Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Sub-tasks:
open-domain-qa
Languages:
English
Size:
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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""" | |
from __future__ import absolute_import, division, print_function | |
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, | |
} | |