From be5796947b06bad060ec6d4be56492ff36929729 Mon Sep 17 00:00:00 2001 From: Robin Date: Thu, 6 Jun 2024 21:24:58 +0530 Subject: [PATCH] [CI] pre-commit hook trailing whitespace fix --- docs/source/posts/2024/2024-06-06-week-1-robin.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/posts/2024/2024-06-06-week-1-robin.rst b/docs/source/posts/2024/2024-06-06-week-1-robin.rst index 4a13dbf8d..601beabc0 100644 --- a/docs/source/posts/2024/2024-06-06-week-1-robin.rst +++ b/docs/source/posts/2024/2024-06-06-week-1-robin.rst @@ -31,7 +31,7 @@ I also merged a `PR `_ on FURY which f 3) **Deciding which embedding model to use** A good embedding model is necessary to generate embeddings which we then upsert into the DB. Ollama had embedding model support, but I found the catalogue very small and the models they provided were not powerful enough. Therefore I decided to try using HuggingFace Sentence Transformers. -Sentence Transformers have a very vibrant catalogue of models available of various sizes. I chose `gte-large-en-v1.5 `_ from Alibaba-NLP, an 8k context, 434 million parameter model. It only had a modest memory requirement of 1.62 GB. +Sentence Transformers have a very vibrant catalogue of models available of various sizes. I chose `gte-large-en-v1.5 `_ from Alibaba-NLP, an 8k context, 434 million parameter model. It only had a modest memory requirement of 1.62 GB. Performance wise, it ranks 11th on the `MTEB leaderboard `_. It is a very interesting model due to its size:performance ratio. 4) **Hosting the embedding model**