# 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": "", "normalized_matched_wiki_entity_name": "", "normalized_value": "", "type": "", "value": "", } 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