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的密集向量中，用于密集检索，它是在MS MARCO语料库上使用BM25负采样进行微调的，遵循了 Aggretriever: A Simple Approach to Aggregate Textual Representation for Robust Dense Passage Retrieval 中所描述的方法。

有关微调的关联GitHub存储库可在此处找到 here ，使用pyserini进行复现的代码在[这里]。还提供了以下变体：

使用方法（HuggingFace Transformers）

直接使用HuggingFace transformers中可用的模型。我们使用了来自pyserini的实现的Aggretriever here 。

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

model_name = '/store/scratch/s269lin/experiments/aggretriever/hf_model/aggretriever-cocondenser'
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: take the last-layer hidden state of the [CLS] token
query_emb = query_encoder.encode(query)
ctx_emb = context_encoder.encode(contexts)
# Compute similarity scores using dot product
score1 = query_emb @ ctx_emb[0]  # 45.56658
score2 = query_emb @ ctx_emb[1]  # 45.81762