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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 json
import os
import faiss
import numpy as np
from tqdm import tqdm
from transformers import BertModel, BertTokenizer
def encode_passage(text, tokenizer, model, device='cuda:0'):
max_length = 154 # hardcode for now
inputs = tokenizer(
'[CLS] [D] ' + text,
max_length=max_length,
truncation=True,
add_special_tokens=False,
return_tensors='pt'
)
inputs.to(device)
outputs = model(**inputs)
embeddings = outputs.last_hidden_state.detach().cpu().numpy()
return np.mean(embeddings[:, 4:, :], axis=-2).flatten()
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('--device', type=str, help='device cpu or cuda [cuda:0, cuda:1...]', default='cuda:0')
args = parser.parse_args()
tokenizer = BertTokenizer.from_pretrained(args.encoder)
model = BertModel.from_pretrained(args.encoder)
model.to(args.device)
index = faiss.IndexFlatIP(args.dimension)
if not os.path.exists(args.index):
os.mkdir(args.index)
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'):
print(f'Encoding {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')
embedding = encode_passage(text, tokenizer, model, args.device)
index.add(np.array([embedding]))
faiss.write_index(index, os.path.join(args.index, 'index')) |