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Bidirectional Encoder Representations From Transformers
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture. BERT dramatically improved the state-of-the-art for large language models. , BERT is a ubiquitous baseline in natural language processing (NLP) experiments. BERT is trained by masked token prediction and next sentence prediction. As a result of this training process, BERT learns contextual, latent representations of tokens in their context, similar to ELMo and GPT-2. It found applications for many natural language processing tasks, such as coreference resolution and polysemy resolution. It is an evolutionary step over ELMo, and spawned the study of "BERTology", which attempts to interpret what is learned by BERT. BERT was originally implemented in the English language at two model sizes, BERTBASE (110 million ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Google AI
Google AI is a division of Google dedicated to artificial intelligence. It was announced at Google I/O 2017 by CEO Sundar Pichai. This division has expanded its reach with research facilities in various parts of the world such as Zurich, Paris, Israel, and Beijing. In 2023, Google AI was part of the reorganization initiative that elevated its head, Jeff Dean, to the position of chief scientist at Google. This reorganization involved the merging of Google Brain and DeepMind, a UK-based company that Google acquired in 2014 that operated separately from the company's core research. In March 2019, Google announced the creation of an Advanced Technology External Advisory Council (ATEAC) comprising eight members: Alessandro Acquisti, Bubacarr Bah, De Kai, Dyan Gibbens, Joanna Bryson, Kay Coles James, Luciano Floridi and William Joseph Burns. Following objections from a large number of Google staff to the appointment of Kay Coles James, the Council was abandoned within one ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
BookCorpus
BookCorpus (also sometimes referred to as the Toronto Book Corpus) is a Data set, dataset consisting of the text of around 7,000 self-published books web scraping, scraped from the indie ebook distribution website Smashwords. It was the main Text corpus, corpus used to train the initial generative pre-trained transformer, GPT model by OpenAI, and has been used as training data for other early large language models including Google's BERT (language model), BERT. The dataset consists of around 985 million words, and the books that comprise it span a range of genres, including romance, science fiction, and fantasy. The corpus was introduced in a 2015 paper by researchers from the University of Toronto and MIT titled "Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books". The authors described it as consisting of "free books written by yet unpublished authors," yet this is factually incorrect. These books were published by self-publishe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sine Wave
A sine wave, sinusoidal wave, or sinusoid (symbol: ∿) is a periodic function, periodic wave whose waveform (shape) is the trigonometric function, trigonometric sine, sine function. In mechanics, as a linear motion over time, this is ''simple harmonic motion''; as rotation, it corresponds to ''uniform circular motion''. Sine waves occur often in physics, including wind waves, sound waves, and light waves, such as monochromatic radiation. In engineering, signal processing, and mathematics, Fourier analysis decomposes general functions into a sum of sine waves of various frequencies, relative phases, and magnitudes. When any two sine waves of the same frequency (but arbitrary phase (waves), phase) are linear combination, linearly combined, the result is another sine wave of the same frequency; this property is unique among periodic waves. Conversely, if some phase is chosen as a zero reference, a sine wave of arbitrary phase can be written as the linear combination of two sine wa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
BERT Input Embeddings
Bert or BERT may refer to: Persons, characters, or animals known as Bert *Bert (name), commonly an abbreviated forename and sometimes a surname *Bert, a character in the poem "Bert the Wombat" by The Wiggles; from their 1992 album ''Here Comes a Song'' *Bert (Sesame Street), fictional character on the TV series ''Sesame Street'' *Bert (horse), foaled 1934 * Bert (Mary Poppins), a Cockney chimney sweep in the book series & Disney film ''Mary Poppins'' * Iron Bert (one half of the two yellow diesels 'Arry and Bert), also in ''Thomas and Friends'' Places *Berd, Armenia, also known as Bert * Bert, Allier, a commune in the French of Allier (pronounced \bɛʁ\) *Bert, West Virginia Electronics and computing *Bit error rate test, a testing method for digital communication circuits *Bit error rate tester, a test equipment used for testing the bit error rate of digital communication circuits *HP Bert, a CPU in certain Hewlett-Packard programmable calculators *BERT (language model) (Bidir ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Byte Pair Encoding
Byte-pair encoding (also known as BPE, or digram coding) is an algorithm, first described in 1994 by Philip Gage, for encoding strings of text into smaller strings by creating and using a translation table. A slightly modified version of the algorithm is used in large language model tokenizers. The original version of the algorithm focused on compression. It replaces the highest-frequency pair of bytes with a new byte that was not contained in the initial dataset. A lookup table of the replacements is required to rebuild the initial dataset. The modified version builds "tokens" (units of recognition) that match varying amounts of source text, from single characters (including single digits or single punctuation marks) to whole words (even long compound words). Original algorithm The original BPE algorithm operates by iteratively replacing the most common contiguous sequences of characters in a target text with unused 'placeholder' bytes. The iteration ends when no sequences can be ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
