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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 multiprocessing
import json
import time
from joblib import Parallel, delayed
from transformers import AutoTokenizer
from ltr_msmarco.convert_common import get_retokenized
'''Replace original contents fields with bert tokenization'''
parser = argparse.ArgumentParser(description='Convert MSMARCO-adhoc documents.')
parser.add_argument('--input', metavar='input file', help='input file',
type=str, required=True)
parser.add_argument('--output', metavar='output file', help='output file',
type=str, required=True)
parser.add_argument('--workers', metavar='# of processes', help='# of workers to spawn',
type=int, default=multiprocessing.cpu_count() - 2)
args = parser.parse_args()
print(args)
arg_vars = vars(args)
def batch_file(iterable, n=10000):
batch = []
for line in iterable:
batch.append(line)
if len(batch) == n:
yield batch
batch = []
if len(batch) > 0:
yield batch
batch = []
return
def batch_process(batch):
bert_tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
def process(line):
if not line:
return None
json_line = json.loads(line)
pid = json_line['id']
body = json_line['contents']
doc = {"id": pid,
"contents": get_retokenized(bert_tokenizer, body.lower())}
return doc
res = []
start = time.time()
for line in batch:
res.append(process(line))
if len(res) % 1000 == 0:
end = time.time()
print(f"finish {len(res)} using {end-start}")
start = end
return res
if __name__ == '__main__':
workers = args.workers
print(f"Spawning {workers} processes")
pool = Parallel(n_jobs=workers, verbose=10)
line_num = 0
with open(args.input) as inFile:
with open(args.output, 'w') as outFile:
for batch_json in pool([delayed(batch_process)(batch) for batch in batch_file(inFile)]):
for doc_json in batch_json:
line_num = line_num + 1
if doc_json is not None:
outFile.write(json.dumps(doc_json) + '\n')
else:
print(f"Ignoring misformatted line {line_num}")
if line_num % 100 == 0:
print(f"Processed {line_num} passages")
print(f"Processed {line_num} passages")
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