Tia  Gottlieb

Tia Gottlieb

1597805160

BART for Paraphrasing with Simple Transformers

Introduction

BART is a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension -

Don’t worry if that sounds a little complicated; we are going to break it down and see what it all means. To add a little bit of background before we dive into BART, it’s time for the now-customary ode to Transfer Learning with self-supervised models. It’s been said many times over the past couple of years, but Transformers really have achieved incredible success in a wide variety of Natural Language Processing (NLP) tasks.

BART uses a standard Transformer architecture (Encoder-Decoder) like the original Transformer model used for neural machine translation but also incorporates some changes from BERT (only uses the encoder) and GPT (only uses the decoder). You can refer to the _2.1 Architecture _section of the BART paper for more details.

Pre-Training BART

BART is pre-trained by minimizing the cross-entropy loss between the decoder output and the original sequence.

Masked Language Modeling (MLM)

MLM models such as BERT are pre-trained to predict masked tokens. This process can be broken down as follows:

  1. Replace a random subset of the input with a _mask token [MASK]. _(Adding noise/corruption)
  2. The model predicts the original tokens for each of the [MASK]tokens. (Denoising)

Importantly, BERT models can “see” the full input sequence (with some tokens replaced with [MASK]) when attempting to predict the original tokens. This makes BERT a bidirectional model, i.e. it can “see” the tokens before and after the masked tokens.

#machine-learning #artificial-intelligence #data-science #nlp #deep learning

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BART for Paraphrasing with Simple Transformers

Ajay Kapoor

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Digital Transformation Consulting Services & solutions

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Chelsie  Towne

Chelsie Towne

1596716340

A Deep Dive Into the Transformer Architecture – The Transformer Models

Transformers for Natural Language Processing

It may seem like a long time since the world of natural language processing (NLP) was transformed by the seminal “Attention is All You Need” paper by Vaswani et al., but in fact that was less than 3 years ago. The relative recency of the introduction of transformer architectures and the ubiquity with which they have upended language tasks speaks to the rapid rate of progress in machine learning and artificial intelligence. There’s no better time than now to gain a deep understanding of the inner workings of transformer architectures, especially with transformer models making big inroads into diverse new applications like predicting chemical reactions and reinforcement learning.

Whether you’re an old hand or you’re only paying attention to transformer style architecture for the first time, this article should offer something for you. First, we’ll dive deep into the fundamental concepts used to build the original 2017 Transformer. Then we’ll touch on some of the developments implemented in subsequent transformer models. Where appropriate we’ll point out some limitations and how modern models inheriting ideas from the original Transformer are trying to overcome various shortcomings or improve performance.

What Do Transformers Do?

Transformers are the current state-of-the-art type of model for dealing with sequences. Perhaps the most prominent application of these models is in text processing tasks, and the most prominent of these is machine translation. In fact, transformers and their conceptual progeny have infiltrated just about every benchmark leaderboard in natural language processing (NLP), from question answering to grammar correction. In many ways transformer architectures are undergoing a surge in development similar to what we saw with convolutional neural networks following the 2012 ImageNet competition, for better and for worse.

#natural language processing #ai artificial intelligence #transformers #transformer architecture #transformer models

Eve  Klocko

Eve Klocko

1596736920

A Deep Dive Into the Transformer Architecture

Transformers for Natural Language Processing

It may seem like a long time since the world of natural language processing (NLP) was transformed by the seminal “Attention is All You Need” paper by Vaswani et al., but in fact that was less than 3 years ago. The relative recency of the introduction of transformer architectures and the ubiquity with which they have upended language tasks speaks to the rapid rate of progress in machine learning and artificial intelligence. There’s no better time than now to gain a deep understanding of the inner workings of transformer architectures, especially with transformer models making big inroads into diverse new applications like predicting chemical reactions and reinforcement learning.

Whether you’re an old hand or you’re only paying attention to transformer style architecture for the first time, this article should offer something for you. First, we’ll dive deep into the fundamental concepts used to build the original 2017 Transformer. Then we’ll touch on some of the developments implemented in subsequent transformer models. Where appropriate we’ll point out some limitations and how modern models inheriting ideas from the original Transformer are trying to overcome various shortcomings or improve performance.

What Do Transformers Do?

Transformers are the current state-of-the-art type of model for dealing with sequences. Perhaps the most prominent application of these models is in text processing tasks, and the most prominent of these is machine translation. In fact, transformers and their conceptual progeny have infiltrated just about every benchmark leaderboard in natural language processing (NLP), from question answering to grammar correction. In many ways transformer architectures are undergoing a surge in development similar to what we saw with convolutional neural networks following the 2012 ImageNet competition, for better and for worse.

#natural language processing #ai artificial intelligence #transformers #transformer architecture #transformer models

Edna  Bernhard

Edna Bernhard

1596525540

A Deep Dive Into the Transformer Architecture

Transformers for Natural Language Processing

It may seem like a long time since the world of natural language processing (NLP) was transformed by the seminal “Attention is All You Need” paper by Vaswani et al., but in fact that was less than 3 years ago. The relative recency of the introduction of transformer architectures and the ubiquity with which they have upended language tasks speaks to the rapid rate of progress in machine learning and artificial intelligence. There’s no better time than now to gain a deep understanding of the inner workings of transformer architectures, especially with transformer models making big inroads into diverse new applications like predicting chemical reactions and reinforcement learning.

Whether you’re an old hand or you’re only paying attention to transformer style architecture for the first time, this article should offer something for you. First, we’ll dive deep into the fundamental concepts used to build the original 2017 Transformer. Then we’ll touch on some of the developments implemented in subsequent transformer models. Where appropriate we’ll point out some limitations and how modern models inheriting ideas from the original Transformer are trying to overcome various shortcomings or improve performance.

What Do Transformers Do?

Transformers are the current state-of-the-art type of model for dealing with sequences. Perhaps the most prominent application of these models is in text processing tasks, and the most prominent of these is machine translation. In fact, transformers and their conceptual progeny have infiltrated just about every benchmark leaderboard in natural language processing (NLP), from question answering to grammar correction. In many ways transformer architectures are undergoing a surge in development similar to what we saw with convolutional neural networks following the 2012 ImageNet competition, for better and for worse.

#natural language processing #ai artificial intelligence #transformers #transformer architecture #transformer models #ai

Digital Transformation - Security Solutions, Technologies, Challenges and Vision

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