| import utils.utils | |
| import utils.dataset_utils | |
| import os | |
| from tqdm import tqdm | |
| import random | |
| import nltk | |
| import argparse | |
| def get_text(qad, domain): | |
| local_file = os.path.join(args.web_dir, qad['Filename']) if domain == 'SearchResults' else os.path.join(args.wikipedia_dir, qad['Filename']) | |
| return utils.utils.get_file_contents(local_file, encoding='utf-8') | |
| def select_relevant_portion(text): | |
| paras = text.split('\n') | |
| selected = [] | |
| done = False | |
| for para in paras: | |
| sents = sent_tokenize.tokenize(para) | |
| for sent in sents: | |
| words = nltk.word_tokenize(sent) | |
| for word in words: | |
| selected.append(word) | |
| if len(selected) >= args.max_num_tokens: | |
| done = True | |
| break | |
| if done: | |
| break | |
| if done: | |
| break | |
| selected.append('\n') | |
| st = ' '.join(selected).strip() | |
| return st | |
| def add_triple_data(datum, page, domain): | |
| qad = {'Source': domain} | |
| for key in ['QuestionId', 'Question', 'Answer']: | |
| qad[key] = datum[key] | |
| for key in page: | |
| qad[key] = page[key] | |
| return qad | |
| def get_qad_triples(data): | |
| qad_triples = [] | |
| for datum in data['Data']: | |
| for key in ['EntityPages', 'SearchResults']: | |
| for page in datum.get(key, []): | |
| qad = add_triple_data(datum, page, key) | |
| qad_triples.append(qad) | |
| return qad_triples | |
| def convert_to_squad_format(qa_json_file, squad_file): | |
| qa_json = utils.dataset_utils.read_triviaqa_data(qa_json_file) | |
| qad_triples = get_qad_triples(qa_json) | |
| random.seed(args.seed) | |
| random.shuffle(qad_triples) | |
| data = [] | |
| for qad in tqdm(qad_triples): | |
| qid = qad['QuestionId'] | |
| text = get_text(qad, qad['Source']) | |
| selected_text = select_relevant_portion(text) | |
| question = qad['Question'] | |
| para = {'context': selected_text, 'qas': [{'question': question, 'answers': []}]} | |
| data.append({'paragraphs': [para]}) | |
| qa = para['qas'][0] | |
| qa['id'] = utils.dataset_utils.get_question_doc_string(qid, qad['Filename']) | |
| qa['qid'] = qid | |
| ans_string, index = utils.dataset_utils.answer_index_in_document(qad['Answer'], selected_text) | |
| if index == -1: | |
| if qa_json['Split'] == 'train': | |
| continue | |
| else: | |
| qa['answers'].append({'text': ans_string, 'answer_start': index}) | |
| if qa_json['Split'] == 'train' and len(data) >= args.sample_size and qa_json['Domain'] == 'Web': | |
| break | |
| squad = {'data': data, 'version': qa_json['Version']} | |
| utils.utils.write_json_to_file(squad, squad_file) | |
| print ('Added', len(data)) | |
| def get_args(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--triviaqa_file', help='Triviaqa file') | |
| parser.add_argument('--squad_file', help='Squad file') | |
| parser.add_argument('--wikipedia_dir', help='Wikipedia doc dir') | |
| parser.add_argument('--web_dir', help='Web doc dir') | |
| parser.add_argument('--seed', default=10, type=int, help='Random seed') | |
| parser.add_argument('--max_num_tokens', default=800, type=int, help='Maximum number of tokens from a document') | |
| parser.add_argument('--sample_size', default=80000, type=int, help='Random seed') | |
| parser.add_argument('--tokenizer', default='tokenizers/punkt/english.pickle', help='Sentence tokenizer') | |
| args = parser.parse_args() | |
| return args | |
| if __name__ == '__main__': | |
| args = get_args() | |
| sent_tokenize = nltk.data.load(args.tokenizer) | |
| convert_to_squad_format(args.triviaqa_file, args.squad_file) | |