Text embeddings are a cornerstone of Natural Language Processing (NLP), providing numerical representations of text where words or phrases become dense vectors of real numbers. This allows machines to understand semantic meaning and relationships between words, significantly improving their ability to process human language.

These embeddings are vital for tasks like text classification, information retrieval, and semantic similarity detection. OpenAI recommends the Ada V2 model for creating them, leveraging the GPT series' strength in capturing contextual meaning and associations within text.

Before proceeding, familiarity with OpenAI's API and the openai Python package is assumed (see "Using GPT-3.5 and GPT-4 via the OpenAI API in Python" for guidance). Understanding of clustering, particularly k-Means, is also helpful (consult "Introduction to k-Means Clustering with scikit-learn in Python").

Applications of Text Embeddings:

Text embeddings find applications in numerous areas, including:

• Text Classification: Building accurate models for sentiment analysis or topic identification.
• Information Retrieval: Retrieving information relevant to a specific query, mimicking search engine functionality.
• Semantic Similarity Detection: Identifying and quantifying the semantic similarity between text snippets.
• Recommendation Systems: Enhancing recommendation quality by understanding user preferences from text interactions.
• Text Generation: Generating more coherent and contextually relevant text.
• Machine Translation: Improving machine translation quality by capturing cross-lingual semantic meaning.

Setup and Installation:

The following Python packages are necessary: os, openai, scipy.spatial.distance, sklearn.cluster.KMeans, and umap.UMAP. Install them using:

pip install -U openai scipy plotly-express scikit-learn umap-learn

Import the required libraries:

import os
import openai
from scipy.spatial import distance
import plotly.express as px
from sklearn.cluster import KMeans
from umap import UMAP

Configure your OpenAI API key:

openai.api_key = "<your_api_key_here>"</your_api_key_here>

(Remember to replace <your_api_key_here></your_api_key_here> with your actual key.)

Generating Embeddings:

This helper function uses the text-embedding-ada-002 model to generate embeddings:

def get_embedding(text_to_embed):
    response = openai.Embedding.create(
        model="text-embedding-ada-002",
        input=[text_to_embed]
    )
    embedding = response["data"][0]["embedding"]
    return embedding

Dataset and Analysis:

This example uses the Amazon musical instrument review dataset (available on Kaggle or the author's Github). For efficiency, a sample of 100 reviews is used.

import pandas as pd

data_URL = "https://raw.githubusercontent.com/keitazoumana/Experimentation-Data/main/Musical_instruments_reviews.csv"
review_df = pd.read_csv(data_URL)[['reviewText']]
review_df = review_df.sample(100)
review_df["embedding"] = review_df["reviewText"].astype(str).apply(get_embedding)
review_df.reset_index(drop=True, inplace=True)

Semantic Similarity:

The Euclidean distance, calculated using scipy.spatial.distance.pdist(), measures the similarity between review embeddings. Smaller distances indicate greater similarity.

Cluster Analysis (K-Means):

K-Means clustering groups similar reviews. Here, three clusters are used:

kmeans = KMeans(n_clusters=3)
kmeans.fit(review_df["embedding"].tolist())

Dimensionality Reduction (UMAP):

UMAP reduces the embedding dimensionality to two for visualization:

reducer = UMAP()
embeddings_2d = reducer.fit_transform(review_df["embedding"].tolist())

Visualization:

A scatter plot visualizes the clusters:

fig = px.scatter(x=embeddings_2d[:, 0], y=embeddings_2d[:, 1], color=kmeans.labels_)
fig.show()

Further Exploration:

For advanced learning, explore DataCamp resources on fine-tuning GPT-3 and the OpenAI API cheat sheet.

The code examples are presented in a more concise and organized manner, improving readability and understanding. The image is included as requested.

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