chore: import upstream snapshot with attribution

docs/source/Introduction/reranker.rst

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+Reranker
+========
+
+.. tip::
+   
+   If you are already familiar with the concepts, take a look at the :doc:`BGE rerankers <../bge/index>`!
+
+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.