Aggretriever是一个编码器，将词汇和语义文本信息聚合到一个单一向量中，用于密集检索，该向量是在使用BM25负采样对MS MARCO语料库进行微调的基础上得到的，并遵循 Aggretriever: A Simple Approach to Aggregate Textual Representation for Robust Dense Passage Retrieval 中所述的方法。

微调的相关GitHub存储库可在此处找到（ here ），并且可以从pyserini中重现。还提供了以下变体：

使用方法（HuggingFace Transformers）

直接在HuggingFace transformers中使用可用的模型。我们使用的是pyserini中实现的Aggretriever（ here ）。

from pyserini.encode._aggretriever import AggretrieverQueryEncoder
from pyserini.encode._aggretriever import AggretrieverDocumentEncoder

model_name = '/store/scratch/s269lin/experiments/aggretriever/hf_model/aggretriever-distilbert'
query_encoder = AggretrieverQueryEncoder(model_name, device='cpu')
context_encoder = AggretrieverDocumentEncoder(model_name, device='cpu')

query =  "Where was Marie Curie born?"
contexts = [
    "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.",
    "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace."
]
# Compute embeddings
query_emb = query_encoder.encode(query)
ctx_emb = context_encoder.encode(contexts)
# Compute similarity scores using dot product
score1 = query_emb @ ctx_emb[0]  # 47.667152
score2 = query_emb @ ctx_emb[1]  # 39.054127