Datasets:

xingkunliuxtracta
/

nlu_evaluation_data

	# 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.
	"""NLU Evaluation Data."""

	from __future__ import absolute_import, division, print_function

	import csv
	import re

	import datasets


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@InProceedings{XLiu.etal:IWSDS2019,
	author = {Xingkun Liu, Arash Eshghi, Pawel Swietojanski and Verena Rieser},
	title = {Benchmarking Natural Language Understanding Services for building Conversational Agents},
	booktitle = {Proceedings of the Tenth International Workshop on Spoken Dialogue Systems Technology (IWSDS)},
	month = {April},
	year = {2019},
	address = {Ortigia, Siracusa (SR), Italy},
	publisher = {Springer},
	pages = {xxx--xxx},
	url = {http://www.xx.xx/xx/}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	Raw part of NLU Evaluation Data. It contains 25 715 non-empty examples (original dataset has 25716 examples) from 68 unique intents belonging to 18 scenarios.
	"""

	_HOMEPAGE = "https://github.com/xliuhw/NLU-Evaluation-Data"

	_LICENSE = "Creative Commons Attribution 4.0 International License (CC BY 4.0)"

	_URL = "https://raw.githubusercontent.com/xliuhw/NLU-Evaluation-Data/master/AnnotatedData/NLU-Data-Home-Domain-Annotated-All.csv"

	ANNOTATION_PATTERN = re.compile(r"\[(.+?)\s+\:+\s(.+?)\]")


	def remove_annotations(text):
	"""Remove named entity annotations from text example.

	Examples are defined based on `answer_annotation` column since it has the least number
	of Nans. However, this column contains patterns of annotation of the form:

	[named_entity : part_of_text]

	e.g. [time : five am], [date : this week]

	We identity them with regex rule and replace all occurrences with just part_of_text.
	"""
	return ANNOTATION_PATTERN.sub(r"\2", text)


	def define_intent_name(scenario, intent):
	"""Intent name is defined as concatenation of `scenario` and `intent`
	values.

	See Also:
	https://github.com/xliuhw/NLU-Evaluation-Data/issues/5
	"""
	return f"{scenario}_{intent}"


	class NLUEvaluationData(datasets.GeneratorBasedBuilder):
	"""Raw part of NLU Evaluation Data."""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	features = datasets.Features(
	{
	"text": datasets.Value("string"),
	"scenario": datasets.Value("string"),
	"label": datasets.features.ClassLabel(
	names=[
	"alarm_query",
	"alarm_remove",
	"alarm_set",
	"audio_volume_down",
	"audio_volume_mute",
	"audio_volume_other",
	"audio_volume_up",
	"calendar_query",
	"calendar_remove",
	"calendar_set",
	"cooking_query",
	"cooking_recipe",
	"datetime_convert",
	"datetime_query",
	"email_addcontact",
	"email_query",
	"email_querycontact",
	"email_sendemail",
	"general_affirm",
	"general_commandstop",
	"general_confirm",
	"general_dontcare",
	"general_explain",
	"general_greet",
	"general_joke",
	"general_negate",
	"general_praise",
	"general_quirky",
	"general_repeat",
	"iot_cleaning",
	"iot_coffee",
	"iot_hue_lightchange",
	"iot_hue_lightdim",
	"iot_hue_lightoff",
	"iot_hue_lighton",
	"iot_hue_lightup",
	"iot_wemo_off",
	"iot_wemo_on",
	"lists_createoradd",
	"lists_query",
	"lists_remove",
	"music_dislikeness",
	"music_likeness",
	"music_query",
	"music_settings",
	"news_query",
	"play_audiobook",
	"play_game",
	"play_music",
	"play_podcasts",
	"play_radio",
	"qa_currency",
	"qa_definition",
	"qa_factoid",
	"qa_maths",
	"qa_stock",
	"recommendation_events",
	"recommendation_locations",
	"recommendation_movies",
	"social_post",
	"social_query",
	"takeaway_order",
	"takeaway_query",
	"transport_query",
	"transport_taxi",
	"transport_ticket",
	"transport_traffic",
	"weather_query",
	]
	),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	train_path = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
	]

	def _generate_examples(self, filepath):
	"""Yields examples as (key, example) tuples."""
	with open(filepath, encoding="utf-8") as f:
	csv_reader = csv.reader(f, quotechar='"', delimiter=";", quoting=csv.QUOTE_ALL, skipinitialspace=True)
	# call next to skip header
	next(csv_reader)
	for id_, row in enumerate(csv_reader):
	(
	userid,
	answerid,
	scenario,
	intent,
	status,
	answer_annotation,
	notes,
	suggested_entities,
	answer_normalised,
	answer,
	question,
	) = row

	# examples with empty answer are removed as part of the dataset
	if answer_annotation == "null":
	continue

	yield id_, {
	"text": remove_annotations(answer_annotation),
	"scenario": scenario,
	"label": define_intent_name(scenario, intent),
	}