Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
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Sub-tasks:
open-domain-qa
Languages:
English
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ArXiv:
License:
| # 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. | |
| """SelQA: A New Benchmark for Selection-Based Question Answering""" | |
| import csv | |
| import json | |
| import datasets | |
| # TODO: Add BibTeX citation | |
| # Find for instance the citation on arxiv or on the dataset repo/website | |
| _CITATION = """\ | |
| @InProceedings{7814688, | |
| author={T. {Jurczyk} and M. {Zhai} and J. D. {Choi}}, | |
| booktitle={2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)}, | |
| title={SelQA: A New Benchmark for Selection-Based Question Answering}, | |
| year={2016}, | |
| volume={}, | |
| number={}, | |
| pages={820-827}, | |
| doi={10.1109/ICTAI.2016.0128} | |
| } | |
| """ | |
| # TODO: Add description of the dataset here | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| The SelQA dataset provides crowdsourced annotation for two selection-based question answer tasks, | |
| answer sentence selection and answer triggering. | |
| """ | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = "" | |
| # 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) | |
| types = { | |
| "answer_selection": "ass", | |
| "answer_triggering": "at", | |
| } | |
| modes = {"analysis": "json", "experiments": "tsv"} | |
| class SelqaConfig(datasets.BuilderConfig): | |
| """ "BuilderConfig for SelQA Dataset""" | |
| def __init__(self, mode, type_, **kwargs): | |
| super(SelqaConfig, self).__init__(**kwargs) | |
| self.mode = mode | |
| self.type_ = type_ | |
| # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case | |
| class Selqa(datasets.GeneratorBasedBuilder): | |
| """A New Benchmark for Selection-based Question Answering.""" | |
| VERSION = datasets.Version("1.1.0") | |
| # 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 = SelqaConfig | |
| # 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 = [ | |
| SelqaConfig( | |
| name="answer_selection_analysis", | |
| mode="analysis", | |
| type_="answer_selection", | |
| version=VERSION, | |
| description="This part covers answer selection analysis", | |
| ), | |
| SelqaConfig( | |
| name="answer_selection_experiments", | |
| mode="experiments", | |
| type_="answer_selection", | |
| version=VERSION, | |
| description="This part covers answer selection experiments", | |
| ), | |
| SelqaConfig( | |
| name="answer_triggering_analysis", | |
| mode="analysis", | |
| type_="answer_triggering", | |
| version=VERSION, | |
| description="This part covers answer triggering analysis", | |
| ), | |
| SelqaConfig( | |
| name="answer_triggering_experiments", | |
| mode="experiments", | |
| type_="answer_triggering", | |
| version=VERSION, | |
| description="This part covers answer triggering experiments", | |
| ), | |
| ] | |
| DEFAULT_CONFIG_NAME = "answer_selection_analysis" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| def _info(self): | |
| if ( | |
| self.config.mode == "experiments" | |
| ): # This is the name of the configuration selected in BUILDER_CONFIGS above | |
| features = datasets.Features( | |
| { | |
| "question": datasets.Value("string"), | |
| "candidate": datasets.Value("string"), | |
| "label": datasets.ClassLabel(names=["0", "1"]), | |
| } | |
| ) | |
| else: | |
| if self.config.type_ == "answer_selection": | |
| features = datasets.Features( | |
| { | |
| "section": datasets.Value("string"), | |
| "question": datasets.Value("string"), | |
| "article": datasets.Value("string"), | |
| "is_paraphrase": datasets.Value("bool"), | |
| "topic": datasets.ClassLabel( | |
| names=[ | |
| "MUSIC", | |
| "TV", | |
| "TRAVEL", | |
| "ART", | |
| "SPORT", | |
| "COUNTRY", | |
| "MOVIES", | |
| "HISTORICAL EVENTS", | |
| "SCIENCE", | |
