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import datasets |
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import os |
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import json |
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_CITATION = "" |
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_DESCRIPTION = """ |
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Scenario for single document text summarization. |
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Currently supports the following datasets: |
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1. XSum (https://arxiv.org/pdf/1808.08745.pdf) |
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2. CNN/DailyMail non-anonymized (https://arxiv.org/pdf/1704.04368.pdf) |
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Task prompt structure |
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Summarize the given document. |
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Document: {tok_1 ... tok_n} |
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Summary: {tok_1 ... tok_m} |
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Example from XSum dataset |
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Document: {Part of the Broad Road was closed to traffic on Sunday at about 18:00 GMT. |
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The three adults and three children have been taken to Altnagelvin Hospital |
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with non life-threatening injuries. The Fire Service, Northern Ireland Ambulance Service |
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and police attended the crash. The Broad Road has since been reopened.} |
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Summary: {Three adults and three children have been taken to hospital following a crash involving |
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a tractor and a campervan in Limavady, County Londonderry} |
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""" |
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class Summarization(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name=name, version=datasets.Version("1.0.0"), description="") |
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for name in ["xsum", "xsum-sampled", "cnn-dm"] |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"article": datasets.Value("string"), |
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"summary": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage="", |
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license="", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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train_json = dl_manager.download(os.path.join(self.config.name, "train.jsonl")) |
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test_json = dl_manager.download(os.path.join(self.config.name, "test.jsonl")) |
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val_json = dl_manager.download(os.path.join(self.config.name, "validation.jsonl")) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"path": train_json}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"path": test_json}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"path": val_json}, |
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) |
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] |
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def _generate_examples(self, path): |
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with open(path, encoding="utf-8") as f: |
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for key, row in enumerate(f): |
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yield key, json.loads(row) |
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