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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.
#
import argparse
import json
import os
from pyserini.analysis import Analyzer, get_lucene_analyzer
"""
append d2q prediction as an extra field to collection jsonl
"""
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Converts MSMARCO\'s tsv collection to Anserini jsonl files.')
parser.add_argument('--collection_path', required=True, help='MS MARCO .tsv collection file')
parser.add_argument('--predictions', required=True, help='File containing predicted queries.')
parser.add_argument('--output_folder', required=True, help='output folder')
parser.add_argument('--max_docs_per_file', default=1000000, type=int,
help='maximum number of documents in each jsonl file.')
args = parser.parse_args()
if not os.path.exists(args.output_folder):
os.makedirs(args.output_folder)
analyzer = Analyzer(get_lucene_analyzer())
print('Converting collection...')
file_index = 0
new_words = 0
total_words = 0
with open(args.collection_path) as f_corpus, open(args.predictions) as f_pred:
for i, (line_doc, line_pred) in enumerate(zip(f_corpus, f_pred)):
# Write to a new file when the current one reaches maximum capacity.
if i % args.max_docs_per_file == 0:
if i > 0:
output_jsonl_file.close()
output_path = os.path.join(args.output_folder, f'docs{file_index:02d}.json')
output_jsonl_file = open(output_path, 'w')
file_index += 1
doc_json = json.loads(line_doc)
pred_text = line_pred.rstrip()
predict_text = pred_text + ' '
analyzed = analyzer.analyze(predict_text)
for token in analyzed:
assert ' ' not in token
predict = ' '.join(analyzed)
doc_json['predict'] = predict
output_jsonl_file.write(json.dumps(doc_json) + '\n')
if i % 100000 == 0:
print('Converted {} docs in {} files'.format(i, file_index))
output_jsonl_file.close()
print('Done!') |