Datasets:
| # 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": "https://huggingface.co./datasets/GEM/wiki_cat_sum/blob/main/animal.zip", | |
| 'company': "https://huggingface.co./datasets/GEM/wiki_cat_sum/blob/main/company.zip", | |
| 'film' : "https://huggingface.co./datasets/GEM/wiki_cat_sum/blob/main/film.zip", | |
| } | |
| # 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] | |
| data_dir = dl_manager.download_and_extract(my_urls) | |
| challenge_sets = [ | |
| ("challenge_test_abstractivity_%d" % lvl,"test-%s_nv_%d.jsonl" % (self.config.name,lvl)) \ | |
| for lvl in range(11) | |
| ] + [ | |
| ("challenge_test_topic_diversity_%d" % lvl,"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": os.path.join(data_dir, "train-%s.jsonl" % (self.config.name)), | |
| "split": "train", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, "test-%s.jsonl" % (self.config.name)), | |
| "split": "test" | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, "valid-%s.jsonl" % (self.config.name)), | |
| "split": "validation", | |
| }, | |
| ), | |
| ] + [ | |
| datasets.SplitGenerator( | |
| name=challenge_split, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, 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 | |