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