# 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. | |
"""TODO: Add a description here.""" | |
import csv | |
import json | |
import re | |
import pdb | |
import datasets | |
# TODO: Add BibTeX citation | |
# Find for instance the citation on arxiv or on the dataset repo/website | |
_CITATION = """\ | |
<bibtext> | |
""" | |
# TODO: Add description of the dataset here | |
# You can copy an official description | |
_DESCRIPTION = """\ | |
The SciTechNews dataset consists of scientific papers paired with their corresponding | |
press release snippet mined from ACM TechNews. | |
This dataset is designed for the task for automatic science journalism, a task that requires summarization, | |
text simplification, and style transfer | |
""" | |
# TODO: Add a link to an official homepage for the dataset here | |
_HOMEPAGE = "https://github.com/ronaldahmed/scitechnews" | |
# TODO: Add the licence for the dataset here if you can find it | |
_LICENSE = "CC BY-SA 3.0" | |
# TODO: Add link to the official dataset URLs here | |
# The HuggingFace dataset library don't host the datasets but only point to the original files | |
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
_URLs = { | |
"train": "train.json", | |
"validation": "valid.json", | |
"test": "test.json", | |
} | |
class SciTechNews(datasets.GeneratorBasedBuilder): | |
"""A summarization dataset with multiple domains.""" | |
VERSION = datasets.Version("0.1.0") | |
# If you need to make complex sub-parts in the datasets with configurable options | |
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
# BUILDER_CONFIG_CLASS = MyBuilderConfig | |
# You will be able to load one or the other configurations in the following list with | |
# data = datasets.load_dataset('my_dataset', 'first_domain') | |
# data = datasets.load_dataset('my_dataset', 'second_domain') | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="scitechnews", version=VERSION, description="SciTechNews dataset for science journalism" | |
), | |
] | |
def _info(self): | |
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
features = datasets.Features( | |
{ | |
"id": datasets.Value("string"), | |
"pr-title": datasets.Value("string"), | |
"pr-article": datasets.Value("string"), | |
"pr-summary": datasets.Value("string"), | |
"sc-title": datasets.Value("string"), | |
"sc-article": datasets.Value("string"), | |
"sc-abstract": datasets.Value("string"), | |
"sc-section_names": datasets.features.Sequence(datasets.Value("string")), | |
"sc-sections": datasets.features.Sequence(datasets.Value("string")), | |
"sc-authors": datasets.features.Sequence(datasets.Value("string")), | |
} | |
) | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
features=features, | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
# license=_LICENSE, | |
# Citation for the dataset | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration | |
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs | |
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
urls_to_download = _URLs | |
downloaded_files = dl_manager.download_and_extract(urls_to_download) | |
return [ | |
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"],"split": "train"}), | |
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation"], "split":"validation"}), | |
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"], "split":"test"}), | |
] | |
def _generate_examples( | |
self, | |
filepath, | |
split, # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
): | |
"""Yields examples as (key, example) tuples.""" | |
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. | |
# The `key` is here for legacy reason (tfds) and is not important in itself. | |
for row in open(filepath, encoding="utf-8"): | |
data = json.loads(row) | |
id_ = data["id"] | |
# pdb.set_trace() | |
yield id_, data | |