fbpx
until LLM Bootcamp: In-Person (Seattle) and Online Learn more

transformer models

Natural language processing (NLP) and large language models (LLMs) have been revolutionized with the introduction of transformer models. These refer to a type of neural network architecture that excels at tasks involving sequences.

While we have talked about the details of a typical transformer architecture, in this blog we will explore the different types of the models.

How to categorize transformer models?

Transformers ensure the efficiency of LLMs in processing information. Their role is critical to ensure improved accuracy, faster training on data, and wider applicability. Hence, it is important to understand the different model types available to choose the right one for your needs.

 

Large language model bootcamp

However, before we delve into the many types of transformer models, it is important to understand the basis of their classification.

Classification by transformer architecture

The most fundamental categorization of transformer models is done based on their architecture. The variations are designed to perform specific tasks or cater to the limitations of the base architecture. The very common model types under this category include encoder-only, decoder-only, and encoder-decoder transformers.

Categorization based on pre-training approaches

While architecture is a basic component of consideration, the training techniques are equally crucial components for transformers. Pre-training approaches refer to the techniques used to train a transformer on a general dataset before finetuning it to perform specific tasks.

Some common approaches that define classification under this category include Masked Language Models (MLMs), autoregressive models, and conditional transformers.

This presents a general outlook on classifying transformer models. While we now know the types present under each broader category, let’s dig deeper into each transformer model type.

 

Read in detail about transformer architectures

 

Architecture-based classification

 

Architecture of transformer models
The general architecture of transformer models

 

Encoder-only transformer

As the name suggests, this architectural type uses only the encoder part of the transformer, focusing on encoding the input sequence. For this model type, understanding the input sequence is crucial while generating an output sequence is not required.

Some common applications of an encoder-only transformer include:

Text classification

It is focused on classifying the input data based on defined parameters. It is often used in email spam filters to categorize incoming emails. The transformer model can also train over the patterns for effective filtration of unwanted messages.

Sentimental analysis

This feature makes it an appropriate choice for social media companies to analyze customer feedback and their emotion toward a service or product. It provides useful data insights, leading to the creation of effective strategies to enhance customer satisfaction.

Anomaly detection

It is particularly useful for finance companies. The analysis of financial transactions allows the timely detection of anomalies. Hence, possible fraudulent activities can be addressed promptly.

Other uses of an encoder-only transformer include question-answering, speech recognition, and image captioning.

Decoder-only transformer

It is a less common type of transformer model that uses only the decoder component to generate text sequences based on input prompts. The self-attention mechanism allows the model to focus on previously generated outputs in the sequence, enabling it to refine the output and create more contextually aware results.

Some common uses of decoder-only transformers include:

Text summarization

It can iteratively generate textual summaries of the input, focusing on including the important aspects of information.

Text generation

It builds on a provided prompt to generate relevant textual outputs. The results cover a diverse range of content types, like poems, codes, and snippets. It is capable of iterating the process to create connected and improved responses.

Chatbots

It is useful to handle conversational interactions via chatbots. The decoder can also consider previous conversations to formulate relevant responses.

 

Explore the role of attention mechanism in transformers

 

Encoder-decoder Transformer

This is a classic architectural type of transformer, efficiently handling sequence-to-sequence tasks, where you need to transform one type of sequence (like text) into another (like a translation or summary). An encoder processes the input sequence while a decoder is used to generate an output sequence.

Some common uses of an encoder-decoder transformer include:

Machine translation

Since the sequence is important at both the input and output, it makes this transformer model a useful tool for translation. It also considers contextual references and relationships between words in both languages.

Text summarization

While this use overlaps with that of a decoder-only transformer, text summarization differs from an encoder-decoder transformer due to its focus on the input sequence. It enables the creation of summaries that focus on relevant aspects of the text highlighted in an input prompt.

Question-answering

It is important to understand the question before providing a relevant answer. An encoder-decoder transformer allows this focus on both ends of the communication, ensuring each question is understood and answered appropriately.

This concludes our exploration of architecture-based transformer models. Let’s explore the classification from the lens of pre-training approaches.

Categorization based on pre-training approaches

While the architectural differences provide a basis for transformer types, the models can be further classified based on their techniques of pre-training.

Let’s explore the various transformer models segregated based on pre-training approaches.

Masked Language Models (MLMs)

Models with this pre-training approach are usually encoder-only in architecture. They are trained to predict a masked word in a sentence based on the contextual information of the surrounding words. The training enables these model types to become efficient in understanding language relationships.

Some common MLM applications are:

Boosting downstream NLP tasks

MLMs train on massive datasets, enabling the models to develop a strong understanding of language context and relationships between words. This knowledge enables MLM models to contribute and excel in diverse NLP applications.

General-purpose NLP tool

The enhanced learning, knowledge, and adaptability of MLMs make them a part of multiple NLP applications. Developers leverage this versatility of pre-trained MLMs to build a basis for different NLP tools.

Efficient NLP development

The pre-trained foundation of MLMs reduces the time and resources needed for the deployment of NLP applications. It promotes innovation, faster development, and efficiency.

 

Explore a hands-on curriculum that helps you build custom LLM applications!

 

Autoregressive models

Typically built using a decoder-only architecture, this pre-training model is used to generate sequences iteratively. It can predict the next word based on the previous one in the text you have written. Some common uses of autoregressive models include:

Text generation

The iterative prediction from the model enables it to generate different text formats. From codes and poems to musical pieces, it can create all while iteratively refining the output as well.

Chatbots

The model can also be utilized in a conversational environment, creating engaging and contextually relevant responses,

Machine translation

While encoder-decoder models are commonly used for translation tasks, some languages with complex grammatical structures are supported by autoregressive models.

Conditional transformer

This transformer model incorporates the additional information of a condition along with the main input sequence. It enables the model to generate highly specific outputs based on particular conditions, ensuring more personalized results.

Some uses of conditional transformers include:

Machine translation with adaptation

The conditional aspect enables the model to set the target language as a condition. It ensures better adjustment of the model to the target language’s style and characteristics.

Summarization with constraints

Additional information allows the model to generate summaries of textual inputs based on particular conditions.

Speech recognition with constraints

With the consideration of additional factors like speaker ID or background noise, the recognition process enhances to produce improved results.

Future of transformer model types

While numerous transformer model variations are available, the ongoing research promises their further exploration and growth. Some major points of further development will focus on efficiency, specialization for various tasks, and integration of transformers with other AI techniques.

Transformers can also play a crucial role in the field of human-computer interaction with their enhanced capabilities. The growth of transformers will definitely impact the future of AI. However, it is important to understand the uses of each variation of a transformer model before you choose the one that fits your requirements.

March 23, 2024

Transformer models are a type of deep learning model that is used for natural language processing (NLP) tasks. They can learn long-range dependencies between words in a sentence, which makes them very powerful for tasks such as machine translation, text summarization, and question answering.

Transformer models work by first encoding the input sentence into a sequence of vectors. This encoding is done using a self-attention mechanism, which allows the model to learn the relationships between the words in the sentence.

Once the input sentence has been encoded, the model decodes it into a sequence of output tokens. This decoding is also done using a self-attention mechanism.

The attention mechanism is what allows transformer models to learn long-range dependencies between words in a sentence. The attention mechanism works by focusing on the most relevant words in the input sentence when decoding the output tokens.

Learn in detail about transformer models here:

Large language model bootcamp

Transformer models are very powerful, but they can be computationally expensive to train. However, they are constantly being improved, and they are becoming more efficient and powerful all the time.

History

The history of transformers in neural networks can be traced back to the early 1990s when Jürgen Schmidhuber proposed the first transformer model. This model was called the “fast weight controller” and it used a self-attention mechanism to learn the relationships between words in a sentence. However, the fast-weight controller was not very efficient, and it was not widely used.

