# 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 = { # 'animals': "https://datashare.ed.ac.uk/bitstream/handle/10283/3368/animal_tok_min5_L7.5k.zip", "animals": "https://huggingface.co/datasets/GEM/wiki_cat_sum/animal.zip" 'company': "https://huggingface.co/datasets/GEM/wiki_cat_sum/company.zip", 'film' : "https://huggingface.co/datasets/GEM/wiki_cat_sum/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("int"), }) # 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_nov_%d" % lvl,"test-%s_nv_%d.jsonl" % (split,self.config.name,lvl)) \ for lvl in range(11) ] + [ ("challenge_tdiv_%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": "dev", }, ), ] + [ 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. for x in ["train","valid","test"]: if x in split: p_split=x; break 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) yield id_,data