From 7b7cff6f282da721a1b8e71aab1de99bd9027db2 Mon Sep 17 00:00:00 2001 From: Sabrina Aquino <77522207+sabrinaaquino@users.noreply.github.com> Date: Mon, 14 Oct 2024 15:22:45 -0300 Subject: [PATCH] Update qdrant-landing/content/articles/cross-encoder-integration-gsoc.md Co-authored-by: George --- .../content/articles/cross-encoder-integration-gsoc.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/articles/cross-encoder-integration-gsoc.md b/qdrant-landing/content/articles/cross-encoder-integration-gsoc.md index 8f87b56fb..ead6a3324 100644 --- a/qdrant-landing/content/articles/cross-encoder-integration-gsoc.md +++ b/qdrant-landing/content/articles/cross-encoder-integration-gsoc.md @@ -39,7 +39,7 @@ Below is a diagram that represents the overall workflow for this project, detail ### 1. Building a New Input-Output Scheme -FastEmbed already had support for embeddings, but re-ranking with cross-encoders meant building a completely new family of classes. These models accept a query and a set of documents, then return a list of relevance scores. For that, I created the base classes like TextCrossEncoderBase and OnnxCrossEncoder, taking inspiration from existing text embedding models. +FastEmbed already had support for embeddings, but re-ranking with cross-encoders meant building a completely new family of classes. These models accept a query and a set of documents, then return a list of relevance scores. For that, I created the base classes like `TextCrossEncoderBase` and `OnnxCrossEncoder`, taking inspiration from existing text embedding models. One thing I had to ensure was that the new class hierarchy was user-friendly. Users should be able to work with cross-encoders without needing to know the complexities of the underlying models. For instance, they should be able to just write: