Introduction to Linformer
Linformer is a linear Transformer model that resolves the self-attention bottleneck associated with Transformer models. It utilizes a linear self-attention mechanism to improve performance and make the model more efficient. By decomposing self-attention into multiple smaller attentions through linear projection, Linformer effectively creates a low-rank factorization of the original attention, reducing the computational cost of processing the input sequence.
The Probl
Introduction to Longformer
Longformer is an advanced artificial intelligence (AI) architecture designed using the Transformer technology. It is designed to process long sequences of text, which is something traditional Transformer models struggle with. Due to their self-attention operation, traditional Transformers have a quadratic scaling with the length of a sequence. In contrast, the Longformer replaces this operation with one that scales linearly, making it an ideal tool for processing thou
MacBERT: A Transformer-Based Model for Chinese NLP with Modified Masking Strategy
If you're interested in natural language processing (NLP) or machine learning for languages other than English, you may have heard of BERT (Bidirectional Encoder Representations from Transformers), a model originally developed by Google AI. BERT is a pre-trained NLP model that uses Transformer architecture and has set state-of-the-art performance on various NLP tasks. However, BERT was pre-trained on English and h
MATE is a type of Transformer architecture that has been specifically designed to help people model web tables. Its design is centered around sparse attention, which enables each head to attend to either the rows or the columns of a table in an efficient way. Additionally, MATE makes use of attention heads that can reorder the tokens found either at the rows or columns of the table, and then apply a windowed attention mechanism.
Understanding Sparse Attention in MATE
The sparse attention mech
Overview of MobileBERT
MobileBERT is a type of inverted-bottleneck BERT that compresses and accelerates the popular BERT model. This means that it takes the original BERT model - which is a powerful machine learning tool for natural language processing - and makes it smaller and faster.
Think of it like this: imagine you have a large library filled with books of different sizes and genres. If you want to quickly find a book on a specific topic, it might take you a while to navigate through all
Understanding MHMA: The Multi-Head of Mixed Attention
The multi-head of mixed attention (MHMA) is a powerful algorithm that combines both self- and cross-attentions to encourage high-level learning of interactions between entities captured in various attention features. In simpler terms, it is a machine learning model that helps computers understand the relationships between different features of different domains. This is especially useful in tasks involving relationship modeling, such as huma
The NormFormer is a type of Pre-LN transformer that allows for more efficient and effective language processing through the use of additional normalization operations.
What is NormFormer?
NormFormer is a type of transformer that is used in natural language processing. Its purpose is to improve the efficiency and effectiveness of language processing by introducing additional normalization operations.
Normalization is a process that helps to reduce variation in a dataset. In natural language p
What is Nyströmformer?
If you have been following the development of natural language processing (NLP), you probably know about BERT and its remarkable ability to understand the nuances of language. Developed by Google, BERT is a deep learning model that uses transformers to process and understand text. However, BERT has one major weakness - it struggles with long texts. In order to overcome this limitation, researchers have developed Nyströmformer, a new technique that could revolutionize NLP.
PAR Transformer is a model designed for language processing that has the ability to use fewer self-attention blocks and still generate accurate results. This technology uses a feed-forward block instead of the traditional self-attention block, which has resulted in a 63% reduction of these blocks in the architecture while maintaining high test accuracies. Read on to learn more about this innovative technology.
What is a Transformer?
A Transformer is a neural network architecture that was intr
PaLM or Pathways Language Model is a new approach to language modeling that enables faster and more efficient training of large neural networks. PaLM utilizes a standard Transformer model architecture along with several modifications to create a densely activated, autoregressive Transformer model with 540 billion parameters. It is trained on a massive dataset of 780 billion tokens, which makes it a powerful tool for a wide range of natural language processing tasks.
What is PaLM?
PaLM is a la
What is PEGASUS?
PEGASUS is a transformer-based model for abstractive summarization, which means that it is a tool that can create summaries of text by taking in the main ideas and presenting them in a shorter form. It is designed to be self-supervised, which means that it can learn without a lot of outside input, and it is specifically aimed at performing well on summarization-related tasks. It uses a pre-training objective called gap-sentences generation (GSG) to help it do this.
How does P
Performers are a type of Transformer architecture used for estimating regular full-rank-attention Transformers. These linear architecture models accurately estimate attention matrices without relying on priors such as sparsity or low-rankness, all while using only linear time and space complexity.
Understanding Performers
Transformers are neural networks that excel at processing and encoding sequential data such as in natural language processing (NLP) tasks. However, traditional Transformers
Understanding PermuteFormer: A Model with Linear Scaling on Long Sequences
PermuteFormer is a cutting-edge model based on Performer and relative position encoding, that enables linear scaling on long sequences. This model applies position-dependent transformation on queries and keys to encode positional information into the attention module. The transformation is designed so that the final output of self-attention is not affected by absolute positions of tokens.
What is PermuteFormer?
Permut
Overview of Primer: A Transformer-Based Architecture with Multi-DConv-Head-Attention
Primer is a new transformer-based architecture built using two improvements found through neural architecture search. The architecture uses the squared RELU activations and depthwise convolutions in the attention multi-head projections, resulting in a new multi-dconv-head-attention module. The module helps improve the accuracy and speed of natural language processing (NLP) models by combining the traditional tr
What is ProphetNet?
ProphetNet is a pre-training model that uses a specific type of prediction to learn and understand language. By predicting several words at once, ProphetNet can plan for future words and improve its overall language prediction abilities.
How does ProphetNet work?
ProphetNet uses a technique called future n-gram prediction to predict the next n words in a sentence. This is done by looking at the context of the sentence so far and making an educated guess about what will co
RAG, short for Retriever-Augmented Generation is a language generation model that is a combination of pre-trained parametric and non-parametric memories. With RAG, users are presented with an efficient and comprehensive system for generating language content.
What is RAG?
RAG is a language generation model that can generate human-like text, even out of context, by combining a pre-trained seq2seq model, and a dense vector index of information from Wikipedia accessed through a pre-trained neura
RealFormer is a new type of Transformer-based language model that uses residual attention to improve its performance. It is capable of creating multiple direct paths, each for a different type of attention module, without adding any parameters or hyper-parameters to the existing architecture.
What is a Transformer-based model?
A Transformer is a type of neural network architecture that is used for natural language processing tasks, such as language translation and text classification. It was
Reformer is an architecture that has been developed to make transformer-based models more efficient. This model replaces dot-product attention with locality-sensitive hashing, making the process more efficient. The complexity is reduced from O($L^2$) to O($L\log L$), where $L$ is the length of the sequence. Furthermore, the use of reversible residual layers allows for the storage of activations only once in the training process instead of $N$ times, where $N$ is the number of layers.
What is a