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In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis.Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). [9] However, more elaborate solutions based on word vector quantization have also been proposed.
The CBOW can be viewed as a ‘fill in the blank’ task, where the word embedding represents the way the word influences the relative probabilities of other words in the context window. Words which are semantically similar should influence these probabilities in similar ways, because semantically similar words should be used in similar contexts.
The first layer is the embedding layer, which contains three components: token type embeddings, position embeddings, and segment type embeddings. Token type: The token type is a standard embedding layer, translating a one-hot vector into a dense vector based on its token type.
An embedding, or a smooth embedding, is defined to be an immersion that is an embedding in the topological sense mentioned above (i.e. homeomorphism onto its image). [4] In other words, the domain of an embedding is diffeomorphic to its image, and in particular the image of an embedding must be a submanifold.
Meaning: If you aren't weighing yourself at a consistent time each day, expect to see different numbers on the scale. Eating or drinking anything adds weight, even the healthy stuff.
Font embedding has been possible with Portable Document Format (PDF), Microsoft Word for Windows and some other applications for many years. LibreOffice supports font embedding since version 4.1 in its Writer, Calc and Impress applications.
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