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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
from tqdm import tqdm
import numpy as np
import pandas as pd
from pyserini.query_iterator import DefaultQueryIterator
from pyserini.encode import DprQueryEncoder, TctColBertQueryEncoder, AnceQueryEncoder, AutoQueryEncoder
from pyserini.encode import UniCoilQueryEncoder, SpladeQueryEncoder
def init_encoder(encoder, device):
if 'dpr' in encoder.lower():
return DprQueryEncoder(encoder, device=device)
elif 'tct' in encoder.lower():
return TctColBertQueryEncoder(encoder, device=device)
elif 'ance' in encoder.lower():
return AnceQueryEncoder(encoder, device=device, tokenizer_name='roberta-base')
elif 'sentence-transformers' in encoder.lower():
return AutoQueryEncoder(encoder, device=device, pooling='mean', l2_norm=True)
elif 'unicoil' in encoder.lower():
return UniCoilQueryEncoder(encoder, device=device)
elif 'splade' in encoder.lower():
return SpladeQueryEncoder(encoder, device=device)
else:
return AutoQueryEncoder(encoder, device=device)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--topics', type=str,
help='path to topics file in tsv format or self-contained topics name', required=True)
parser.add_argument('--encoder', type=str, help='encoder model name or path', required=True)
parser.add_argument('--weight-range', type=int, help='range of weights for sparse embedding', required=False)
parser.add_argument('--quant-range', type=int, help='range of quantization for sparse embedding', required=False)
parser.add_argument('--output', type=str, help='path to stored encoded queries', required=True)
parser.add_argument('--device', type=str, help='device cpu or cuda [cuda:0, cuda:1...]',
default='cpu', required=False)
args = parser.parse_args()
encoder = init_encoder(args.encoder, device=args.device)
query_iterator = DefaultQueryIterator.from_topics(args.topics)
is_sparse = False
query_ids = []
query_texts = []
query_embeddings = []
for topic_id, text in tqdm(query_iterator):
embedding = encoder.encode(text)
if isinstance(embedding, dict):
is_sparse = True
pseudo_str = []
for tok, weight in embedding.items():
weight_quanted = int(np.round(weight/args.weight_range*args.quant_range))
pseudo_str += [tok] * weight_quanted
pseudo_str = " ".join(pseudo_str)
embedding = pseudo_str
query_ids.append(topic_id)
query_texts.append(text)
query_embeddings.append(embedding)
if is_sparse:
with open(args.output, 'w') as f:
for i in range(len(query_ids)):
f.write(f"{query_ids[i]}\t{query_embeddings[i]}\n")
else:
embeddings = {'id': query_ids, 'text': query_texts, 'embedding': query_embeddings}
embeddings = pd.DataFrame(embeddings)
embeddings.to_pickle(args.output)
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