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.""" | |
| from __future__ import absolute_import, division, print_function | |
| import glob | |
| import json | |
| import logging | |
| import os | |
| import six | |
| import datasets | |
| _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" | |
| _TRAIN_FILE_FORMAT = "*-train.json" | |
| _VALIDATION_FILE_FORMAT = "*-dev.json" | |
| _TEST_FILE_FORMAT = "*test-without-answers.json" | |
| _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))) | |
| class TriviaQaConfig(datasets.BuilderConfig): | |
| """BuilderConfig for TriviaQA.""" | |
| def __init__(self, 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" | |
| 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("1.1.0"), **kwargs | |
| ) | |
| 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(unfiltered=False, exclude_context=False), # rc | |
| TriviaQaConfig(unfiltered=False, exclude_context=True), # rc.nocontext | |
| TriviaQaConfig(unfiltered=True, exclude_context=False), # unfiltered | |
| TriviaQaConfig(unfiltered=True, exclude_context=True), | |
| # unfilered.nocontext | |
| ] | |
| 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) | |
| qa_dir = ( | |
| os.path.join(file_paths["unfiltered"], "triviaqa-unfiltered") | |
| if cfg.unfiltered | |
| else os.path.join(file_paths["rc"], "qa") | |
| ) | |
| train_files = sorted(glob.glob(os.path.join(qa_dir, _TRAIN_FILE_FORMAT))) | |
| valid_files = sorted(glob.glob(os.path.join(qa_dir, _VALIDATION_FILE_FORMAT))) | |
| test_files = sorted(glob.glob(os.path.join(qa_dir, _TEST_FILE_FORMAT))) | |
| 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=datasets.Split.TRAIN, | |
| gen_kwargs={"files": train_files, "web_dir": web_evidence_dir, "wiki_dir": wiki_evidence_dir}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={"files": valid_files, "web_dir": web_evidence_dir, "wiki_dir": wiki_evidence_dir}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={"files": test_files, "web_dir": web_evidence_dir, "wiki_dir": wiki_evidence_dir}, | |
| ), | |
| ] | |
| 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: | |
| logging.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, datasets.Value("errors").NotFoundError): | |
| logging.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, six.string_types) 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: | |
| logging.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 | |