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| # | |
| # Pyserini: Reproducible IR research with sparse and dense representations | |
| # | |
| # 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. | |
| # | |
| # Starting point for writing this script | |
| # https://github.com/castorini/docTTTTTquery/blob/master/convert_msmarco_passages_doc_to_anserini.py | |
| import argparse | |
| import os | |
| import sys | |
| import gzip | |
| import json | |
| import spacy #Currently using spacy 2.3.5 | |
| from tqdm import tqdm | |
| import re | |
| import glob | |
| from multiprocessing import Pool | |
| def create_segments(doc_text, max_length, stride): | |
| doc_text = doc_text.strip() | |
| doc = nlp(doc_text[:10000]) | |
| sentences = [sent.string.strip() for sent in doc.sents] | |
| segments = [] | |
| for i in range(0, len(sentences), stride): | |
| segment = " ".join(sentences[i:i+max_length]) | |
| segments.append(segment) | |
| if i + max_length >= len(sentences): | |
| break | |
| return segments | |
| def split_document(f_ins, f_out): | |
| print('Spliting documents...') | |
| output = open(f_out, 'w') | |
| output_id = open(f_out.replace(".json", ".id"), 'w') | |
| for f_in in f_ins: | |
| with gzip.open(f_in, 'rt', encoding='utf8') as in_fh: | |
| for json_string in tqdm(in_fh): | |
| doc = json.loads(json_string) | |
| f_doc_id = doc['docid'] | |
| doc_url = doc['url'] | |
| doc_title = doc['title'] | |
| doc_headings = doc['headings'] | |
| doc_text = doc['body'] | |
| segments = create_segments(doc_text, args.max_length, args.stride) | |
| for seg_id, segment in enumerate(segments): | |
| # expanded_text = f'{doc_url}\n{doc_headings}\n{doc_title}\n{segment}' | |
| doc_seg = f'{f_doc_id}#{seg_id}' | |
| output_dict = {'docid': doc_seg, 'url': doc_url, 'title': doc_title, 'headings': doc_headings, 'segment': segment} | |
| output.write(json.dumps(output_dict) + '\n') | |
| output_id.write(doc_seg+'\n') | |
| output.close() | |
| output_id.close() | |
| print('Done!') | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser( | |
| description='Segment MS MARCO V2 original docs into passages') | |
| parser.add_argument('--input', required=True, help='MS MARCO V2 corpus path.') | |
| parser.add_argument('--output', required=True, help='output file path with json format.') | |
| parser.add_argument('--max_length', default=10, help='maximum sentence length per passage') | |
| parser.add_argument('--stride', default=5, help='the distance between each beginning sentence of passage in a document') | |
| parser.add_argument('--num_workers', default=1, type=int) | |
| args = parser.parse_args() | |
| os.makedirs(os.path.dirname(args.output_docs_path), exist_ok=True) | |
| max_length = args.max_length | |
| stride = args.stride | |
| nlp = spacy.blank("en") | |
| nlp.add_pipe(nlp.create_pipe("sentencizer")) | |
| files = glob.glob(os.path.join(args.original_docs_path, '*.gz')) | |
| num_files = len(files) | |
| pool = Pool(args.num_workers) | |
| num_files_per_worker=num_files//args.num_workers | |
| for i in range(args.num_workers): | |
| f_out = os.path.join(args.output_docs_path, 'doc' + str(i) + '.json') | |
| if i==(args.num_workers-1): | |
| file_list = files[i*num_files_per_worker:] | |
| else: | |
| file_list = files[i*num_files_per_worker:((i+1)*num_files_per_worker)] | |
| pool.apply_async(split_document ,(file_list, f_out)) | |
| pool.close() | |
| pool.join() | |
| print('Done!') | |