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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 | |
| '''Replace original contents fields with bert tokenization''' | |
| parser = argparse.ArgumentParser(description='Convert BEIR original documents to word piece tokenized.') | |
| 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) | |
| parser.add_argument('--tokenizer', metavar='tokenizer', help='tokenizer', | |
| type=str, default='bert-base-cased') | |
| args = parser.parse_args() | |
| print(args) | |
| arg_vars = vars(args) | |
| def get_retokenized(tokenizer, text): | |
| """ | |
| copy from pyserini.scripts.ltr_msmarco.convert_common.get_retokenized | |
| Obtain a space separated re-tokenized text. | |
| :param tokenizer: a tokenizer that has the function | |
| tokenize that returns an array of tokens. | |
| :param text: a text to re-tokenize. | |
| """ | |
| return ' '.join(tokenizer.tokenize(text)) | |
| 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(args.tokenizer) | |
| def process(line): | |
| if not line: | |
| return None | |
| json_line = json.loads(line) | |
| pid = json_line['_id'] | |
| title = json_line['title'] | |
| body = json_line['text'] | |
| doc = {"_id": pid, | |
| "title": get_retokenized(bert_tokenizer, title.lower()), | |
| "text": get_retokenized(bert_tokenizer, body.lower())} | |
| return doc | |
| res = [] | |
| start = time.time() | |
| for line in batch: | |
| res.append(process(line)) | |
| if len(res) % 100000 == 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 % 10000 == 0: | |
| print(f"Processed {line_num} passages") | |
| print(f"Processed {line_num} passages") | |