Vector and Embedding Explained | Representation Learning | Bunny Labs | LLM | NLU | NLP | Text

in the realm of natural language processing vectors play a pivotal role in representing and processing textual data vectors are essentially mathematical representations of words sentences or documents that enable machines to understand and process human language this transformation of text into numerical vectors allows NLP models to analyze interpret and manipulate language data let's delve into the significance of vectors in NLP and how they're used to facilitate various language processing tasks in general we may use Dimensions such as 256 768 1024 or 2048 to represent a single entity however as humans we are unable to comprehend such high-dimensional spaces therefore we often reduce the dimensions to two or three in order to visualize the data for instance in the figure on the left hand side two dimensions are used to illustrate the differences among animals vectors can be created using techniques such as one hot encoding where each word is represented as a binary Vector with a one in the position corresponding to the words index in the vocabulary and zeros elsewhere this method results in high-dimensional sparse vectors that lack semantic information and struggle to capture relationships between words on the other hand word embeddings are a specific type of vector representation in NLP embeddings aim to capture the semantic relationships between words by mapping them to continuous Vector spaces embeddings are designed to capture the meaning and context of individual words allowing for semantic relationships and similarities to be encoded in the vector space in normal NLP applications we use over hundreds of Dimensions to represent tokens for the sake of Simplicity we use two dimensions for demonstration you can see that a bunny rabbit and a hair are very close because the meanings of these three words are very similar you may also notice that AA is next to them on the other hand dog and a puppy are very close while they are far away from a bunny and a pika obtaining embeddings is a fundamental process that involves converting tokens into numerical representations to enable machines to understand and process human language there are lots of techniques and algorithms used to obtain token embeddings each with its own advantages and applications the very first step is tokenization which breaks down text into individual tokens later on textual information is mapped to Vector space often using techniques like skip gram and mask language modeling which map words to high-dimensional Vector spaces based on their context and co-occurrence in large text corpora the next step is training The Chosen language model on a large Corpus of Text data to learn the semantic relationships between words fine-tuning may also be performed to adapt the embedding to specific NLP tasks or domains embeddings are the backbone of NLP providing a mathematical framework for representing and processing textual data from subword embeddings word embeddings to sentence representations it enables language models to comprehend and manipulate human language Paving the way for advancements in language understanding and generation as NLP continues to evolve the role of embeddings in empowering machines to understand and process language will undoubtedly remain Central to The Field's progress and innovation