BERT Encoder-only Attention
Bert or BERT may refer to: Persons, characters, or animals known as Bert *Bert (name), commonly an abbreviated forename and sometimes a surname *Bert, a character in the poem "Bert the Wombat" by The Wiggles; from their 1992 album ''Here Comes a Song'' *Bert (Sesame Street), fictional character on the TV series ''Sesame Street'' *Bert (horse), foaled 1934 * Bert (Mary Poppins), a Cockney chimney sweep in the book series & Disney film ''Mary Poppins'' * Iron Bert (one half of the two yellow diesels 'Arry and Bert), also in ''Thomas and Friends'' Places *Berd, Armenia, also known as Bert * Bert, Allier, a commune in the French of Allier (pronounced \bɛʁ\) *Bert, West Virginia Electronics and computing *Bit error rate test, a testing method for digital communication circuits *Bit error rate tester, a test equipment used for testing the bit error rate of digital communication circuits *HP Bert, a CPU in certain Hewlett-Packard programmable calculators *BERT (language model) (Bidir ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Transfer Learning
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks. This topic is related to the psychological literature on transfer of learning, although practical ties between the two fields are limited. Reusing/transferring information from previously learned tasks to new tasks has the potential to significantly improve learning efficiency. Since transfer learning makes use of training with multiple objective functions it is related to cost-sensitive machine learning and multi-objective optimization. History In 1976, Bozinovski and Fulgosi published a paper addressing transfer learning in neural network training. The paper gives a mathematical and geometrical model of the topic. In 1981, a report considered the application of transfer learni ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sentiment Analysis
Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly.Hamborg, Felix; Donnay, Karsten (2021)"NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles" "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume" Simple cases * "Coron ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Question Answering
Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP) that is concerned with building systems that automatically answer questions that are posed by humans in a natural language. Overview A question-answering implementation, usually a computer program, may construct its answers by querying a structured database of knowledge or information, usually a knowledge base. More commonly, question-answering systems can pull answers from an unstructured collection of natural language documents. Some examples of natural language document collections used for question answering systems include: * a collection of reference texts * internal organization documents and web pages * compiled newswire reports * a set of Wikipedia pages * a subset of World Wide Web pages Types of question answering Question-answering research attempts to develop ways of answering a wide range of question types, including fact, li ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Attention (machine Learning)
In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence. In natural language processing, importance is represented b"soft"weights assigned to each word in a sentence. More generally, attention encodes vectors called lexical token, token Word embedding, embeddings across a fixed-width context window, sequence that can range from tens to millions of tokens in size. Unlike "hard" weights, which are computed during the backwards training pass, "soft" weights exist only in the forward pass and therefore change with every step of the input. Earlier designs implemented the attention mechanism in a serial recurrent neural network (RNN) language translation system, but a more recent design, namely the Transformer (machine learning model), transformer, removed the slower sequential RNN and relied more heavily on the faster parallel attention scheme. Inspired by ideas about attention, attent ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Euclidean Space
Euclidean space is the fundamental space of geometry, intended to represent physical space. Originally, in Euclid's ''Elements'', it was the three-dimensional space of Euclidean geometry, but in modern mathematics there are ''Euclidean spaces'' of any positive integer dimension ''n'', which are called Euclidean ''n''-spaces when one wants to specify their dimension. For ''n'' equal to one or two, they are commonly called respectively Euclidean lines and Euclidean planes. The qualifier "Euclidean" is used to distinguish Euclidean spaces from other spaces that were later considered in physics and modern mathematics. Ancient Greek geometers introduced Euclidean space for modeling the physical space. Their work was collected by the ancient Greek mathematician Euclid in his ''Elements'', with the great innovation of '' proving'' all properties of the space as theorems, by starting from a few fundamental properties, called '' postulates'', which either were considered as evid ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Word Embedding
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. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors of real numbers. Methods to generate this mapping include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, explainable knowledge base method, and explicit representation in terms of the context in which words appear. Word and phrase embeddings, when used as the underlying input representation, have been shown to boost the performance in NLP tasks such as syntactic parsing and sentiment analysis. Development and history of the approach In distributional semantics ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
