features:
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- article: text of news article, used as the document to be summarized
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- highlights: joined text of highlights with <s> and </s> around each
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highlight, which is the target summary
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"""
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# The second citation introduces the source data, while the first
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# introduces the specific form (non-anonymized) we use here.
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_CITATION = """\
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@article{DBLP:journals/corr/SeeLM17,
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author = {Abigail See and
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Peter J. Liu and
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Christopher D. Manning},
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title = {Get To The Point: Summarization with Pointer-Generator Networks},
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journal = {CoRR},
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volume = {abs/1704.04368},
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year = {2017},
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url = {http://arxiv.org/abs/1704.04368},
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archivePrefix = {arXiv},
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eprint = {1704.04368},
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timestamp = {Mon, 13 Aug 2018 16:46:08 +0200},
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biburl = {https://dblp.org/rec/bib/journals/corr/SeeLM17},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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@inproceedings{hermann2015teaching,
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title={Teaching machines to read and comprehend},
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author={Hermann, Karl Moritz and Kocisky, Tomas and Grefenstette, Edward and Espeholt, Lasse and Kay, Will and Suleyman, Mustafa and Blunsom, Phil},
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booktitle={Advances in neural information processing systems},
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pages={1693--1701},
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year={2015}
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}
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"""
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_DL_URLS = {
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"cnn_stories": "https://huggingface.co/datasets/cnn_dailymail/resolve/11343c3752184397d56efc19a8a7cceb68089318/data/cnn_stories.tgz",
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"dm_stories": "https://huggingface.co/datasets/cnn_dailymail/resolve/11343c3752184397d56efc19a8a7cceb68089318/data/dailymail_stories.tgz",
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"train": "https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_train.txt",
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"validation": "https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_val.txt",
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"test": "https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_test.txt",
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}
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_HIGHLIGHTS = "highlights"
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_ARTICLE = "article"
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_SUPPORTED_VERSIONS = [
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# Using cased version.
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datasets.Version("3.0.0", "Using cased version."),
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# Same data as 0.0.2
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datasets.Version("1.0.0", ""),
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# Having the model predict newline separators makes it easier to evaluate
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# using summary-level ROUGE.
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datasets.Version("2.0.0", "Separate target sentences with newline."),
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]
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_DEFAULT_VERSION = datasets.Version("3.0.0", "Using cased version.")
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class CnnDailymailConfig(datasets.BuilderConfig):
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"""BuilderConfig for CnnDailymail."""
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def __init__(self, **kwargs):
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"""BuilderConfig for CnnDailymail.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(CnnDailymailConfig, self).__init__(**kwargs)
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def _get_url_hashes(path):
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"""Get hashes of urls in file."""
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urls = _read_text_file_path(path)
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def url_hash(u):
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h = hashlib.sha1()
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try:
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u = u.encode("utf-8")
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except UnicodeDecodeError:
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logger.error("Cannot hash url: %s", u)
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h.update(u)
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return h.hexdigest()
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return {url_hash(u) for u in urls}
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def _get_hash_from_path(p):
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"""Extract hash from path."""
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return os.path.splitext(os.path.basename(p))[0]
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DM_SINGLE_CLOSE_QUOTE = "\u2019" # unicode
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DM_DOUBLE_CLOSE_QUOTE = "\u201d"
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# acceptable ways to end a sentence
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END_TOKENS = [
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".",
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"!",
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"?",
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"...",
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"'",
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"`",
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'"',
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DM_SINGLE_CLOSE_QUOTE,
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DM_DOUBLE_CLOSE_QUOTE,
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")",
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]
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def _read_text_file_path(path):
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with open(path, "r", encoding="utf-8") as f:
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lines = [line.strip() for line in f]
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return lines
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def _read_text_file(file):
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return [line.decode("utf-8").strip() for line in file]
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def _get_art_abs(story_file, tfds_version):
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"""Get abstract (highlights) and article from a story file path."""
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# Based on https://github.com/abisee/cnn-dailymail/blob/master/
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# make_datafiles.py
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lines = _read_text_file(story_file)
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# The github code lowercase the text and we removed it in 3.0.0.
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# Put periods on the ends of lines that are missing them
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# (this is a problem in the dataset because many image captions don't end in
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# periods; consequently they end up in the body of the article as run-on
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# sentences)
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def fix_missing_period(line):
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"""Adds a period to a line that is missing a period."""
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if "@highlight" in line:
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return line
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if not line:
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return line
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if line[-1] in END_TOKENS:
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return line
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return line + "."
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lines = [fix_missing_period(line) for line in lines]
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# Separate out article and abstract sentences
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article_lines = []
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highlights = []
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next_is_highlight = False
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for line in lines:
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if not line:
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continue # empty line
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elif line.startswith("@highlight"):
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next_is_highlight = True
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elif next_is_highlight:
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highlights.append(line)
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else:
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article_lines.append(line)
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# Make article into a single string
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article = " ".join(article_lines)
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if tfds_version >= "2.0.0":
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abstract = "\n".join(highlights)
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else:
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abstract = " ".join(highlights)
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if article[:5] == "(CNN)":
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article = article[5:]
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return article, abstract
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class CnnDailymail(datasets.GeneratorBasedBuilder):
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"""CNN/DailyMail non-anonymized summarization dataset."""
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BUILDER_CONFIGS = [
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CnnDailymailConfig(name=str(version), description="Plain text", version=version)
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for version in _SUPPORTED_VERSIONS
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]
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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(
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{
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_ARTICLE: datasets.Value("string"),
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_HIGHLIGHTS: datasets.Value("string"),
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"id": datasets.Value("string"),
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}
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),
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supervised_keys=None,
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homepage=_HOMEPAGE,
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citation=_CITATION,
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)
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def _vocab_text_gen(self, paths):
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for _, ex in self._generate_examples(paths):
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yield " ".join([ex[_ARTICLE], ex[_HIGHLIGHTS]])
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def _split_generators(self, dl_manager):
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dl_paths = dl_manager.download(_DL_URLS)
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return [
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datasets.SplitGenerator(
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name=split,
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gen_kwargs={
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"urls_file": dl_paths[split],
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"files_per_archive": [
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dl_manager.iter_archive(dl_paths["cnn_stories"]),
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dl_manager.iter_archive(dl_paths["dm_stories"]),
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],
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},
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)
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for split in [
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datasets.Split.TRAIN,
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datasets.Split.VALIDATION,
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datasets.Split.TEST,
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]
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]
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def _generate_examples(self, urls_file, files_per_archive):
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urls = _get_url_hashes(urls_file)
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idx = 0
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for files in files_per_archive:
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for path, file in files:
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hash_from_path = _get_hash_from_path(path)
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if hash_from_path in urls:
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article, highlights = _get_art_abs(file, self.config.version)
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if not article or not highlights:
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continue
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yield idx, {
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_ARTICLE: article,
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_HIGHLIGHTS: highlights,
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"id": hash_from_path,
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}
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idx += 1
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