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Byte pair encoding [1] [2] (also known as digram coding) [3] is an algorithm, first described in 1994 by Philip Gage, for encoding strings of text into tabular form for use in downstream modeling. [4] A slightly-modified version of the algorithm is used in large language model tokenizers. The original version of the algorithm focused on ...
This kind of expression tree consists of the phenotypic expression of GEP genes, whereas the genes are linear strings encoding these complex structures. For this particular example, the linear string corresponds to: 01234567 Q*-+abcd. which is the straightforward reading of the expression tree from top to bottom and from left to right.
The Burrows–Wheeler transform (BWT, also called block-sorting compression) rearranges a character string into runs of similar characters. This is useful for compression, since it tends to be easy to compress a string that has runs of repeated characters by techniques such as move-to-front transform and run-length encoding.
A string-searching algorithm, sometimes called string-matching algorithm, is an algorithm that searches a body of text for portions that match by pattern. A basic example of string searching is when the pattern and the searched text are arrays of elements of an alphabet ( finite set ) Σ.
Block diagram for the full Transformer architecture. Schematic object hierarchy for the full Transformer architecture, in object-oriented programming style. The final points of detail are the residual connections and layer normalization (LayerNorm, or LN), which while conceptually unnecessary, are necessary for numerical stability and ...
In a typical document classification task, the input to the machine learning algorithm (both during learning and classification) is free text. From this, a bag of words (BOW) representation is constructed: the individual tokens are extracted and counted, and each distinct token in the training set defines a feature (independent variable) of each of the documents in both the training and test sets.
This can be done using a variety of techniques, such as one-hot encoding, label encoding, and ordinal encoding. The type of feature that is used in feature engineering depends on the specific machine learning algorithm that is being used. Some machine learning algorithms, such as decision trees, can handle both numerical and categorical features.
The design has its origins from pre-training contextual representations, including semi-supervised sequence learning, [23] generative pre-training, ELMo, [24] and ULMFit. [25] Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus .