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feedbackQA

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feedbackQA / feedbackQA.py

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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.
	"""

	#_URLS = {
	# "train": "https://cdn-lfs.huggingface.co/datasets/McGill-NLP/FeedbackQA/46bd763229fc603d73f634a312367acb83c3b713a5dfd9fcf8a9b3e310c39a67",
	# "dev": "https://cdn-lfs.huggingface.co/datasets/McGill-NLP/FeedbackQA/40a93282e5fdee4706c20e32ddd4734151139d67f6844dbcffb9e7be22ae6b8f",
	# "test": "https://cdn-lfs.huggingface.co/datasets/McGill-NLP/FeedbackQA/50c4a21dc778cf064f731161e2213f21d2951cabd9331a1c524f791055040d02"
	#}

	_URL = 'https://drive.google.com/uc?export=download&id=14KV6yKgdjzb6fbFzshGuNvEp9zGv_gol'

	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')
	val_file = os.path.join(downloaded_files_path, 'feedback_valid.json')
	test_file = os.path.join(downloaded_files_path, 'feedback_test.json')
	print(test_file)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_file}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_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