Update some cookbook titles (#795)

examples/Clustering.ipynb

@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Clustering\n",
+    "## K-means Clustering in Python using OpenAI\n",
     "\n",
     "We use a simple k-means algorithm to demonstrate how clustering can be done. Clustering can help discover valuable, hidden groupings within the data. The dataset is created in the [Get_embeddings_from_dataset Notebook](Get_embeddings_from_dataset.ipynb)."
    ]

examples/Code_search.ipynb → examples/Code_search_using_embeddings.ipynb

@@ -5,12 +5,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Code search\n",
+    "## Code search using embeddings\n",
     "\n",
     "This notebook shows how Ada embeddings can be used to implement semantic code search. For this demonstration, we use our own [openai-python code repository](https://github.com/openai/openai-python). We implement a simple version of file parsing and extracting of functions from python files, which can be embedded, indexed, and queried."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -98,6 +99,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -132,6 +134,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -242,6 +245,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [

examples/Get_embeddings.ipynb → examples/Using_embeddings.ipynb

@@ -1,10 +1,11 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Get embeddings\n",
+    "## Using embeddings\n",
     "\n",
     "This notebook contains some helpful snippets you can use to embed text with the 'text-embedding-ada-002' model via the OpenAI API."
    ]
@@ -35,6 +36,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [

examples/evaluation/How_to_eval_abstractive_summarization.ipynb

@@ -14,7 +14,7 @@
     "deepnote_cell_type": "markdown"
    },
    "source": [
-    "# Evaluating Abstractive Summarization\n",
+    "# How to evaluate a summarization task\n",
     "\n",
     "In this notebook we delve into the evaluation techniques for abstractive summarization tasks using a simple example. We explore traditional evaluation methods like [ROUGE](https://aclanthology.org/W04-1013/) and [BERTScore](https://arxiv.org/abs/1904.09675), in addition to showcasing a more novel approach using LLMs as evaluators.\n",
     "\n",

registry.yaml

@@ -43,8 +43,8 @@
     - embeddings
     - completions
 
-- title: Code search
-  path: examples/Code_search.ipynb
+- title: Code search using embeddings
+  path: examples/Code_search_using_embeddings
   date: 2022-03-10
   authors:
     - BorisPower
@@ -107,8 +107,8 @@
     - completions
     - functions
 
-- title: Get embeddings
-  path: examples/Get_embeddings.ipynb
+- title: Using embeddings
+  path: examples/Using_embeddings.ipynb
   date: 2022-03-10
   authors:
     - BorisPower
@@ -445,7 +445,7 @@
   tags:
     - dall-e
 
-- title: Evaluating Abstractive Summarization
+- title: How to evaluate a summarization task
   path: examples/evaluation/How_to_eval_abstractive_summarization.ipynb
   date: 2023-08-16
   authors: