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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]
Two types of literal expression are usually offered: one with interpolation enabled, the other without. Non-interpolated strings may also escape sequences, in which case they are termed a raw string, though in other cases this is separate, yielding three classes of raw string, non-interpolated (but escaped) string, interpolated (and escaped) string.
In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance [8] by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset.
In computing, a script is a relatively short and simple set of instructions that typically automate an otherwise manual process. The act of writing a script is called scripting . A scripting language or script language is a programming language that is used for scripting.
Text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word embedding table. [1] At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism, allowing the signal for key tokens ...
The Rich Text Format was the standard file format for text-based documents in applications developed for Microsoft Windows. Microsoft did not initially make the RTF specification publicly available, making it difficult for competitors to develop document conversion features in their applications.
Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.
A prompt for a text-to-text language model can be a query, a command, or a longer statement including context, instructions, and conversation history. Prompt engineering may involve phrasing a query, specifying a style, choice of words and grammar, [ 3 ] providing relevant context, or describing a character for the AI to mimic.