Demystifying Embeddings

Using below code snippet I am going to explain how embeddings are created during text data processing and significance of these in context of different AI/NLP Applications.

Here's a simple code example that demonstrates how word embeddings can be created and used in Natural Language Processing (NLP) tasks using pretrained Bert model that is an advanced NLP Model developed by Google.

It starts with splitting the text into individual words or tokens.

Lets try to decipher the final output(embedding) which is nothing but a tensor object.

When we use BERT (a popular NLP model), it generates embeddings (representations) for each word or sequence of characters in the text. These embeddings are like special codes that capture different aspects of the text's meaning and context.

Imagine BERT as having multiple layers, each looking at the text from a different angle. Each layer generates its own embedding for the words in the text. So, for each word, we have 13 different embeddings (layers) in total.

To make sense of all these embeddings and get a comprehensive representation of the text, we can combine or pool information from multiple layers. It's like taking the most important details from each layer and putting them together to form a complete picture.

By pooling the embeddings, we can create a final representation that captures a deeper understanding of the text. This allows us to use BERT's embeddings for various NLP tasks like sentiment analysis, text classification, or question-answering.

Overall, BERT's embeddings provide a rich and nuanced view of the text, and by combining information from different layers, we can extract the most relevant and meaningful insights for our NLP applications.

When we create an embedding for a word, sentence, or image that represents the artifact in the multidimensional space, we can do any number of things with this embedding. For example, for tasks that focus on content understanding in machine learning, we are often interested in comparing two given items to see how similar they are. Projecting text as a vector allows us to do so with mathematical rigor and compare words in a shared embedding space.

Note: A text tensor, also known as a text embedding, refers to a numerical representation of text data that captures the semantic meaning and contextual information of words or sequences of characters. In the context of natural language processing (NLP), a text tensor is created using techniques such as word embeddings or contextual embeddings.