Vector embedding models are techniques used to convert data, such as words, sentences, or images, into numerical vectors in a continuous vector space. This transformation allows for easier manipulation and comparison of data, making it an important tool in various applications like natural language processing (NLP), recommendation systems, and image recognition. Common models for generating these embeddings include Word2Vec, GloVe, FastText, and BERT.
Word2Vec, developed by Google, is one of the most well-known models for creating word embeddings. It operates using two main architectures: Continuous Bag of Words (CBOW) and Skip-gram. CBOW predicts a target word based on its surrounding context words, while Skip-gram does the reverse by predicting the context words from a given target word. GloVe, created by Stanford, takes a different approach by focusing on the global statistical information of word co-occurrences in a corpus. It generates embeddings that represent the relationship between words based on their frequency in relation to all words in the dataset.
Another notable model is FastText, developed by Facebook. It enhances Word2Vec by representing each word as a bag of character n-grams, which allows it to generate better embeddings for rare words and handle out-of-vocabulary words more effectively. For more complex sentence or document embeddings, BERT (Bidirectional Encoder Representations from Transformers) offers a powerful alternative by using attention mechanisms to take context into account in both directions. Each of these models serves different needs and can greatly help developers in building applications that require semantic understanding of text or efficient data retrieval.