Update some cookbook titles (#795)

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Shyamal H Anadkat 2023-10-17 17:39:42 -07:00 committed by GitHub
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5 changed files with 15 additions and 9 deletions

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"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Clustering\n", "## K-means Clustering in Python using OpenAI\n",
"\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)." "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)."
] ]

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"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Code search\n", "## Code search using embeddings\n",
"\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." "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."
] ]
}, },
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{ {
"cells": [ "cells": [
{ {
"attachments": {},
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Get embeddings\n", "## Using embeddings\n",
"\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." "This notebook contains some helpful snippets you can use to embed text with the 'text-embedding-ada-002' model via the OpenAI API."
] ]
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] ]
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"deepnote_cell_type": "markdown" "deepnote_cell_type": "markdown"
}, },
"source": [ "source": [
"# Evaluating Abstractive Summarization\n", "# How to evaluate a summarization task\n",
"\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", "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", "\n",

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