Skip to content

Embeddings transformers

Embeddings

Embeddings are a fundamental concept in natural language processing (NLP).

In essence, embeddings are numerical representations of words, phrases, or even entire documents.

Think of them as a way to translate human language into a format that computers can understand and process.

Here's a breakdown: Each word or concept is assigned a unique vector (a list of numbers). Words with similar meanings or contexts will have vectors that are closer together in this "embedding space."

Why are embeddings so powerful?

Capturing semantic relationships: Embeddings allow us to see the relationships between words beyond just their literal definitions. For example, "king" and "queen" would be closer together than "king" and "banana."

Improved performance in NLP tasks: Embeddings are used in a wide range of NLP applications, such as:

  • Text classification: Categorizing documents into predefined categories.
  • Sentiment analysis: Determining the emotional tone of a piece of text.
  • Machine translation: Automatically translating text from one language to another.
  • Text generation: Creating new text that is coherent and grammatically correct.

Types of Embeddings:

There are various types of embeddings, each with its own strengths and weaknesses. Some popular ones include:

  • Word2Vec: A widely used technique that learns embeddings from large text corpora.

  • GloVe (Global Vectors for Word Representation): Another popular method that leverages global word co-occurrence statistics.

  • BERT (Bidirectional Encoder Representations from Transformers): A more advanced technique that considers the context of words in both directions.

Text Embeddings

Embeddings What are they and why they matter

Transformers : GPT : Generative Pre-Processing Transformer

Generative: This means it's designed to create new text, not just analyze existing text. You can think of it as a very advanced chatbot that can write stories, poems, articles, and more.

Pre-trained: GPT models are trained on massive amounts of text data before they are released. This pre-training allows them to learn the patterns and rules of language, giving them a broad understanding of how words work together.

Transformer: This refers to the specific type of neural network architecture used in GPT. Transformers are particularly good at handling sequential data like text because they can consider the context of words in a sentence.

Transformer Explainer. This is a very neat interactive visualization (with accompanying essay and video - scroll down for those) that explains the Transformer architecture for LLMs, using a GPT-2 model running directly in the browser using the ONNX runtime and Andrej Karpathy's nanoGPT project.

Transfomer Explainer