| from argparse import ArgumentParser |
| from transformers import AutoTokenizer |
| import os |
| import random |
| from tqdm import tqdm |
| from datetime import datetime |
| from multiprocessing import Pool |
| from dense.processor import MarcoPassageTrainProcessor as TrainProcessor |
|
|
| random.seed(datetime.now()) |
| parser = ArgumentParser() |
| parser.add_argument('--tokenizer_name', required=True) |
| parser.add_argument('--negative_file', required=True) |
| parser.add_argument('--qrels', required=True) |
| parser.add_argument('--queries', required=True) |
| parser.add_argument('--collection', required=True) |
| parser.add_argument('--save_to', required=True) |
|
|
| parser.add_argument('--truncate', type=int, default=128) |
| parser.add_argument('--n_sample', type=int, default=30) |
| parser.add_argument('--mp_chunk_size', type=int, default=500) |
| parser.add_argument('--shard_size', type=int, default=45000) |
|
|
| args = parser.parse_args() |
|
|
|
|
| qrel = TrainProcessor.read_qrel(args.qrels) |
|
|
| def read_line(l): |
| q, nn = l.strip().split('\t') |
| nn = nn.split(',') |
| random.shuffle(nn) |
| return q, qrel[q], nn[:args.n_sample] |
|
|
|
|
| tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True) |
| processor = TrainProcessor( |
| query_file=args.queries, |
| collection_file=args.collection, |
| tokenizer=tokenizer, |
| max_length=args.truncate, |
| ) |
|
|
| counter = 0 |
| shard_id = 0 |
| f = None |
| os.makedirs(args.save_to, exist_ok=True) |
|
|
| with open(args.negative_file) as nf: |
| pbar = tqdm(map(read_line, nf)) |
|
|
| with Pool() as p: |
| for x in p.imap(processor.process_one, pbar, chunksize=args.mp_chunk_size): |
| counter += 1 |
| if f is None: |
| f = open(os.path.join(args.save_to, f'split{shard_id:02d}.json'), 'w') |
| pbar.set_description(f'split - {shard_id:02d}') |
| f.write(x + '\n') |
|
|
| if counter == args.shard_size: |
| f.close() |
| f = None |
| shard_id += 1 |
| counter = 0 |
|
|
| if f is not None: |
| f.close() |
