Final thoughts – Navigating Real-World Data Science Case Studies in Action

03/05/2021
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Final thoughts

Remember, while we strive to achieve the best model performance, it’s crucial to constantly revisit the fairness aspect. Addressing fairness isn’t a one-time task but rather an iterative process that involves refining the model, re-evaluating fairness metrics, and ensuring that our model decisions are as impartial as possible. Our ultimate aim is to ensure the equitable treatment of all subgroups while making accurate predictions.

Text embeddings using pretrainedmodels and OpenAI

In the realm of natural language processing (NLP), the quest for effectively converting textual information into mathematical representations, often referred to as embeddings, has always been paramount. Embeddings allow machines to “understand” and process textual content, bridging the gap between human language and computational tasks. In our previous NLP chapters, we dived deep into the creation of text embeddings and witnessed the transformative power of large language models (LLMs) such as BERT in capturing the nuances of language.

Enter OpenAI, a forefront entity in the field of artificial intelligence research. OpenAI has not only made significant contributions to the LLM landscape but has also provided various tools and engines to foster advancements in embedding technology. In this study, we’re going to embark on a detailed exploration of text embeddings using OpenAI’s offerings.

By embedding paragraphs from this textbook, we’ll demonstrate the efficacy of OpenAI’s embeddings in answering natural language queries. For instance, a seemingly whimsical question such as “How many horns does a flea have?” can be efficiently addressed by scanning through the embedded paragraphs, showcasing the prowess of semantic search.

Setting up and importing necessary libraries

Before we dive into the heart of this case study, it’s essential to set our environment right. We need to ensure we have the appropriate libraries imported for the tasks we’ll perform. This case study introduces a couple of new packages:


import os
import openai
import numpy as np
from urllib.request import urlopen
from openai.embeddings_utils import get_embedding
from sentence_transformers import util

Let’s break down our imports:

  • os: Essential for interacting with the operating system – in our case, to fetch the API key.
  • openai: The official OpenAI library, which will grant us access to various models and utilities.
  • numpy: A fundamental package for scientific computing in Python. Helps in manipulating large data and arrays.
  • urlopen: Enables us to fetch data from URLs, which will be handy when we’re sourcing our text data.
  • get_embedding: A utility from OpenAI to convert text to embeddings.
  • sentence_transformers.util: Contains helpful utilities for semantic searching, a cornerstone of our case study.

Once our environment is set up, the next step is to configure our connection to the OpenAI service:
openai.api_key = os.environ[‘OPENAI_API_KEY’]
ENGINE = ‘text-embedding-ada-002’

Here, we’re sourcing our API key from our environment variables. It’s a secure way to access sensitive keys without hardcoding them. The chosen engine for our embeddings is text-embedding-ada-002.