This page details the command-line utility for the Embedding Atlas, a tool for exploring large text datasets with metadata. It covers installation, data loading (local and Hugging Face), visualization of embeddings using SentenceTransformers and UMAP, and usage instructions with available options.
A guide on how to use OpenAI embeddings and clustering techniques to analyze survey data and extract meaningful topics and actionable insights from the responses.
The process involves transforming textual survey responses into embeddings, grouping similar responses through clustering, and then identifying key themes or topics to aid in business improvement.
The process involves transforming textual survey responses into embeddings, grouping similar responses through clustering, and then identifying key themes or topics to aid in business improvement.
- Challenges in measuring similarity between unstructured text data like movie descriptions.
- Simple NLP methods may not yield meaningful results; thus, a controlled vocabulary is proposed.
- Using an LLM, a genre list is generated for movie titles, which helps improve the similarity model.
A function is created to find the most similar movies to a given title based on cosine similarity scores.
Network visualization highlights clusters of genres linked via movies, showcasing potential improvements in recommender systems.
- Simple NLP methods may not yield meaningful results; thus, a controlled vocabulary is proposed.
- Using an LLM, a genre list is generated for movie titles, which helps improve the similarity model.
A function is created to find the most similar movies to a given title based on cosine similarity scores.
Network visualization highlights clusters of genres linked via movies, showcasing potential improvements in recommender systems.
Alternative to t-SNE and PCA
