How does the architecture of GPT-2 differ from the standard encoder/decoder transformer model?

GPT-2 consists of solely stacked decoder blocks from the transformer architecture. In the standard transformer architecture, the decoder is fed a word embedding concatenated with a context vector, both generated by the encoder. In GPT-2 the context vector is zero-initialized for the first word embedding (presumably?).

Does GPT use encoder?

In GPT there is no Encoder, therefore I assume its blocks only have one attention mechanism. That’s the main difference I found. At the same time, since GPT is used to generate language, its blocks must be masked, so that Self-Attention can only attend previous tokens.

What is the difference between GPT and BERT?

BERT and GPT are transformer-based architecture while ELMo is Bi-LSTM Language model. BERT is purely Bi-directional, GPT is unidirectional and ELMo is semi-bidirectional. GPT is trained on the BooksCorpus (800M words); BERT is trained on the BooksCorpus (800M words) and Wikipedia (2,500M words).

Is GPT transformer-based?

GPT-n models are based on this Transformer-based deep learning neural network architecture. There are a number of NLP systems capable of processing, mining, organizing, connecting, contrasting, understanding and generating answers to questions.

Is ELMo a Transformer model?

ELMo vs. BERT -> all pre-training model architectures. BERT uses a bidirectional Transformer vs. ELMo uses the concatenation of independently trained left-to-right and right-to-left LSTM to generate features for downstream task.

Is GPT-2 a transformer?

Generative Pre-trained Transformer 2 (GPT-2) is an open-source artificial intelligence created by OpenAI in February 2019. The GPT architecture implements a deep neural network, specifically a transformer model, which uses attention in place of previous recurrence- and convolution-based architectures.

Is the GPT-2 similar to the decoder only Transformer?

This year, we saw a dazzling application of machine learning. The OpenAI GPT-2 exhibited impressive ability of writing coherent and passionate essays that exceed what we anticipated current language models are able to produce. The GPT-2 wasn’t a particularly novel architecture – it’s architecture is very similar to the decoder-only transformer.

How big is the dataset for GPT-2?

GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset [1] of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text.

What is the difference between GPT and GPT-2?

GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data. GPT-2 is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left.

What’s the difference between the GPT2 and Bert?

It turns out that’s one of the main distinguishing factors between the different GPT2 model sizes: A robot may not injure a human being or, through inaction, allow a human being to come to harm. The GPT-2 is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks.

How does the architecture of GPT 2 differ from the standard encoder/decoder transformer model?

GPT-2 consists of solely stacked decoder blocks from the transformer architecture. In the standard transformer architecture, the decoder is fed a word embedding concatenated with a context vector, both generated by the encoder. In GPT-2 the context vector is zero-initialized for the first word embedding (presumably?).

Does GPT have encoder?

In GPT there is no Encoder, therefore I assume its blocks only have one attention mechanism. That’s the main difference I found. At the same time, since GPT is used to generate language, its blocks must be masked, so that Self-Attention can only attend previous tokens.

How does a transformer decoder work?

On a high level, the encoder maps an input sequence into an abstract continuous representation that holds all the learned information of that input. The decoder then takes that continuous representation and step by step generates a single output while also being fed the previous output.

Is the GPT-2 similar to the decoder only Transformer?

This year, we saw a dazzling application of machine learning. The OpenAI GPT-2 exhibited impressive ability of writing coherent and passionate essays that exceed what we anticipated current language models are able to produce. The GPT-2 wasn’t a particularly novel architecture – it’s architecture is very similar to the decoder-only transformer.

What’s the difference between the GPT2 and Bert?

It turns out that’s one of the main distinguishing factors between the different GPT2 model sizes: A robot may not injure a human being or, through inaction, allow a human being to come to harm. The GPT-2 is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks.

Why are encoder and decoder used in the illustrated transformer?

As we’ve seen in The Illustrated Transformer, the original transformer model is made up of an encoder and decoder – each is a stack of what we can call transformer blocks. That architecture was appropriate because the model tackled machine translation – a problem where encoder-decoder architectures have been successful in the past.

How many states are there in GPT-2 model?

Our model largely follows the original transformer work. We trained a 12-layer decoder-only transformer with masked self-attention heads (768 dimensional states and 12 attention heads). For the position-wise feed-forward networks, we used 3072 dimensional inner states.