# 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. """PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition.""" import csv import json import os import datasets _CITATION = """\ @inproceedings{chen2023propsegment, title = "{PropSegmEnt}: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition", author = "Chen, Sihao and Buthpitiya, Senaka and Fabrikant, Alex and Roth, Dan and Schuster, Tal", booktitle = "Findings of the Association for Computational Linguistics: ACL 2023", year = "2023", } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ This is a reproduced (i.e. after web-crawling) and processed version of the "PropSegment" dataset from Google Research. Since the News portion of the dataset is released only via urls, we reconstruct the dataset by crawling. Overall, ~96% of the dataset can be reproduced, and the rest ~4% either have url no longer valid, or sentences that have been edited (i.e. cannot be aligned with the orignial dataset). PropSegment (Proposition-level Segmentation and Entailment) is a large-scale, human annotated dataset for segmenting English text into propositions, and recognizing proposition-level entailment relations --- whether a different, related document entails each proposition, contradicts it, or neither. The original dataset features >45k human annotated propositions, i.e. individual semantic units within sentences, as well as >45k entailment labels between propositions and documents. """ _HOMEPAGE = "https://github.com/google-research-datasets/PropSegmEnt" _LICENSE = "CC-BY-4.0" # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URL = "https://raw.githubusercontent.com/schen149/PropSegmEnt/main/" _URLS = { "segmentation": { "train": _URL + "proposition_segmentation.train.jsonl", "dev": _URL + "proposition_segmentation.dev.jsonl", "test": _URL + "proposition_segmentation.test.jsonl", }, "nli": { "train": _URL + "propnli.train.jsonl", "dev": _URL + "propnli.dev.jsonl", "test": _URL + "propnli.test.jsonl", } } _CONFIG_TO_FILENAME = { "segmentation": "proposition_segmentation", "nli": "propnli" } class PropSegment(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # 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="segmentation", version=VERSION, description="This part of my dataset covers a first domain"), datasets.BuilderConfig(name="nli", version=VERSION, description="This part of my dataset covers a second domain"), ] DEFAULT_CONFIG_NAME = "segmentation" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): if self.config.name == "segmentation": # This is the name of the configuration selected in BUILDER_CONFIGS above features = datasets.Features( { "sentence": datasets.Value("string"), "propositions": datasets.Value("string"), } ) else: features = datasets.Features( { "hypothesis": datasets.Value("string"), "premise": datasets.Value("string"), "label": 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, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # 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): config_name = self.config.name urls = _URLS[config_name] data_dir = dl_manager.download_and_extract(urls) file_prefix = _CONFIG_TO_FILENAME[config_name] return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join( data_dir, "{}.train.jsonl".format(file_prefix)), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join( data_dir, "{}.dev.jsonl".format(file_prefix)), "split": "dev", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join( data_dir, "{}.test.jsonl".format(file_prefix)), "split": "test" }, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. with open(filepath, encoding="utf-8") as f: for key, row in enumerate(f): data = json.loads(row) if self.config.name == "segmentation": yield key, { "sentence": data["sentence"], "propositions": data["propositions"], } else: yield key, { "hypothesis": data["hypothesis"], "premise": data["premise"], "label": data["label"], }