# 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")