krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-09-25 22:18:30 +02:00
Code Issues Packages Projects Releases Wiki Activity

Update qdrant-landing/content/articles/cross-encoder-integration-gsoc.md

Browse Source 
Co-authored-by: George <panchuk.george@outlook.com>
This commit is contained in:
Sabrina Aquino
2024-10-14 15:22:45 -03:00
committed by GitHub
co-authored by George
parent b512a97ef7
commit 7b7cff6f28
1 changed files with 1 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/articles/cross-encoder-integration-gsoc.md
+1 -1
View File
@@ -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: