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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""FeedbackQA: An Retrieval-based Question Answering Dataset with User Feedback"""
import json
import datasets
import os
logger = datasets.logging.get_logger(__name__)
_CITATION = """
"""
_DESCRIPTION = """\
FeedbackQA is a retrieval-based QA dataset \
that contains interactive feedback from users. \
It has two parts: the first part contains a conventional RQA dataset, \
whilst this repo contains the second part, which contains feedback(ratings and natural language explanations) for QA pairs.
"""
_URL = "https://drive.google.com/drive/folders/1mIcxZZ643k6SVJnZw1FmEOhndaFx4_PG?usp=sharing"
#_URLS = {
# "train": _URL + "train-v1.1.json",
# "dev": _URL + "dev-v1.1.json",
#}
class FeedbackConfig(datasets.BuilderConfig):
"""BuilderConfig for FeedbackQA."""
def __init__(self, **kwargs):
"""BuilderConfig for FeedbackQA.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(FeedbackConfig, self).__init__(**kwargs)
class FeedbackQA(datasets.GeneratorBasedBuilder):
"""FeedbackQA: retrieval-based QA dataset that contains interactive feedback from users."""
BUILDER_CONFIGS = [
FeedbackConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
description="Plain text",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
#"id": datasets.Value("string"),
#"title": datasets.Value("string"),
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
"feedback": datasets.features.Sequence(
{
"rating": datasets.Value("string"),
"explanation": datasets.Value("string"),
}
),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
homepage="https://mcgill-nlp.github.io/feedbackQA_data/",
citation=_CITATION
)
def _split_generators(self, dl_manager):
downloaded_files_path = dl_manager.download_and_extract(_URL)
train_file = os.path.join(downloaded_files_path, 'feedback_train.json')
valid_file = os.path.join(downloaded_files_path, 'feedback_valid.json')
test_file = os.path.join(downloaded_files_path, 'feedback_test.json')
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_file}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_file}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_file}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
key = 0
with open(filepath, encoding="utf-8") as f:
fbqa = json.load(f)
for dict_item in fbqa:
question = dict_item['question']
passage_text = ''
if dict_item['passage']['reference']['page_title']:
passage_text += dict_item['passage']['reference']['page_title'] + '\n'
if dict_item['passage']['reference']['section_headers']:
passage_text += '\n'.join(dict_item['passage']['reference']['section_headers']) + '\n'
if dict_item['passage']['reference']['section_content']:
passage_text += dict_item['passage']['reference']['section_content']
yield key, {
"question": question,
"answer": passage_text,
"feedback": {
"rating": dict_item['rating'],
"explanation": dict_item['feedback'],
},
}
key += 1 |