NetsPresso_QA / tools /scripts /msmarco /augment_collection_with_predictions.py
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#
# Pyserini: Python interface to the Anserini IR toolkit built on Lucene
#
# 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 json
import os
import argparse
def convert_collection(args):
print('Converting collection...')
predictions_file = open(args.predictions)
file_index = 0
with open(args.collection_path) as f:
for i, line in enumerate(f):
# Start writting to a new file whent the current one reached its 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, 'docs{:02d}.json'.format(file_index))
output_jsonl_file = open(output_path, 'w')
file_index += 1
doc_id, doc_text = line.rstrip().split('\t')
# Reads from predictions and merge then to the original doc text.
pred_text = []
for _ in range(args.stride):
pred_text.append(predictions_file.readline().strip())
pred_text = ' '.join(pred_text)
pred_text = pred_text.replace(' / ', ' ')
text = (doc_text + ' ') * args.original_copies + pred_text
output_dict = {'id': doc_id, 'contents': text}
output_jsonl_file.write(json.dumps(output_dict) + '\n')
if i % 100000 == 0:
print('Converted {} docs in {} files'.format(i, file_index))
output_jsonl_file.close()
predictions_file.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Augments MS MARCO TSV collection with predicted queries ' +
'to create an expanded Anserini jsonl collection.')
parser.add_argument('--collection-path', required=True, help='MS MARCO tsv collection.')
parser.add_argument('--predictions', required=True, help='Query predictions file.')
parser.add_argument('--output-folder', required=True, help='Qutput folder for jsonl collection.')
parser.add_argument('--stride', required=True, type=int,
help='Every [s] lines in predictions file is associated with each document.')
parser.add_argument('--max-docs-per-file', default=1000000, type=int,
help='Maximum number of documents in each jsonl file.')
parser.add_argument('--original-copies', default=1, type=int,
help='Number of copies of the original document to duplicate.')
args = parser.parse_args()
if not os.path.exists(args.output_folder):
os.makedirs(args.output_folder)
convert_collection(args)
print('Done!')