Datasets:
Languages:
English
Multilinguality:
monolingual
Language Creators:
machine-generated
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
Tags:
License:
# 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 | |
"""TriviaQA: A Reading Comprehension Dataset.""" | |
import glob | |
import json | |
import os | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
_CITATION = """ | |
@article{2017arXivtriviaqa, | |
author = {{Joshi}, Mandar and {Choi}, Eunsol and {Weld}, | |
Daniel and {Zettlemoyer}, Luke}, | |
title = "{triviaqa: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension}", | |
journal = {arXiv e-prints}, | |
year = 2017, | |
eid = {arXiv:1705.03551}, | |
pages = {arXiv:1705.03551}, | |
archivePrefix = {arXiv}, | |
eprint = {1705.03551}, | |
} | |
""" | |
_DOWNLOAD_URL_TMPL = "http://nlp.cs.washington.edu/triviaqa/data/triviaqa-{}.tar.gz" | |
_WEB_EVIDENCE_DIR = "evidence/web" | |
_WIKI_EVIDENCE_DIR = "evidence/wikipedia" | |
_DESCRIPTION = """\ | |
TriviaqQA is a reading comprehension dataset containing over 650K | |
question-answer-evidence triples. TriviaqQA includes 95K question-answer | |
pairs authored by trivia enthusiasts and independently gathered evidence | |
documents, six per question on average, that provide high quality distant | |
supervision for answering the questions. | |
""" | |
_RC_DESCRIPTION = """\ | |
Question-answer pairs where all documents for a given question contain the | |
answer string(s). | |
""" | |
_UNFILTERED_DESCRIPTION = """\ | |
110k question-answer pairs for open domain QA where not all documents for a | |
given question contain the answer string(s). This makes the unfiltered dataset | |
more appropriate for IR-style QA. | |
""" | |
_CONTEXT_ADDENDUM = "Includes context from Wikipedia and search results." | |
def _web_evidence_dir(tmp_dir): | |
return sorted(glob.glob(os.path.join(tmp_dir, _WEB_EVIDENCE_DIR))) | |
def _wiki_evidence_dir(tmp_dir): | |
return sorted(glob.glob(os.path.join(tmp_dir, _WIKI_EVIDENCE_DIR))) | |
def _qa_files(file_paths, sources, split, unfiltered): | |
qa_dir = ( | |
os.path.join(file_paths["unfiltered"], "triviaqa-unfiltered") | |
if unfiltered | |
else os.path.join(file_paths["rc"], "qa") | |
) | |
suffix_mapping = { | |
datasets.Split.TRAIN: "train.json", | |
datasets.Split.VALIDATION: "dev.json", | |
datasets.Split.TEST: "test-without-answers.json", | |
} | |
suffix = suffix_mapping[split] | |
filenames = [f"unfiltered-web-{suffix}"] if unfiltered else [f"{source}-{suffix}" for source in sources] | |
filenames = [os.path.join(qa_dir, filename) for filename in filenames] | |
return sorted(filenames) | |
class TriviaQaConfig(datasets.BuilderConfig): | |
"""BuilderConfig for TriviaQA.""" | |
def __init__(self, source="all", unfiltered=False, exclude_context=False, **kwargs): | |
"""BuilderConfig for TriviaQA. | |
Args: | |
unfiltered: bool, whether to use the unfiltered version of the dataset, | |
intended for open-domain QA. | |
exclude_context: bool, whether to exclude Wikipedia and search context for | |
reduced size. | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
name = "unfiltered" if unfiltered else "rc" | |
assert source in ["all", "web", "wikipedia"] | |
# there is no unfiltered version for the wikipedia subset | |
# --> unfiltered subset for source="all" is the same as for source="web" | |
# --> only accept source="all" if unfiltered is True | |
assert not unfiltered or source == "all" | |
if source != "all": | |
name += f".{source}" | |
if exclude_context: | |
name += ".nocontext" | |
description = _UNFILTERED_DESCRIPTION if unfiltered else _RC_DESCRIPTION | |
if not exclude_context: | |
description += _CONTEXT_ADDENDUM | |
super(TriviaQaConfig, self).__init__( | |
name=name, description=description, version=datasets.Version("1.2.0"), **kwargs | |
) | |
self.sources = ["web", "wikipedia"] if source == "all" else [source] | |
self.unfiltered = unfiltered | |
self.exclude_context = exclude_context | |
class TriviaQa(datasets.GeneratorBasedBuilder): | |
"""TriviaQA is a reading comprehension dataset. | |
It containss over 650K question-answer-evidence triples. | |
""" | |
BUILDER_CONFIGS = [ | |
TriviaQaConfig(source="all", unfiltered=False, exclude_context=False), # rc | |
TriviaQaConfig(source="all", unfiltered=False, exclude_context=True), # rc.nocontext | |
TriviaQaConfig(source="all", unfiltered=True, exclude_context=False), # unfiltered | |
TriviaQaConfig(source="all", unfiltered=True, exclude_context=True), # unfilered.nocontext | |
TriviaQaConfig(source="web", unfiltered=False, exclude_context=False), # rc | |
TriviaQaConfig(source="web", unfiltered=False, exclude_context=True), # rc.nocontext | |
TriviaQaConfig(source="wikipedia", unfiltered=False, exclude_context=False), # rc | |
TriviaQaConfig(source="wikipedia", unfiltered=False, exclude_context=True), # rc.nocontext | |
] | |
DEFAULT_WRITER_BATCH_SIZE = 1000 # examples are quite big, so set this value to save some RAM | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"question": datasets.Value("string"), | |
"question_id": datasets.Value("string"), | |
"question_source": datasets.Value("string"), | |
"entity_pages": datasets.features.Sequence( | |
{ | |
