# 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 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": "main_splits/train-film.jsonl", "validation": "main_splits/valid-film.jsonl", "test": "main_splits/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", ], }, } def detokenize(text): """ Untokenizing a text undoes the tokenizing operation, restoring punctuation and spaces to the places that people expect them to be. Ideally, `untokenize(tokenize(text))` should be identical to `text`, except for line breaks. """ step1 = text.replace("`` ", '"').replace(" ''", '"').replace(". . .", "...") step2 = step1.replace(" ( ", " (").replace(" ) ", ") ") step3 = re.sub(r' ([.,:;?!%]+)([ \'"`])', r"\1\2", step2) step4 = re.sub(r" ([.,:;?!%]+)$", r"\1", step3) step5 = ( step4.replace(" '", "'") .replace(" n't", "n't") .replace("can not", "cannot") .replace(" 've", "'ve") ) step6 = step5.replace(" ` ", " '") return step6.strip() class WikiCatSum(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="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"), } ), "target": datasets.Value("string"), "references": [ datasets.Value("string"), ], } ) 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), fname) for lvl, fname in enumerate(d_conf["cs_abs"]) ] 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["paragraphs"] = [detokenize(p) for p in data["paragraphs"]] # If summary is a list itself, we have multi-ref. if isinstance(data["summary"], list): detok_targets = " ".join([ detokenize(s["text"]) for s in data["summary"] ]) data["target"] = detok_targets data["references"] = [detok_targets] # elif isinstance(data["summary"]["text"], list): # detok_target = detokenize(" ".join(data["summary"]["text"])) # print("\n\n\n\n", detok_target) # exit() # data["target"] = detok_target # data["references"] = [detok_target] # elif isinstance(data["summary"]["text"], str): # detok_target = detokenize(data["summary"]["text"]) else: print(data["summary"]) exit() data["gem_parent_id"] = f"{self.config.name}-{split}-{id_+1}" data["gem_id"] = f"{self.config.name}-{split}-{id_+1}" yield id_, data