Introduction to Embeddings in Large Language Models
Embeddings are a fundamental concept in natural language processing (NLP) and are used in many state-of-the-art models. An embedding is a vector representation of a word or a phrase that captures its semantic meaning. It is a way of representing words in a high-dimensional space, where each dimension represents a different feature or characteristic of the word, such as its context, usage, and meaning.
One common type of embedding is the word embedding, which represents each word as a vector in a high-dimensional space. This vector captures the meaning of the word in the context of the other words in the sentence or document. For example, the word 'apple' might be represented as a vector with high values in dimensions related to fruit, food, and sweetness, and low values in dimensions related to animals, transportation, and clothing.
Embeddings are typically learned from large datasets using unsupervised learning techniques such as neural networks. One popular algorithm for learning embeddings is Word2Vec, which uses a neural network to predict the context in which a word appears. Another algorithm is GloVe, which uses global word co-occurrence statistics to learn word embeddings.
Embeddings have become an important tool in NLP and are used in many applications, including text classification, language translation, and sentiment analysis. They have also been used in other fields, such as computer vision and speech recognition, where they are used to represent visual and audio data as vectors.
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