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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. | |
# | |
from elasticsearch import Elasticsearch | |
from pyserini.search import get_topics, QueryEncoder | |
from tqdm import tqdm | |
client = Elasticsearch("http://localhost:9200", timeout=1000) | |
topics = get_topics("msmarco-passage-dev-subset") | |
query_encoder = QueryEncoder.load_encoded_queries('sbert-msmarco-passage-dev-subset') | |
with open('run.es_sbert.tsv', 'w') as f: | |
for qid in tqdm(topics): | |
query = topics[qid]['title'] | |
formated_query = { | |
"field": "text-vector", | |
"query_vector": query_encoder.encode(query), | |
"k": 10, | |
"num_candidates": 100 | |
} | |
resp = client.knn_search(index="tct-colbert-hnsw", knn=formated_query) | |
for i in range(len(resp["hits"]["hits"])): | |
pid = resp["hits"]["hits"][i]['_id'] | |
record = f"{qid}\t{pid}\t{i+1}\n" | |
f.write(record) | |