# 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. """WikiAuto dataset for Text Simplification""" import json import datasets _CITATION = """\ @inproceedings{acl/JiangMLZX20, author = {Chao Jiang and Mounica Maddela and Wuwei Lan and Yang Zhong and Wei Xu}, editor = {Dan Jurafsky and Joyce Chai and Natalie Schluter and Joel R. Tetreault}, title = {Neural {CRF} Model for Sentence Alignment in Text Simplification}, booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, {ACL} 2020, Online, July 5-10, 2020}, pages = {7943--7960}, publisher = {Association for Computational Linguistics}, year = {2020}, url = {https://www.aclweb.org/anthology/2020.acl-main.709/} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ WikiAuto provides a set of aligned sentences from English Wikipedia and Simple English Wikipedia as a resource to train sentence simplification systems. The authors first crowd-sourced a set of manual alignments between sentences in a subset of the Simple English Wikipedia and their corresponding versions in English Wikipedia (this corresponds to the `manual` config), then trained a neural CRF system to predict these alignments. The trained model was then applied to the other articles in Simple English Wikipedia with an English counterpart to create a larger corpus of aligned sentences (corresponding to the `auto`, `auto_acl`, `auto_full_no_split`, and `auto_full_with_split` configs here). """ # 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 = { "manual": { "train": "https://www.dropbox.com/sh/ohqaw41v48c7e5p/AACdl4UPKtu7CMMa-CJhz4G7a/wiki-manual/train.tsv?dl=1", "dev": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/dev.tsv", "test": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-manual/test.tsv", }, "auto_acl": { "normal": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/ACL2020/train.src", "simple": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/ACL2020/train.dst", }, "auto_full_no_split": { "normal": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_no_split/train.src", "simple": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_no_split/train.dst", }, "auto_full_with_split": { "normal": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_with_split/train.src", "simple": "https://github.com/chaojiang06/wiki-auto/raw/master/wiki-auto/GEM2021/full_with_split/train.dst", }, "auto": { "part_1": "https://www.dropbox.com/sh/ohqaw41v48c7e5p/AAATBDhU1zpdcT5x5WgO8DMaa/wiki-auto-all-data/wiki-auto-part-1-data.json?dl=1", "part_2": "https://www.dropbox.com/sh/ohqaw41v48c7e5p/AAATgPkjo_tPt9z12vZxJ3MRa/wiki-auto-all-data/wiki-auto-part-2-data.json?dl=1", }, } # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case class WikiAuto(datasets.GeneratorBasedBuilder): """WikiAuto dataset for sentence simplification""" VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ datasets.BuilderConfig( name="manual", version=VERSION, description="A set of 10K Wikipedia sentence pairs aligned by crowd workers.", ), datasets.BuilderConfig( name="auto_acl", version=VERSION, description="Automatically aligned and filtered sentence pairs used to train the ACL2020 system.", ), datasets.BuilderConfig( name="auto_full_no_split", version=VERSION, description="All automatically aligned sentence pairs without sentence splitting.", ), datasets.BuilderConfig( name="auto_full_with_split", version=VERSION, description="All automatically aligned sentence pairs with sentence splitting.", ), datasets.BuilderConfig( name="auto", version=VERSION, description="A large set of automatically aligned sentence pairs." ), ] DEFAULT_CONFIG_NAME = "auto" def _info(self): if self.config.name == "manual": # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( { "alignment_label": datasets.ClassLabel(names=["notAligned", "aligned", "partialAligned"]), "normal_sentence_id": datasets.Value("string"), "simple_sentence_id": datasets.Value("string"), "normal_sentence": datasets.Value("string"), "simple_sentence": datasets.Value("string"), "gleu_score": datasets.Value("float32"), } ) elif ( self.config.name == "auto_acl" or self.config.name == "auto_full_no_split" or self.config.name == "auto_full_with_split" ): features = datasets.Features( { "normal_sentence": datasets.Value("string"), "simple_sentence": datasets.Value("string"), } ) else: features = datasets.Features( { "example_id": datasets.Value("string"), "normal": { "normal_article_id": datasets.Value("int32"), "normal_article_title": datasets.Value("string"), "normal_article_url": datasets.Value("string"), "normal_article_content": datasets.Sequence( { "normal_sentence_id": datasets.Value("string"), "normal_sentence": datasets.Value("string"), } ), }, "simple": { "simple_article_id": datasets.Value("int32"), "simple_article_title": datasets.Value("string"), "simple_article_url": datasets.Value("string"), "simple_article_content": datasets.Sequence( { "simple_sentence_id": datasets.Value("string"), "simple_sentence": datasets.Value("string"), } ), }, "paragraph_alignment": datasets.Sequence( { "normal_paragraph_id": datasets.Value("string"), "simple_paragraph_id": datasets.Value("string"), } ), "sentence_alignment": datasets.Sequence( { "normal_sentence_id": datasets.Value("string"), "simple_sentence_id": datasets.Value("string"), } ), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage="https://github.com/chaojiang06/wiki-auto", license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): my_urls = _URLs[self.config.name] data_dir = dl_manager.download_and_extract(my_urls) if self.config.name in ["manual", "auto"]: return [ datasets.SplitGenerator( name=spl, gen_kwargs={ "filepaths": data_dir, "split": spl, }, ) for spl in data_dir ] else: return [ datasets.SplitGenerator( name="full", gen_kwargs={"filepaths": data_dir, "split": "full"}, ) ] def _generate_examples(self, filepaths, split): if self.config.name == "manual": keys = [ "alignment_label", "simple_sentence_id", "normal_sentence_id", "simple_sentence", "normal_sentence", "gleu_score", ] with open(filepaths[split], encoding="utf-8") as f: for id_, line in enumerate(f): values = line.strip().split("\t") assert len(values) == 6, f"Not enough fields in ---- {line} --- {values}" yield id_, dict( [(k, val) if k != "gleu_score" else (k, float(val)) for k, val in zip(keys, values)] ) elif ( self.config.name == "auto_acl" or self.config.name == "auto_full_no_split" or self.config.name == "auto_full_with_split" ): with open(filepaths["normal"], encoding="utf-8") as fi: with open(filepaths["simple"], encoding="utf-8") as fo: for id_, (norm_se, simp_se) in enumerate(zip(fi, fo)): yield id_, { "normal_sentence": norm_se, "simple_sentence": simp_se, } else: dataset_dict = json.load(open(filepaths[split], encoding="utf-8")) for id_, (eid, example_dict) in enumerate(dataset_dict.items()): res = { "example_id": eid, "normal": { "normal_article_id": example_dict["normal"]["id"], "normal_article_title": example_dict["normal"]["title"], "normal_article_url": example_dict["normal"]["url"], "normal_article_content": { "normal_sentence_id": [ sen_id for sen_id, sen_txt in example_dict["normal"]["content"].items() ], "normal_sentence": [ sen_txt for sen_id, sen_txt in example_dict["normal"]["content"].items() ], }, }, "simple": { "simple_article_id": example_dict["simple"]["id"], "simple_article_title": example_dict["simple"]["title"], "simple_article_url": example_dict["simple"]["url"], "simple_article_content": { "simple_sentence_id": [ sen_id for sen_id, sen_txt in example_dict["simple"]["content"].items() ], "simple_sentence": [ sen_txt for sen_id, sen_txt in example_dict["simple"]["content"].items() ], }, }, "paragraph_alignment": { "normal_paragraph_id": [ norm_id for simp_id, norm_id in example_dict.get("paragraph_alignment", []) ], "simple_paragraph_id": [ simp_id for simp_id, norm_id in example_dict.get("paragraph_alignment", []) ], }, "sentence_alignment": { "normal_sentence_id": [ norm_id for simp_id, norm_id in example_dict.get("sentence_alignment", []) ], "simple_sentence_id": [ simp_id for simp_id, norm_id in example_dict.get("sentence_alignment", []) ], }, } yield id_, res