ablit.py

# 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: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{roemmele2023ablit,
    title={AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature},
    author={Roemmele, Melissa and Shaffer, Kyle and Olsen, Katrina and Wang, Yiyi and DeNeefe, Steve},
    booktitle = {Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
    publisher = {Association for Computational Linguistics},
    year={2023}
}
"""

_VERSION = datasets.Version("1.0.0")

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This dataset contains abridged versions of 10 classic English literature books,
aligned with their original versions on various passage levels.\
The abridgements were written and made publically available by Emma Laybourn: \
http://www.englishliteratureebooks.com/classicnovelsabridged.html.\
This is the first known dataset for NLP research that focuses on the abridgement task.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/roemmele/AbLit"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

_PASSAGE_SIZES = ["chapters", "rows", "sentences",
                  "paragraphs", "chunks-10-sentences"]

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {size: "./{}".format(size) for size in _PASSAGE_SIZES}


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class AbLitDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # 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=size, version=_VERSION,
                               description="Aligned passages of {} length".format(size))
        for size in _PASSAGE_SIZES
    ]

    # It's not mandatory to have a default configuration. Just use one if it make sense.
    DEFAULT_CONFIG_NAME = None

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        # This is the name of the configuration selected in BUILDER_CONFIGS above
        features = datasets.Features(
            {
                "original": datasets.Value("string"),
                "abridged": datasets.Value("string"),
                "book": datasets.Value("string"),
                "chapter": datasets.Value("string")
                # These are the features of your dataset like images, labels ...
            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            # Here we define them above because they are different between the two configurations
            features=features,
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # 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 = {split: "{}/{}.jsonl".format(_URLS[self.config.name], split)
                for split in ('train', 'dev', 'test')}
        urls = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name="train",
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": urls["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name="dev",
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": urls["dev"],
                    "split": "dev",
                },
            ),
            datasets.SplitGenerator(
                name="test",
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": urls["test"],
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            for key, item in enumerate(f):
                item = json.loads(item)
                # Yields examples as (key, example) tuples
                yield key, {
                    "original": item["original"],
                    "abridged": item["abridged"],
                    "book": item["book"],
                    "chapter": item["chapter"],
                }