mirror of
https://github.com/FlagOpen/FlagEmbedding.git
synced 2026-01-02 02:02:11 +00:00
19 lines
1.1 KiB
ReStructuredText
19 lines
1.1 KiB
ReStructuredText
Reranker
|
|
========
|
|
|
|
Reranker, or Cross-Encoder, is a model that refines the ranking of candidate pairs (e.g., query-document pairs) by jointly encoding and scoring them.
|
|
|
|
Typically, we use embedder as a Bi-Encoder. It first computes the embeddings of two input sentences, then compute their similarity using metrics such as cosine similarity or Euclidean distance.
|
|
Whereas a reranker takes two sentences at the same time and directly computer a score representing their similarity.
|
|
|
|
The following figure shows their difference:
|
|
|
|
.. figure:: https://raw.githubusercontent.com/UKPLab/sentence-transformers/master/docs/img/Bi_vs_Cross-Encoder.png
|
|
:width: 500
|
|
:align: center
|
|
|
|
Bi-Encoder & Cross-Encoder (from Sentence Transformers)
|
|
|
|
Although Cross-Encoder usually has better performances than Bi-Encoder, it is extremly time consuming to use Cross-Encoder if we have a great amount of data.
|
|
Thus a widely accepted approach is to use a Bi-Encoder for initial retrieval (e.g., selecting the top 100 candidates from 100,000 sentences) and then refine the ranking of the selected candidates using a Cross-Encoder for more accurate results.
|