"doc_source": datasets.Value("string"), | |
"filename": datasets.Value("string"), | |
"title": datasets.Value("string"), | |
"wiki_context": datasets.Value("string"), | |
} | |
), | |
"search_results": datasets.features.Sequence( | |
{ | |
"description": datasets.Value("string"), | |
"filename": datasets.Value("string"), | |
"rank": datasets.Value("int32"), | |
"title": datasets.Value("string"), | |
"url": datasets.Value("string"), | |
"search_context": datasets.Value("string"), | |
} | |
), | |
"answer": dict( | |
{ | |
"aliases": datasets.features.Sequence(datasets.Value("string")), | |
"normalized_aliases": datasets.features.Sequence(datasets.Value("string")), | |
"matched_wiki_entity_name": datasets.Value("string"), | |
"normalized_matched_wiki_entity_name": datasets.Value("string"), | |
"normalized_value": datasets.Value("string"), | |
"type": datasets.Value("string"), | |
"value": datasets.Value("string"), | |
} | |
), | |
} | |
), | |
supervised_keys=None, | |
homepage="http://nlp.cs.washington.edu/triviaqa/", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
cfg = self.config | |
download_urls = dict() | |
if not (cfg.unfiltered and cfg.exclude_context): | |
download_urls["rc"] = _DOWNLOAD_URL_TMPL.format("rc") | |
if cfg.unfiltered: | |
download_urls["unfiltered"] = _DOWNLOAD_URL_TMPL.format("unfiltered") | |
file_paths = dl_manager.download_and_extract(download_urls) | |
if cfg.exclude_context: | |
web_evidence_dir = None | |
wiki_evidence_dir = None | |
else: | |
web_evidence_dir = os.path.join(file_paths["rc"], _WEB_EVIDENCE_DIR) | |
wiki_evidence_dir = os.path.join(file_paths["rc"], _WIKI_EVIDENCE_DIR) | |
return [ | |
datasets.SplitGenerator( | |
name=name, | |
gen_kwargs={ | |
"files": _qa_files(file_paths, cfg.sources, name, cfg.unfiltered), | |
"web_dir": web_evidence_dir, | |
"wiki_dir": wiki_evidence_dir, | |
}, | |
) | |
for name in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST] | |
] | |
def _generate_examples(self, files, web_dir, wiki_dir): | |
"""This function returns the examples.""" | |
def parse_example(article): | |
"""Return a single example from an article JSON record.""" | |
def _strip(collection): | |
return [item.strip() for item in collection] | |
if "Answer" in article: | |
answer = article["Answer"] | |
answer_dict = { | |
"aliases": _strip(answer["Aliases"]), | |
"normalized_aliases": _strip(answer["NormalizedAliases"]), | |
"matched_wiki_entity_name": answer.get("MatchedWikiEntryName", "").strip(), | |
"normalized_matched_wiki_entity_name": answer.get("NormalizedMatchedWikiEntryName", "").strip(), | |
"normalized_value": answer["NormalizedValue"].strip(), | |
"type": answer["Type"].strip(), | |
"value": answer["Value"].strip(), | |
} | |
else: | |
answer_dict = { | |
"aliases": [], | |
"normalized_aliases": [], | |
"matched_wiki_entity_name": "<unk>", | |
"normalized_matched_wiki_entity_name": "<unk>", | |
"normalized_value": "<unk>", | |
"type": "", | |
"value": "<unk>", | |
} | |
if self.config.exclude_context: | |
article["SearchResults"] = [] | |
article["EntityPages"] = [] | |
def _add_context(collection, context_field, file_dir): | |
"""Adds context from file, or skips if file does not exist.""" | |
new_items = [] | |
for item in collection: | |
if "Filename" not in item: | |
logger.info("Missing context 'Filename', skipping.") | |
continue | |
new_item = item.copy() | |
fname = item["Filename"] | |
try: | |
with open(os.path.join(file_dir, fname), encoding="utf-8") as f: | |
new_item[context_field] = f.read() | |
except (IOError, FileNotFoundError): | |
logger.info("File does not exist, skipping: %s", fname) | |
continue | |
new_items.append(new_item) | |
return new_items | |
def _strip_if_str(v): | |
return v.strip() if isinstance(v, str) else v | |
def _transpose_and_strip_dicts(dicts, field_names): | |
return { | |
datasets.naming.camelcase_to_snakecase(k): [_strip_if_str(d[k]) for d in dicts] | |
for k in field_names | |
} | |
search_results = _transpose_and_strip_dicts( | |
_add_context(article.get("SearchResults", []), "SearchContext", web_dir), | |
["Description", "Filename", "Rank", "Title", "Url", "SearchContext"], | |
) | |
entity_pages = _transpose_and_strip_dicts( | |
_add_context(article.get("EntityPages", []), "WikiContext", wiki_dir), | |
["DocSource", "Filename", "Title", "WikiContext"], | |
) | |
question = article["Question"].strip() | |
question_id = article["QuestionId"] | |
question_source = article["QuestionSource"].strip() | |
return { | |
"entity_pages": entity_pages, | |
"search_results": search_results, | |
"question": question, | |
"question_id": question_id, | |
"question_source": question_source, | |
"answer": answer_dict, | |
} | |
for filepath in files: | |
logger.info("generating examples from = %s", filepath) | |
fname = os.path.basename(filepath) | |
with open(filepath, encoding="utf-8") as f: | |
current_record = "" | |
for line in f: | |
if line == " {\n": | |
current_record = line | |
elif line.startswith(" }"): # Handles final record as well. | |
article = json.loads(current_record + "}") | |
current_record = "" | |
example = parse_example(article) | |
yield "%s_%s" % (fname, example["question_id"]), example | |
else: | |
current_record += line | |