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# 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.
import json
import datasets
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{Elgohary:Peskov:Boyd-Graber-2019,
Title = {Can You Unpack That? Learning to Rewrite Questions-in-Context},
Author = {Ahmed Elgohary and Denis Peskov and Jordan Boyd-Graber},
Booktitle = {Empirical Methods in Natural Language Processing},
Year = {2019}
}
"""
# You can copy an official description
_DESCRIPTION = """\
CANARD has been preprocessed by Voskarides et al. to train and evaluate their Query Resolution Term Classification
model (QuReTeC).
CANARD is a dataset for question-in-context rewriting that consists of questions each given in a dialog context
together with a context-independent rewriting of the question. The context of each question is the dialog utterences
that precede the question. CANARD can be used to evaluate question rewriting models that handle important linguistic
phenomena such as coreference and ellipsis resolution.
"""
_HOMEPAGE = "https://sites.google.com/view/qanta/projects/canard"
_LICENSE = "CC BY-SA 4.0"
# 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)
_URL = "https://huggingface.co/datasets/uva-irlab/canard_quretec/resolve/main/"
_URLs = {
'gold_supervision': {
'train': _URL+"train_gold_supervision.json",
'dev': _URL+"dev_gold_supervision.json",
'test': _URL+"test_gold_supervision.json"
},
'original_all': {
'train': _URL+"train_original_all.json",
'dev': _URL+"dev_original_all.json",
'test': _URL+"test_original_all.json"
},
'distant_supervision': {
'train': _URL+"train_distant_supervision.json",
'dev': _URL+"dev_distant_supervision.json",
'test': _URL+"test_distant_supervision.json"
}
}
class CanardQuretec(datasets.GeneratorBasedBuilder):
"""
Voskarides et al. have preprocessed CANARD in different ways depending on their experiment.
"""
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="gold_supervision", version=VERSION, description="Was used for training quretec with gold supervision"),
datasets.BuilderConfig(name="original_all", version=VERSION, description="Was used for creating dataset statistics"),
datasets.BuilderConfig(name="distant_supervision", version=VERSION, description="Was used for training quretec with distant supervision"),
]
# It's not mandatory to have a default configuration. Just use one if it make sense.
DEFAULT_CONFIG_NAME = "gold_supervision"
def _info(self):
# This is the name of the configuration selected in BUILDER_CONFIGS above
# if self.config.name == "gold_supervision" or self.config.name == "original_all":
features = datasets.Features(
{
"id": datasets.Value("string"),
"prev_questions": datasets.Value("string"),
"cur_question": datasets.Value("string"),
"gold_terms": datasets.features.Sequence(feature=datasets.Value('string')),
"semantic_terms": datasets.features.Sequence(feature=datasets.Value('string')),
"overlapping_terms": datasets.features.Sequence(feature=datasets.Value('string')),
"answer_text_with_window": datasets.Value("string"),
"answer_text": datasets.Value("string"),
"bert_ner_overlap": datasets.features.Sequence(feature=datasets.features.Sequence(feature=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]
downloaded_files = dl_manager.download_and_extract(my_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={ # These kwargs will be passed to _generate_examples
"filepath": downloaded_files['train'],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={ # These kwargs will be passed to _generate_examples
"filepath": downloaded_files['test'],
"split": "test"
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={ # These kwargs will be passed to _generate_examples
"filepath": downloaded_files['dev'],
"split": "dev",
},
),
]
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) as f:
data_array = json.load(f)
for item_dict in data_array:
# if self.config.name == "first_domain":
yield item_dict.get('id'), item_dict |