In 2017, Vaswani et al. published the paper “Attention is All You Need”, which introduced a new transformer model that was much more efficient than the fast-weight controller. This new model, which is now simply called the “transformer”, quickly became state-of-the-art for a wide range of natural language efficient (NLP) tasks, including machine translation, text summarization, and question answering.

Learn more about NLP in this blog —-> Applications of Natural Language Processing

The transformer has been so successful because it can learn long-range dependencies between words in a sentence. This is essential for many NLP tasks, as it allows the model to understand the context of a word in a sentence. The transformer does this using a self-attention mechanism, which allows the model to focus on the most relevant words in a sentence when decoding the output tokens.

The transformer has had a major impact on the field of NLP. It is now the go-to approach for many NLP tasks, and it is constantly being improved. In the future, transformers are likely to be used to solve a wider range of NLP tasks, and they will become even more efficient and powerful.

Here are some of the key events in the history of transformers in neural networks:

  • 1990: Jürgen Schmidhuber proposes the first transformer model, the “fast weight controller”.
  • 2017: Vaswani et al. publish the paper “Attention is All You Need”, which introduces the transformer model.
  • 2018: Transformer models achieve state-of-the-art results on a wide range of NLP tasks, including machine translation, text summarization, and question answering.
  • 2019: Transformers are used to create large language models (LLMs) such as BERT and GPT-2.
  • 2020: LLMs are used to create even more powerful models such as GPT-3.

The history of transformers in neural networks is still being written. It is an exciting time to be in the field of NLP, as transformers are making it possible to solve previously intractable problems.

 

NLP transformer architecture

The transformer model is made up of two main components: an encoder and a decoder. The encoder takes the input sentence as input and produces a sequence of vectors. The decoder then takes these vectors as input and produces the output sentence.

transformer models
How a transfer model works

The encoder consists of a stack of self-attention layers. Each self-attention layer takes a sequence of vectors as input and produces a new sequence of vectors. The self-attention layer works by first computing a score for each pair of words in the input sequence. The score for a pair of words is a measure of how related the two words are. The self-attention layer then uses these scores to compute a weighted sum of the input vectors. The weighted sum is the output of the self-attention layer.

The decoder consists of a stack of self-attention layers and a recurrent neural network (RNN). The self-attention layers work the same way as in the encoder. The RNN takes the output of the self-attention layers as input and produces a sequence of output tokens. The output tokens are the words in the output sentence.

The attention mechanism is what allows the transformer model to learn long-range dependencies between words in a sentence. The attention mechanism works by focusing on the most relevant words in the input sentence when decoding the output tokens.

For example, let’s say we want to translate the sentence “I love you” from English to Spanish. The transformer model would first encode the sentence into a sequence of vectors. Then, the model would decode the vectors into a sequence of Spanish words. The attention mechanism would allow the model to focus on the words “I” and “you” in the English sentence when decoding the Spanish words “te amo”.

Transformer models are a powerful tool for NLP, and they are constantly being improved. They are now the go-to approach for many NLP tasks, and they are constantly being improved.

Learn More                  

Encoding and Decoding

Encoding and decoding are two key concepts in natural language processing (NLP). Encoding is the process of converting a sequence of words into a sequence of vectors. Decoding is the process of converting a sequence of vectors back into a sequence of words.

Encoding

The encoder in a transformer model takes a sequence of words as input and produces a sequence of vectors. The encoder consists of a stack of self-attention layers. Each self-attention layer takes a sequence of vectors as input and produces a new sequence of vectors. The self-attention layer works by first computing a score for each pair of words in the input sequence. The score for a pair of words is a measure of how related the two words are. The self-attention layer then uses these scores to compute a weighted sum of the input vectors. The weighted sum is the output of the self-attention layer.

For example, let’s say we have the sentence “I like you”. The encoder would first compute a score for each pair of words in the sentence. The score for the word “I” and the word “like” would be high, because these words are related. The score for the word “like” and the word “you” would also be high, for the same reason. The encoder would then use these scores to compute a weighted sum of the input vectors. The weighted sum would be a vector that represents the meaning of the sentence “I like you”.

