Datasets:

uva-irlab
/

trec-cast-2019-multi-turn

	# 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
	from collections import defaultdict

	import datasets
	import csv
	from trec_car import read_data

	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@misc{dalton2020trec,
	title={TREC CAsT 2019: The Conversational Assistance Track Overview},
	author={Jeffrey Dalton and Chenyan Xiong and Jamie Callan},
	year={2020},
	eprint={2003.13624},
	archivePrefix={arXiv},
	primaryClass={cs.IR}
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	The Conversational Assistance Track (CAsT) is a new track for TREC 2019 to facilitate Conversational Information
	Seeking (CIS) research and to create a large-scale reusable test collection for conversational search systems.
	The document corpus is 38,426,252 passages from the TREC Complex Answer Retrieval (CAR) and Microsoft MAchine
	Reading COmprehension (MARCO) datasets.
	"""

	_HOMEPAGE = "http://www.treccast.ai"

	_LICENSE = ""

	# 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/trec-cast-2019-multi-turn/resolve/main/"
	_URLs = {
	'topics': _URL+"cast2019_test_annotated_without_context.tsv",
	'topics_with_context': _URL+"cast2019_test_annotated_with_context.tsv",
	'qrels': _URL+"2019qrels.txt",
	'test_collection': {
	'car': "http://trec-car.cs.unh.edu/datareleases/v2.0/paragraphCorpus.v2.0.tar.xz",
	'msmarco': 'https://msmarco.blob.core.windows.net/msmarcoranking/collection.tar.gz',
	},
	}

	SAMPLE_SIZE = 100000


	class TrecCast2019MultiTurn(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.1")

	# 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="qrels",
	version=VERSION,
	description=""),
	datasets.BuilderConfig(name="topics",
	version=VERSION,
	description="The topics contain the queries, query IDs and their history."),
	datasets.BuilderConfig(name="topics_with_context",
	version=VERSION,
	description="The topics contain the queries with relevant terms from the history, query IDs and their history."),
	datasets.BuilderConfig(name="test_collection",
	version=VERSION,
	description="The test collection will provide the passages of TREC CAR and MSMARCO"),
	datasets.BuilderConfig(name="test_collection_sample",
	version=VERSION,
	description="A small sample of 20000 of the test collection passages."),
	]

	# It's not mandatory to have a default configuration. Just use one if it make sense.
	DEFAULT_CONFIG_NAME = "test_collection"

	def _info(self):
	# This is the name of the configuration selected in BUILDER_CONFIGS above
	download_size = None
	if self.config.name == "topics":
	features = datasets.Features({
	"qid": datasets.Value("string"),
	"history": datasets.features.Sequence(feature=datasets.Value('string')),
	"query": datasets.Value("string"),
	})
	download_size = 6784
	elif self.config.name == "topics_with_context":
	features = datasets.Features({
	"qid": datasets.Value("string"),
	"history": datasets.features.Sequence(feature=datasets.Value('string')),
	"query": datasets.Value("string"),
	})
	download_size = 8010
	elif self.config.name == "qrels":
	features = datasets.Features({
	"qid": datasets.Value("string"),
	"qrels": datasets.features.Sequence(feature=datasets.Features({
	'docno': datasets.Value("string"),
	'relevance': datasets.Value("string"),
	})),
	})
	download_size = 1138032
	else: # for self.config.name == 'test_collection':
	features = datasets.Features({
	"docno": datasets.Value("string"),
	"text": datasets.Value("string"),
	})
	download_size = 5085726092 + 1035009698
	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,
	download_size=download_size
	)

	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
	urlkey = 'test_collection' if self.config.name == 'test_collection_sample' else self.config.name
	my_urls = _URLs[urlkey]
	downloaded_files = dl_manager.download_and_extract(my_urls)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={ # These kwargs will be passed to _generate_examples
	"file": downloaded_files,
	"split": self.config.name
	},
	),
	]

	def _generate_examples(
	self, file, 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.

	if split == 'qrels':
	qrels_file = csv.reader(open(file), delimiter=" ")
	qrels = defaultdict(list)
	for row in qrels_file:
	qid = row[0]
	docno = row[2]
	relevance = row[3]
	qrels[qid].append({'docno': docno, 'relevance': relevance})

	for qid in qrels.keys():
	yield qid, {'qid': qid, 'qrels': qrels[qid]}

	elif split == 'topics' or split == 'topics_with_context':
	topics_file = csv.reader(open(file), delimiter="\t")
	topics = defaultdict(list)
	for row in topics_file:
	qid, query = row
	conversation_id, question_number = qid.split('_')
	topics[conversation_id].append(query)

	for conversation_id in topics.keys():
	queries = topics[conversation_id] # type: list
	for idx in range(len(queries)):
	query = queries[idx]
	qid = f"{conversation_id}_{str(idx+1)}"
	yield qid, ({'query': query, 'history': queries[:idx], 'qid': qid})
	elif split == 'test_collection' or split == 'test_collection_sample':
	car_file = file['car'] + "/paragraphCorpus/dedup.articles-paragraphs.cbor"
	msmarco_file = file['msmarco']+"/collection.tsv"
	is_sample = split == 'test_collection_sample'
	i = 0
	with open(car_file, 'rb') as f:
	for para in read_data.iter_paragraphs(f):
	docid = f"CAR_{para.para_id}"
	yield docid, ({"docno": docid, "text": para.get_text()})
	i += 1
	if is_sample and i >= SAMPLE_SIZE:
	break

	i = 0
	with open(msmarco_file) as f:
	msmarco = csv.reader(f, delimiter="\t")
	for line in msmarco:
	docid, text = line
	docid = f"MARCO_{docid}"
	yield docid, ({"docno": docid, "text": text})
	i += 1
	if is_sample and i >= SAMPLE_SIZE:
	break
	else:
	raise NotImplementedError(f"'{split}' is not yet implemented")