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What are Embeddings?
Embeddings are dense vectors of fixed sizes that represent objects like text, products, or images in a continuous vector space. Unlike one-hot encodings, embeddings capture semantic relationships.

Embeddings of Different Data Types

Tabular data models transform categorical variables into continuous vectors, simplifying complex relationships and making it easier for models to learn patterns. This is particularly useful in systems like recommendation engines and customer segmentation tools.
Image embedding models convert entire images into fixed-length vectors, facilitating tasks like similarity matching and object recognition. This enhances user experiences in visual search platforms and content discovery engines.
Text embeddings turn words or sentences into continuous vectors, allowing for advanced features like semantic search, sentiment analysis, and natural language understanding. Such embeddings improve the capabilities of chatbots, search engines, and automated customer service platforms.
One can also generate multi-model hybrid embeddings using text and image data together. Product embeddings is a great example of multi-model embeddings. For instance, a great product embedding model would embed items like clothes and cars into embedding space and their distance would be adjusted parallel to semantic relationships.
A visualization of product embeddings

LLMs Empower Textual Embeddings

  • Contextual Embeddings: Unlike static word embeddings like Word2Vec, LLMs (Language Model Learners) like BERT and GPT provide embeddings that consider context, providing better semantic representation.
  • Task Specific: LLMs can be fine-tuned for specific tasks, improving performance over general-purpose embeddings.
  • Text and Beyond: Some LLMs can be trained to understand multiple types of data, creating embeddings that represent complex relationships across text, images, and more.
  • One Model, Many Tasks: LLMs can generate embeddings for a wide range of tasks, reducing the need for task-specific models.

Generating Embeddings

Generating embeddings has never been more accessible thanks to a wealth of open-source and as-a-service options available to developers. From text to images, there are specialized architectures and pre-trained models ready for integration. Open-source libraries like Hugging Face offer a multitude of text-based models like BERT and GPT-2, allowing for quick implementation of NLP tasks.
Similarly, Keras provides various architectures for both image and text embeddings, including but not limited to LSTM and VGG models.
An example of generating image embeddings with VGG16
from tensorflow.keras.applications import VGG16
model = VGG16(weights='imagenet', include_top=False)
image_embedding = model.predict(preprocessed_image)
For those who prefer ready-to-use solutions, services like OpenAI and Cohere offer powerful, fine-tuned models as a service, abstracting away much of the complexity involved in training and maintenance. These diverse avenues for generating embeddings make it easier than ever to create intelligent, data-driven applications across industries.