From 45e33df2660668acabda19aa243aa618b79a07b9 Mon Sep 17 00:00:00 2001 From: Pringled Date: Fri, 22 Nov 2024 16:26:12 +0100 Subject: [PATCH] Updated readme --- tutorials/semantic_chunking.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/semantic_chunking.ipynb b/tutorials/semantic_chunking.ipynb index 9c6b2c3..551d5a6 100644 --- a/tutorials/semantic_chunking.ipynb +++ b/tutorials/semantic_chunking.ipynb @@ -6,7 +6,7 @@ "source": [ "**Semantic Chunking with Chonkie and Model2Vec**\n", "\n", - "Semantic chunking is a task of identifying the semantic boundaries of a piece of text. In this tutorial, we will use the [Chonkie](https://github.com/bhavnicksm/chonkie) library to perform semantic chunking on the book War & Peace. Chonkie is a library that provides a lightweight and fast solution to semantic chunking using pre-trained models. It supports our [potion models](https://huggingface.co/collections/minishlab/potion-6721e0abd4ea41881417f062) out of the box, which we will be using in this tutorial.\n", + "Semantic chunking is a task of identifying the semantic boundaries of a piece of text. In this tutorial, we will use the [Chonkie](https://github.com/bhavnicksm/chonkie) library to perform semantic chunking on the book War and Peace. Chonkie is a library that provides a lightweight and fast solution to semantic chunking using pre-trained models. It supports our [potion models](https://huggingface.co/collections/minishlab/potion-6721e0abd4ea41881417f062) out of the box, which we will be using in this tutorial.\n", "\n", "After chunking our text, we will be using [Vicinity](https://github.com/MinishLab/vicinity), a lightweight nearest neighbors library, to create an index of our chunks and query them." ]