wiki_cat_sum.py

# 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 os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{perez2019generating,
  title={Generating Summaries with Topic Templates and Structured Convolutional Decoders},
  author={Perez-Beltrachini, Laura and Liu, Yang and Lapata, Mirella},
  booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  pages={5107--5116},
  year={2019}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
Summarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://datashare.ed.ac.uk/handle/10283/3368"

# 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 = {
    "animal": {
        "train": "./main_splits/train-animal.jsonl",
        "validation": "./main_splits/valid-animal.jsonl",
        "test": "./main_splits/test-animal.jsonl",
        "cs_abs":[
            "./cs_abs/test-animal_nv_0.jsonl",
            "./cs_abs/test-animal_nv_1.jsonl",
            "./cs_abs/test-animal_nv_2.jsonl",
            "./cs_abs/test-animal_nv_3.jsonl",
            "./cs_abs/test-animal_nv_4.jsonl",
            "./cs_abs/test-animal_nv_6.jsonl",
            "./cs_abs/test-animal_nv_7.jsonl",
            "./cs_abs/test-animal_nv_8.jsonl",
            "./cs_abs/test-animal_nv_9.jsonl",
        ],
        "cs_tdiv": [
            "./cs_tdiv/test-animal_tdiv_0.jsonl",
            "./cs_tdiv/test-animal_tdiv_1.jsonl",
            "./cs_tdiv/test-animal_tdiv_2.jsonl",
            "./cs_tdiv/test-animal_tdiv_3.jsonl",
        ]
    },
    "company": {
        "train": "./main_splits/train-company.jsonl",
        "validation": "./main_splits/valid-company.jsonl",
        "test": "./main_splits/test-company.jsonl",
        "cs_abs":[
            "./cs_abs/test-company_nv_0.jsonl",
            "./cs_abs/test-company_nv_1.jsonl",
            "./cs_abs/test-company_nv_2.jsonl",
            "./cs_abs/test-company_nv_3.jsonl",
            "./cs_abs/test-company_nv_4.jsonl",
            "./cs_abs/test-company_nv_6.jsonl",
            "./cs_abs/test-company_nv_7.jsonl",
            "./cs_abs/test-company_nv_8.jsonl",
            "./cs_abs/test-company_nv_9.jsonl",
        ],
        "cs_tdiv": [
            "./cs_tdiv/test-company_tdiv_0.jsonl",
            "./cs_tdiv/test-company_tdiv_1.jsonl",
            "./cs_tdiv/test-company_tdiv_2.jsonl",
            "./cs_tdiv/test-company_tdiv_3.jsonl",
        ]
    },
    "film": {
        "train": "./film/train-film.jsonl",
        "validation": "./film/valid-film.jsonl",
        "test": "./film/test-film.jsonl",
        "cs_abs":[
            "./cs_abs/test-film_nv_0.jsonl",
            "./cs_abs/test-film_nv_1.jsonl",
            "./cs_abs/test-film_nv_2.jsonl",
            "./cs_abs/test-film_nv_3.jsonl",
            "./cs_abs/test-film_nv_4.jsonl",
            "./cs_abs/test-film_nv_6.jsonl",
            "./cs_abs/test-film_nv_7.jsonl",
            "./cs_abs/test-film_nv_8.jsonl",
            "./cs_abs/test-film_nv_9.jsonl",
        ],
        "cs_tdiv": [
            "./cs_tdiv/test-film_tdiv_0.jsonl",
            "./cs_tdiv/test-film_tdiv_1.jsonl",
            "./cs_tdiv/test-film_tdiv_2.jsonl",
            "./cs_tdiv/test-film_tdiv_3.jsonl",
        ]
    }
}


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

    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="animal" , version=VERSION, description="Animal domain"),
        datasets.BuilderConfig(name="company", version=VERSION, description="Company domain"),
        datasets.BuilderConfig(name="film"   , version=VERSION, description="Film domain"),
    ]

    DEFAULT_CONFIG_NAME = "animal"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        features = datasets.Features(
            {
                "gem_id": datasets.Value("string"),
                "gem_parent_id": datasets.Value("string"),
                "id": datasets.Value("string"),
                "title": datasets.Value("string"),
                "paragraphs": datasets.features.Sequence(
                    datasets.Value("string")),
                "summary": datasets.features.Sequence(
                    {
                        "text": datasets.Value("string"),
                        "topic": datasets.Value("int16"),
                    })
                # 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
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # 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):
        """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
        my_urls = _URLs[self.config.name]
        d_conf = dl_manager.download_and_extract(my_urls)
        challenge_sets = [
            ("challenge_test_abstractivity_%d" % (lvl), fname) \
                for lvl,fname in enumerate(d_conf["cs_abs"])
        ] + [
            ("challenge_test_topic_diversity_%d" % (lvl),d_conf["cs_tdiv"]["test-%s_tdiv_%d.jsonl" % (self.config.name,lvl)]) \
                for lvl in range(4)
        ]
        

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": d_conf["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": d_conf["validation"],
                    "split": "test"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": d_conf["test"],
                    "split": "validation",
                },
            ),
        ] + [
                datasets.SplitGenerator(
                    name=challenge_split,
                    gen_kwargs={
                        "filepath": filename,
                        "split": challenge_split,
                    },
                )
                for challenge_split, filename in challenge_sets
            ]

    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.

        with open(filepath, encoding="utf-8") as f:
            for id_, row in enumerate(f):
                data = json.loads(row)
                # data["gem_parent_id"] = "GEM-wiki_cat_sum-%s-%d" % (split,data["id"]+1)
                # data["gem_id"] = "GEM-wiki_cat_sum-%s-%d" % (split,data["id"]+1)
                data["gem_parent_id"] = f"{self.config.name}-{split}-{id_+1}"
                data["gem_id"] = f"{self.config.name}-{split}-{id_+1}"
                yield id_,data