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Add msmarco doc corpus v2 document segmentation script (#706)
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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 | ||
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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 = [] | ||
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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 | ||
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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'] | ||
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segments = create_segments(doc_text, args.max_length, args.stride) | ||
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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') | ||
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output.close() | ||
output_id.close() | ||
print('Done!') | ||
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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() | ||
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os.makedirs(os.path.dirname(args.output_docs_path), exist_ok=True) | ||
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max_length = args.max_length | ||
stride = args.stride | ||
nlp = spacy.blank("en") | ||
nlp.add_pipe(nlp.create_pipe("sentencizer")) | ||
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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)] | ||
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pool.apply_async(split_document ,(file_list, f_out)) | ||
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pool.close() | ||
pool.join() | ||
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print('Done!') | ||
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