Spaces:
Runtime error
Runtime error
File size: 3,537 Bytes
d6585f5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 |
#
# 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
import sys
import numpy as np
import faiss
from tqdm import tqdm
from transformers import RobertaTokenizer
# We're going to explicitly use a local installation of Pyserini (as opposed to a pip-installed one).
# Comment these lines out to use a pip-installed one instead.
sys.path.insert(0, './')
sys.path.insert(0, '../pyserini/')
from pyserini.dsearch import AnceEncoder
def encode_passage(texts, tokenizer, model, device='cuda:0'):
max_length = 512 # hardcode for now
inputs = tokenizer(
texts,
max_length=max_length,
padding='longest',
truncation=True,
add_special_tokens=True,
return_tensors='pt'
)
inputs.to(device)
embeddings = model(inputs["input_ids"]).detach().cpu().numpy()
return embeddings
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--encoder', type=str, help='encoder name or path', required=True)
parser.add_argument('--dimension', type=int, help='dimension of passage embeddings', required=False, default=768)
parser.add_argument('--corpus', type=str,
help='directory that contains corpus files to be encoded, in jsonl format.', required=True)
parser.add_argument('--index', type=str, help='directory to store brute force index of corpus', required=True)
parser.add_argument('--batch', type=int, help='batch size', default=8)
parser.add_argument('--device', type=str, help='device cpu or cuda [cuda:0, cuda:1...]', default='cuda:0')
args = parser.parse_args()
tokenizer = RobertaTokenizer.from_pretrained(args.encoder)
model = AnceEncoder.from_pretrained(args.encoder)
model.to(args.device)
index = faiss.IndexFlatIP(args.dimension)
if not os.path.exists(args.index):
os.mkdir(args.index)
texts = []
with open(os.path.join(args.index, 'docid'), 'w') as id_file:
for file in sorted(os.listdir(args.corpus)):
file = os.path.join(args.corpus, file)
if file.endswith('json') or file.endswith('jsonl'):
print(f'Loading {file}')
with open(file, 'r') as corpus:
for idx, line in enumerate(tqdm(corpus.readlines())):
info = json.loads(line)
docid = info['id']
text = info['contents']
id_file.write(f'{docid}\n')
url, title, text = text.split('\n')
text = f"{url} <sep> {title} <sep> {text}"
texts.append(text.lower())
for idx in tqdm(range(0, len(texts), args.batch)):
text_batch = texts[idx: idx+args.batch]
embeddings = encode_passage(text_batch, tokenizer, model, args.device)
index.add(np.array(embeddings))
faiss.write_index(index, os.path.join(args.index, 'index'))
|