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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]
Pastel sticks historically tended to have lower saturation than paints of the same pigment, hence the name of this color family. The colors of this family are usually described as "soothing." [ 3 ] Pink , mauve , [ 4 ] and baby blue [ 5 ] are commonly used pastel colors, as are mint green , peach , periwinkle , lilac , and lavender .
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.
Displayed here is the web color light pink.The name of the web color is written as "lightpink" (no space) in HTML for computer display. Although this color is called "light pink", as can be ascertained by inspecting its hex code, it is actually a slightly deeper, not a lighter, tint of pink than the color pink itself.
Leon Dabo, Flowers in a Green Vase, c. 1910s, pastel. A pastel (US: / p æ ˈ s t ɛ l /) is an art medium that consist of powdered pigment and a binder.It can exist in a variety of forms, including a stick, a square, a pebble, and a pan of color, among other forms.
Table summary of the memory complexity and the link prediction accuracy of the knowledge graph embedding models according to Rossi et al. [5] in terms of Hits@10, MR, and MRR. Best results on each metric for each dataset are in bold. Model name Memory complexity FB15K (Hits@10) FB15K (MR) FB15K (MRR) FB15K - 237 (Hits@10) FB15K - 237 (MR)
In statistics and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body.
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