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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]
Both OpenOffice.org and LibreOffice support font embedding in the PDF export feature. [3] Font embedding in word processors is not widely supported nor interoperable. [4] [5] For example, if a .rtf file made in Microsoft Word is opened in LibreOffice Writer, it will usually remove the embedded fonts. [citation needed]
[12] [13] [14] Support for the commonly used TrueType and OpenType font formats has since been implemented in Safari 3.1, Opera 10, Mozilla Firefox 3.5 and Internet Explorer 9. In 2010, the WOFF compression method for TrueType and OpenType fonts was submitted to W3C by the Mozilla Foundation , Opera Software and Microsoft , and browsers have ...
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [ 1 ] [ 2 ] Like the original Transformer model, [ 3 ] T5 models are encoder-decoder Transformers , where the encoder processes the input text, and the decoder generates the output text.
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. [1]
The Web Embedding Fonts Tool, or WEFT, is Microsoft's utility for generating embeddable web fonts.. WEFT is used by webmasters to create 'font objects' that are linked to their web pages so that users using Microsoft's Internet Explorer web browser will see the pages displayed in the font style contained within the font object.
Words represented in an embedding vector were not necessarily consecutive anymore, but could leave gaps that are skipped over. [6] Formally, a k-skip-n-gram is a length-n subsequence where the components occur at distance at most k from each other. For example, in the input text: the rain in Spain falls mainly on the plain
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.