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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.
"""SAMSum dataset."""
import json
import py7zr
import datasets
_CITATION = """
@article{gliwa2019samsum,
title={SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization},
author={Gliwa, Bogdan and Mochol, Iwona and Biesek, Maciej and Wawer, Aleksander},
journal={arXiv preprint arXiv:1911.12237},
year={2019}
}
"""
_DESCRIPTION = """
SAMSum Corpus contains over 16k chat dialogues with manually annotated
summaries.
There are two features:
- dialogue: text of dialogue.
- summary: human written summary of the dialogue.
- id: id of a example.
"""
_HOMEPAGE = "https://arxiv.org/abs/1911.12237"
_LICENSE = "CC BY-NC-ND 4.0"
_URL = "https://huggingface.co/datasets/samsum/resolve/main/data/corpus.7z"
class Samsum(datasets.GeneratorBasedBuilder):
"""SAMSum Corpus dataset."""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="samsum"),
]
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"dialogue": datasets.Value("string"),
"summary": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
path = dl_manager.download(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": (path, "train.json"),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": (path, "test.json"),
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": (path, "val.json"),
"split": "val",
},
),
]
def _generate_examples(self, filepath, split):
"""Yields examples."""
path, fname = filepath
with open(path, "rb") as f:
with py7zr.SevenZipFile(f, "r") as z:
for name, bio in z.readall().items():
if name == fname:
data = json.load(bio)
for example in data:
yield example["id"], example
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