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ambig_qa / ambig_qa.py
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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.
"""AmbigQA: Answering Ambiguous Open-domain Questions"""
import json
import os
import datasets
_CITATION = """\
@inproceedings{ min2020ambigqa,
title={ {A}mbig{QA}: Answering Ambiguous Open-domain Questions },
author={ Min, Sewon and Michael, Julian and Hajishirzi, Hannaneh and Zettlemoyer, Luke },
booktitle={ EMNLP },
year={2020}
}
"""
_DESCRIPTION = """\
AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous. The types of ambiguity are diverse and sometimes subtle, many of which are only apparent after examining evidence provided by a very large text corpus. AMBIGNQ, a dataset with
14,042 annotations on NQ-OPEN questions containing diverse types of ambiguity.
We provide two distributions of our new dataset AmbigNQ: a full version with all annotation metadata and a light version with only inputs and outputs.
"""
_HOMEPAGE = "https://nlp.cs.washington.edu/ambigqa/"
_LICENSE = "CC BY-SA 3.0"
_URL = "https://nlp.cs.washington.edu/ambigqa/data/"
_URLS = {
"light": _URL + "ambignq_light.zip",
"full": _URL + "ambignq.zip",
}
class AmbigQa(datasets.GeneratorBasedBuilder):
"""AmbigQA dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="light",
version=VERSION,
description="AmbigNQ light version with only inputs and outputs",
),
datasets.BuilderConfig(
name="full",
version=VERSION,
description="AmbigNQ full version with all annotation metadata",
),
]
DEFAULT_CONFIG_NAME = "full"
def _info(self):
features_dict = {
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"annotations": datasets.features.Sequence(
{
"type": datasets.Value("string"), # datasets.ClassLabel(names = ["singleAnswer","multipleQAs"])
"answer": datasets.features.Sequence(datasets.Value("string")),
"qaPairs": datasets.features.Sequence(
{
"question": datasets.Value("string"),
"answer": datasets.features.Sequence(datasets.Value("string")),
}
),
}
),
}
if self.config.name == "full":
detail_features = {
"viewed_doc_titles": datasets.features.Sequence(datasets.Value("string")),
"used_queries": datasets.features.Sequence(
{
"query": datasets.Value("string"),
"results": datasets.features.Sequence(
{
"title": datasets.Value("string"),
"snippet": datasets.Value("string"),
}
),
}
),
"nq_answer": datasets.features.Sequence(datasets.Value("string")),
"nq_doc_title": datasets.Value("string"),
}
features_dict.update(detail_features)
features = datasets.Features(features_dict)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# download and extract URLs
urls_to_download = _URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
train_file_name = "train.json" if self.config.name == "full" else "train_light.json"
dev_file_name = "dev.json" if self.config.name == "full" else "dev_light.json"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": os.path.join(downloaded_files[self.config.name], train_file_name)},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": os.path.join(downloaded_files[self.config.name], dev_file_name)},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for example in data:
id_ = example["id"]
annotations = example["annotations"]
# Add this because we cannot have None values (all keys in the schema should be present)
for an in annotations:
if "qaPairs" not in an:
an["qaPairs"] = []
if "answer" not in an:
an["answer"] = []
yield id_, example