| "FOOD", | |
| ] | |
| ), | |
| "answers": datasets.Sequence(datasets.Value("int32")), | |
| "candidates": datasets.Sequence(datasets.Value("string")), | |
| "q_types": datasets.Sequence( | |
| datasets.ClassLabel(names=["what", "why", "when", "who", "where", "how", ""]) | |
| ), | |
| } | |
| ) | |
| else: | |
| features = datasets.Features( | |
| { | |
| "section": datasets.Value("string"), | |
| "question": datasets.Value("string"), | |
| "article": datasets.Value("string"), | |
| "is_paraphrase": datasets.Value("bool"), | |
| "topic": datasets.ClassLabel( | |
| names=[ | |
| "MUSIC", | |
| "TV", | |
| "TRAVEL", | |
| "ART", | |
| "SPORT", | |
| "COUNTRY", | |
| "MOVIES", | |
| "HISTORICAL EVENTS", | |
| "SCIENCE", | |
| "FOOD", | |
| ] | |
| ), | |
| "q_types": datasets.Sequence( | |
| datasets.ClassLabel(names=["what", "why", "when", "who", "where", "how", ""]) | |
| ), | |
| "candidate_list": datasets.Sequence( | |
| { | |
| "article": datasets.Value("string"), | |
| "section": datasets.Value("string"), | |
| "candidates": datasets.Sequence(datasets.Value("string")), | |
| "answers": datasets.Sequence(datasets.Value("int32")), | |
| } | |
| ), | |
| } | |
| ) | |
| 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 | |
| urls = { | |
| "train": f"https://raw.githubusercontent.com/emorynlp/selqa/master/{types[self.config.type_]}/selqa-{types[self.config.type_]}-train.{modes[self.config.mode]}", | |
| "dev": f"https://raw.githubusercontent.com/emorynlp/selqa/master/{types[self.config.type_]}/selqa-{types[self.config.type_]}-dev.{modes[self.config.mode]}", | |
| "test": f"https://raw.githubusercontent.com/emorynlp/selqa/master/{types[self.config.type_]}/selqa-{types[self.config.type_]}-test.{modes[self.config.mode]}", | |
| } | |
| data_dir = dl_manager.download_and_extract(urls) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": data_dir["train"], | |
| "split": "train", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={"filepath": data_dir["test"], "split": "test"}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": data_dir["dev"], | |
| "split": "dev", | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath, split): | |
| """Yields examples.""" | |
| # TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method. | |
| # It is in charge of opening the given file and yielding (key, example) tuples from the dataset | |
| # The key is not important, it's more here for legacy reason (legacy from tfds) | |
| with open(filepath, encoding="utf-8") as f: | |
| if self.config.mode == "experiments": | |
| csv_reader = csv.DictReader( | |
| f, delimiter="\t", quoting=csv.QUOTE_NONE, fieldnames=["question", "candidate", "label"] | |
| ) | |
| for id_, row in enumerate(csv_reader): | |
| yield id_, row | |
| else: | |
| if self.config.type_ == "answer_selection": | |
| for row in f: | |
| data = json.loads(row) | |
| for id_, item in enumerate(data): | |
| yield id_, { | |
| "section": item["section"], | |
| "question": item["question"], | |
| "article": item["article"], | |
| "is_paraphrase": item["is_paraphrase"], | |
| "topic": item["topic"], | |
| "answers": item["answers"], | |
| "candidates": item["candidates"], | |
| "q_types": item["q_types"], | |
| } | |
| else: | |
| for row in f: | |
| data = json.loads(row) | |
| for id_, item in enumerate(data): | |
| candidate_list = [] | |
| for entity in item["candidate_list"]: | |
| candidate_list.append( | |
| { | |
| "article": entity["article"], | |
| "section": entity["section"], | |
| "answers": entity["answers"], | |
| "candidates": entity["candidates"], | |
| } | |
| ) | |
| yield id_, { | |
| "section": item["section"], | |
| "question": item["question"], | |
| "article": item["article"], | |
| "is_paraphrase": item["is_paraphrase"], | |
| "topic": item["topic"], | |
| "q_types": item["q_types"], | |
| "candidate_list": candidate_list, | |
| } | |