DeeBERT: A Gamechanger for NLP
DeeBERT is a method for accelerating BERT inference, which has revolutionized the field of Natural Language Processing (NLP). Named after the famous Sesame Street character Bert, Bidirectional Encoder Representations from Transformers (BERT) is a powerful algorithm that has improved the performance of various NLP tasks.
To understand the significance of DeeBERT, let's first understand how BERT works. BERT is a deep neural network that is trained on massive amount
What is DeLighT?
DeLighT is a transformer architecture that aims to improve parameter efficiency by using DExTra, a light-weight transformation within each Transformer block, and block-wise scaling across blocks. This allows for more efficient use of single-headed attention and bottleneck FFN layers, and shallower and narrower DeLighT blocks near the input, and wider and deeper DeLighT blocks near the output.
What is a Transformer Architecture?
A transformer architecture is a type of neural
DistilBERT is an innovative machine learning tool designed to create smaller, faster, and more efficient models based on the architecture of BERT, a popular transformer model. The goal of DistilBERT is to reduce the size of the BERT model by 40%, allowing for faster and simpler machine learning. DistilBERT accomplishes this task through a process known as knowledge distillation, which uses a triple loss to combine language modeling, distillation, and cosine-distance losses.
What is DistilBERT?
Are you curious about Edge-augmented Graph Transformer (EGT)? This is a new framework that is designed to process graph-structured data, which is different from unstructured data such as text and images. Transformer neural networks have been used to process unstructured data, but their use for graphs has been limited. One of the reasons for this is the complexity of integrating structural information into the basic transformer framework. EGT provides a solution by introducing residual edge chann
What is ELECTRA? An Overview of the Transformer with a New Pre-training Approach
ELECTRA is a groundbreaking transformer model that uses a unique approach to pre-training. Transformer models are a type of neural network that can process variable-length sequences of data in parallel, making them particularly useful for natural language processing (NLP) tasks like text generation and classification. One big challenge in training such models is obtaining large quantities of high-quality labeled da
The Basics of Electric: A Cloze Model for Text Representation Learning
Electric is an advanced energy-based cloze model for representation learning over text, developed in the field of machine learning. It has a similar structure to the popular BERT, but with subtle differences in its architecture and functioning.
The primary purpose of Electric is to generate vector representations for text, and it uses the generative model methodology to achieve this goal. Specifically, it models $p\_{\text{
Introduction to ESACL
ESACL, which stands for Enhanced Seq2Seq Autoencoder via Contrastive Learning, is a type of denoising seq2seq autoencoder that has been designed for abstractive text summarization. It uses self-supervised contrastive learning along with several other sentence-level document augmentations to enhance its denoising ability.
What is Seq2Seq Autoencoder?
Autoencoder is a type of deep learning algorithm used for unsupervised learning tasks, in which an input dataset is used t
Introduction to ERNIE: An Overview
ERNIE is a transformer-based model that combines textual and knowledgeable encoders to integrate extra token-oriented knowledge information into textual information. It has become one of the most popular language models used in natural language processing (NLP) and is widely used in text classification, question answering, and other NLP applications. In this article, we will dive deeper into the details of ERNIE and how it works.
What is a transformer-based
Extended Transformer Construction, also known as ETC, is an enhanced version of the Transformer architecture that utilizes a new attention mechanism to extend the original in two main ways: (1) it allows for a larger input length, up to several thousands, and (2) it can process structured inputs as well as sequential ones.
What is ETC?
The Transformer architecture is a machine learning model used for natural language processing tasks such as translation and summarization. The original Transfo
What is Fastformer?
Fastformer is a new type of Transformer, a type of neural network commonly used in natural language processing tasks like language translation and text classification.
Transformers typically model the pairwise interactions between tokens, or individual units of text, to understand their relationships within a larger context. However, Fastformer uses a different approach called additive attention to model global contexts. This means that Fastformer considers the entire input
A Feedback Transformer is a type of sequential transformer that utilizes a feedback mechanism to expose all previous representations to all future representations. This unique architecture allows for recursive computation, building stronger representations by utilizing past representations.
What is a Feedback Transformer?
A Feedback Transformer is a type of neural network architecture that is used in natural language processing tasks, image recognition, and other artificial intelligence appli
Overview of Funnel Transformer
Funnel Transformer is a type of machine learning model designed to reduce the cost of computation while increasing model capacity for tasks such as pretraining. This is achieved by compressing the sequence of hidden states to a shorter one, saving the FLOPs, and re-investing them in constructing a deeper or wider model.
The proposed model maintains the same overall structure as Transformer, with interleaved self-attention and feed-forward sub-modules wrapped by r
GANformer: A Novel Visual Generative Modeling Technique
GANformer is a new way to generate realistic images using machine learning. It's a type of transformer that allows for long-range interactions across an image while maintaining linear computation efficiency. This means it can create high-resolution images quickly and easily.
What is a Transformer?
Before diving into GANformer, it's important to understand what a transformer is. It's a type of neural network used in machine learning for
GPT-Neo Overview: The AI Language Model You Need to Know About
Language models such as GPT-Neo are becoming increasingly popular thanks to their ability to understand, learn and generate human-like speech. GPT-Neo, in particular, is a model that has attracted a lot of attention in the Artificial Intelligence (AI) community due to its impressive performance.
What is GPT-Neo?
GPT-Neo stands for "Generative Pre-training Transformer - Neo". It is an open-source language model developed by Eleuth
Are you fascinated by how computers can understand and process human language? If you are, then you might be interested in the latest advancement in natural language processing technology called GPT.
What is GPT?
GPT stands for Generative Pre-trained Transformer. It is a type of neural network architecture that uses a transformer-based model for natural language processing tasks. With its advanced language processing capabilities, it is capable of understanding and generating human-like text.
Have you heard of I-BERT? If you're interested in natural language processing, it's a topic you should know about. I-BERT is a quantized version of BERT, a popular pre-trained language model. But what does that actually mean? Let's break it down.
What is BERT?
Before we dive into I-BERT, it's important to understand BERT. BERT stands for Bidirectional Encoder Representations from Transformers. It was introduced by Google in 2018 and quickly became popular in the field of natural language proc
What is IB-BERT?
IB-BERT stands for Inverted Bottleneck BERT, which is a variation of the popular Bidirectional Encoder Representations from Transformers (BERT) model. This variation uses an inverted bottleneck structure and is primarily used as a teacher network to train the MobileBERT models.
What is BERT?
BERT is a natural language processing model that uses a transformer-based architecture. It is pre-trained on large amounts of text data, allowing it to understand the nuances of human la
The Levenshtein Transformer: Enhancing Flexibility in Language Decoding
The Levenshtein Transformer (LevT) is a type of transformer that addresses the limitations of previous decoding models by introducing two basic operations—insertion and deletion. These operations make decoding more flexible, allowing for the revision, replacement, revocation, or deletion of any part of the generated text. LevT is trained using imitation learning, making it a highly effective model for language decoding.
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