The Adaptive Span Transformer is a deep learning model that uses a self-attention mechanism to process long sequences of data. It is an improved version of the Transformer model that allows the network to choose its own context size by utilizing adaptive masking. This way, each attention layer can gather information on its own context, resulting in better scaling to input sequences with more than 8 thousand tokens.
What is the Adaptive Span Transformer?
The Adaptive Span Transformer is a neur
The Adaptively Sparse Transformer: Understanding this Cutting-Edge Development in AI
If you’ve heard of Transformers in the context of artificial intelligence, then you might be interested to know about the latest iteration: the Adaptively Sparse Transformer. This new technology shows great promise in improving the efficiency and effectiveness of natural language processing (NLP) and other applications. Here’s everything you need to know about this cutting-edge development in AI.
What is the
What is ALBERT?
ALBERT is a transformer architecture that is based on BERT but with fewer parameters. It was designed to make it easier to grow the hidden size without increasing the parameter size of the vocabulary embeddings. ALBERT uses two parameter reduction techniques: factorized embeddings parameterization and cross-layer parameter sharing.
How does ALBERT work?
The first parameter reduction technique used in ALBERT is factorized embeddings parameterization. ALBERT decomposes the larg
AutoTinyBERT is an advanced version of BERT, which stands for Bidirectional Encoder Representations from Transformers. BERT is a powerful tool for natural language processing. It is a pre-trained deep learning model that can be fine-tuned for various language-related tasks.
What is AutoTinyBERT?
AutoTinyBERT is a more efficient version of BERT, which has been optimized through neural architecture search. One-shot learning is used to obtain a big Super Pretrained Language Model (SuperPLM), on
BART: A Denoising Autoencoder for Pretraining NLP Models
BART is a powerful tool used for natural language processing (NLP) that uses denoising autoencoders for pretraining sequence-to-sequence models. In simple terms, it helps computers understand natural language so they can perform various tasks, such as language translation or summarization.
How BART Works
Here's how BART works:
1. First, it takes input text and "corrupts" it with a noising function. This creates a set of sentences tha
The Bidirectional Encoder Representations from Transformers (BERT) is a powerful language model that uses a masked language model (MLM) pre-training objective to improve upon standard Transformers. BERT is a deep bidirectional Transformer that fuses the left and right contexts of a sentence together. Consequently, this allows for better contextual understanding of the input.
What is BERT?
BERT is a language model developed by Google that uses deep neural networks to better understand the cont
Introduction to BigBird
BigBird is one of the latest breakthroughs in natural language processing. It is a transformer-based model that uses a sparse attention mechanism to reduce the quadratic dependency of self-attention to linear in the number of tokens, making it possible for the model to scale to much longer sequence lengths (up to 8 times longer) while maintaining high performance. The model was introduced by researchers at Google Research in 2020 and has since generated significant excit
Get To Know BinaryBERT: An Overview of a New Language Model
If you're a tech enthusiast, then you've probably heard of BERT. It is the most impressive natural language processing (NLP) model that has ever been devised. It can understand the complexities of language and provide context for human-like responses. Now there is a new entry into the market: BinaryBERT. In this article, we're going to explore what BinaryBERT is, how it works, and what its benefits are.
What is BinaryBERT?
BinaryBER
BP-Transformer (BPT) is a new type of transformer that has gained popularity for self-attention tasks owing to its better balance between capability and computational complexity. It achieves this by partitioning the input sequence into multi-scale spans through binary partitioning.
Motivation for BP-Transformer
The motivation behind developing BP-Transformer was to overcome the limitations with existing transformer models that struggle with self-attention and are computationally expensive. BP
Charformer is a new type of model in the field of natural language processing that uses a unique approach to subword tokenization. Similar to other Transformer models, Charformer is designed to learn and process sequences of text. However, unlike other models that use a fixed subword tokenization strategy, Charformer is capable of learning its own subword representation in an end-to-end manner as part of the overall training process.
What is Transformer Language Model?
Before diving into Char
Introduction to Chinese Pre-trained Unbalanced Transformer
Chinese language processing has gained tremendous attention in AI research and development. One of the major challenges in Chinese natural language understanding and generation (NLU and NLG) is that they entail complex syntactical and semantic features. To overcome this challenge and improve the performance of Chinese NLU and NLG, Chinese Pre-trained Unbalanced Transformers (CPT) emerged as an effective solution.
What is CPT?
CPT is
Overview of ClipBERT Framework for Video-and-Language Tasks
ClipBERT is a newly developed framework used for end-to-end learning for video-and-language tasks. This method employs sparse sampling to compress required data by sampling one or very few sparsely selected short clips from a video at each training step. This is unique compared to most previous work that used densely extracted video features.
The Uniqueness of ClipBERT
During training, ClipBERT uses a sparse sampling technique where
CodeBERT is a special kind of computer model that can help people understand computer code and information written in English. It is called a bimodal model because it can understand both programming language (PL) and natural language (NL). This model can help people do many things, like find specific code that they need or automatically write descriptions of how code works.
How Does CodeBERT Work?
CodeBERT is made with a special kind of neural network called a Transformer. This network helps
CodeT5 is a new model that uses Transformer technology for better code understanding and generation. It is based on the T5 architecture, which has been extended to include two identifier tagging and prediction tasks that help the model to better leverage the token type information from programming languages. CodeT5 uses a bimodal dual learning objective for a bidirectional conversion between natural language and programming language, which helps improve the natural language-programming language
The Compressive Transformer is a type of neural network that is an extension of the Transformer model. It works by mapping past hidden activations, also known as memories, to a smaller set of compressed representations called compressed memories. This allows the network to better process information over time and use both short-term and long-term memory.
Compressive Transformer vs. Transformer-XL
The Compressive Transformer builds on the ideas of the Transformer-XL, which is another type of T
ConvBERT is an advanced software technology that was developed to modify the architecture of BERT. The new version of BERT includes a span-based dynamic convolution, replacing self-attention heads with direct modeling of local dependencies, taking advantage of convolution to better capture local dependency.
What is BERT architecture?
BERT is short for Bidirectional Encoder Representations from Transformers, developed by Google's Natural Language Processing (NLP) research team. BERT is a deep
CTRL is a machine learning model that uses conditional transformer language to generate text based on specific control codes. It can manipulate style, content, and task-specific behavior to create unique and targeted text.
What is CTRL?
Captioned Representation of Text with Location (CTRL) is a natural language processing model developed by the team at Salesforce. This machine learning model uses a transformer architecture to generate text that can be controlled by specific codes, allowing fo
DeBERTa is an advanced neural language model that aims to improve upon the popular BERT and RoBERTa models. It achieves this through the use of two innovative techniques: a disentangled attention mechanism and an enhanced mask decoder.
Disentangled Attention Mechanism
The disentangled attention mechanism is where each word is represented using two vectors that encode its content and position, respectively. This allows the attention weights among words to be computed using disentangle matrices