Decoding

The decoder in a transformer model takes a sequence of vectors as input and produces a sequence of words. The decoder also consists of a stack of self-attention layers. The self-attention layers work the same way as in the encoder. The decoder also has an RNN, which takes the output of the self-attention layers as input and produces a sequence of output tokens. The output tokens are the words in the output sentence.

For example, let’s say we want to translate the sentence “I love you” from English to Spanish. The decoder would first take the vector that represents the meaning of the sentence “I love you” as input. Then, the decoder would use the self-attention layers to compute a weighted sum of the input vectors. The weighted sum would be a vector that represents the meaning of the sentence “I love you” in Spanish. The decoder would then use the RNN to produce a sequence of Spanish words. The output of the RNN would be the Spanish sentence “Te amo”

Encoder only models

Encoder-only models are a type of transformer model that only has an encoder. Encoder-only models are typically used for tasks like text classification, where the model only needs to understand the meaning of the input text.

For example, an encoder-only model could be used to classify a news article as either “positive” or “negative”. The encoder would first encode the article into a sequence of vectors. Then, the model would use a classifier to classify the article.

Encoder-only models are typically less powerful than full transformer models, but they are much faster and easier to train. This makes them a good choice for tasks where speed and efficiency are more important than accuracy.

Decoder only models

Decoder-only models are a type of transformer model that only has a decoder. Decoder-only models are typically used for tasks like machine translation, where the model needs to generate the output text.

For example, a decoder-only model could be used to translate a sentence from English to Spanish. The decoder would first take the English sentence as input. Then, the decoder would use the self-attention layers to compute a weighted sum of the input vectors. The weighted sum would be a vector that represents the meaning of the sentence in Spanish. The decoder would then use an RNN to produce a sequence of Spanish words. The output of the RNN would be the Spanish sentence.

Decoder-only models are typically less powerful than full transformer models, but they are much faster and easier to train. This makes them a good choice for tasks where speed and efficiency are more important than accuracy.

Here is a table that summarizes the differences between encoder-only models and decoder-only models:

Differences between a decoder-only and an encoder-only transformer model
Differences between a decoder-only and an encoder-only transformer model

What are transformer models built of

Transformer models are built of the following components:

  • Embedding layer: The embedding layer converts the input text into a sequence of vectors. The vectors represent the meaning of the words in the text.
  • Self-attention layers: The self-attention layers allow the model to learn long-range dependencies between words in a sentence. The self-attention layers work by computing a score for each pair of words in the sentence. The score for a pair of words is a measure of how related the two words are. The self-attention layers then use these scores to compute a weighted sum of the input vectors. The weighted sum is the output of the self-attention layer.
  • Positional encoding: The positional encoding layer adds information about the position of each word in the sentence. This is important for learning long-range dependencies, as it allows the model to know which words are close to each other in the sentence.
  • Decoder: The decoder takes the output of the self-attention layers as input and produces a sequence of output tokens. The output tokens are the words in the output sentence.

Transformer models are also typically trained with the following techniques:

  • Masked language modeling: Masked language modeling is a technique used to train transformer models to predict the missing words in a sentence. This helps the model to learn to attend to the most relevant words in a sentence.
  • Attention masking: Attention masking is a technique used to prevent the model from attending to future words in a sentence. This is important for preventing the model from learning circular dependencies.
  • Gradient clipping: Gradient clipping is a technique used to prevent the gradients from becoming too large. This helps to stabilize the training process and prevent the model from overfitting.

Attention layers are a type of neural network layer that allows the model to learn long-range dependencies between words in a sentence. The attention layer works by computing a score for each pair of words in the sentence. The score for a pair of words is a measure of how related the two words are. The attention layer then uses these scores to compute a weighted sum of the input vectors. The weighted sum is the output of the attention layer.

