Background
One of the core challenges in information retrieval is to ensure that the returned results are highly relevant to the user's query. Traditional approaches such as BM25 are fast but have limited semantic understanding, and Rankify systematically addresses this problem by combining multiple techniques.
prescription
- skill set: A two-stage process of "search + reordering" is used:
1. Initial screening with BM25/DPR (retrieve module)
2. Optimization of results by 24 reordering models (reranking modules) such as MonoT5 - concrete operation::
- Install the full version:
pip install "rankify[all]" - Load the Wikipedia pre-built index:
retriever = Retriever(method="bge", index_type="wiki") - Reordering with Sentence Transformer:
reranker = Reranking(method="sentence-transformer")
- Install the full version:
- Evaluation and validation: Use
MetricsModule calculates Recall@10 and other metrics to validate improved results
key point
A mix of dense retrieval (e.g., BGE) and neural reordering models is recommended to improve relevance metrics by an average of 15-301 TP3T compared to a single method (MS MARCO benchmarking results).
This answer comes from the articleRankify: a Python toolkit supporting information retrieval and reorderingThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of insufficient relevance of information retrieval results?
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