The input to the attention layer is a sequence of vectors. The output of the attention layer is a weighted sum of the input vectors. The weights are computed using the scores for each pair of words in the sentence.

The attention layer can learn long-range dependencies because it allows the model to attend to any word in the sentence, regardless of its position. This is in contrast to recurrent neural networks (RNNs), which can only attend to words that are close to the current word.

Transformer architecture is a neural network architecture that is based on attention layers. Transformer models are typically made up of an encoder and a decoder. The encoder takes the input text as input and produces a sequence of vectors. The decoder takes the output of the encoder as input and produces a sequence of output tokens.

The encoder consists of a stack of self-attention layers. The decoder also consists of a stack of self-attention layers. The self-attention layers in the decoder can attend to both the input text and the output text. This allows the decoder to generate the output text in a way that is consistent with the input text.

Transformer models are typically trained with the masked language modeling technique. Masked language modeling is a technique used to train transformer models to predict the missing words in a sentence. This helps the model to learn to attend to the most relevant words in a sentence.

Tackle transformer model challenges

Transformer models are a powerful tool for natural language processing (NLP) tasks, but they can be challenging to train and deploy. Here are some of the challenges of transformer models and how to tackle them:
  • Computational complexity: Transformer models are very computationally expensive to train and deploy. This is because they require a large number of parameters and a lot of data. To tackle this challenge, researchers are developing new techniques to make transformer models more efficient.
  • Data requirements: Transformer models require a large amount of data to train. This is because they need to learn the relationships between words in a sentence. To tackle this challenge, researchers are developing new techniques to pre-train transformer models on large datasets.
  • Interpretability: Transformer models are not as interpretable as other machine learning models, such as decision trees and logistic regression. This makes it difficult to understand why the model makes the predictions that it does. To tackle this challenge, researchers are developing new techniques to make transformer models more interpretable.

Here are some specific techniques that have been developed to tackle the challenges of transformer models:

  • Knowledge distillation: Knowledge distillation is a technique that can be used to train a smaller, more efficient transformer model by distilling the knowledge from a larger, more complex transformer model.
  • Data augmentation: Data augmentation is a technique that can be used to increase the size of a dataset by creating new data points from existing data points. This can help to improve the performance of transformer models on small datasets.
  • Attention masking: Attention masking is a technique that can be used to prevent the transformer model from attending to future words in a sentence. This helps to prevent the model from learning circular dependencies.
  • Gradient clipping: Gradient clipping is a technique that can be used to prevent the gradients from becoming too large. This helps to stabilize the training process and prevent the model from overfitting.
August 16, 2023

The buzz surrounding large language models is wreaking havoc and for all the good reason! The game-changing technological marvels have got everyone talking and have to be topping the charts in 2023.

What are large language models?

A large language model (LLM) is a machine learning model capable of performing various natural language processing (NLP) tasks, including text generation, text classification, question answering in conversational settings, and language translation. The term “large” in this context refers to the model’s extensive set of parameters, which are the values it can autonomously adjust during the learning process. Some highly successful LLMs possess hundreds of billions of these parameters.

LLMs undergo training with vast amounts of data and utilize self-supervised learning to predict the next token in a sentence based on its context. They can be used to perform a variety of tasks, including: 

  • Natural language understanding: LLMs can understand the meaning of text and code, and can answer questions about it. 
  • Natural language generation: LLMs can generate text that is similar to human-written text. 
  • Translation: LLMs can translate text from one language to another. 
  • Summarization: LLMs can summarize text into a shorter, more concise version. 
  • Question answering: LLMs can answer questions about text. 
  • Code generation: LLMs can generate code, such as Python or Java code. 
Understanding Large Language Models
Understanding Large Language Models

Best examples of large language models

Let’s explore a range of noteworthy large language models that have made waves in the field:

1. BERT (Bidirectional Encoder Representations from Transformers)

BERT is a revolutionary transformer-based model that underwent extensive pre-training on vast amounts of text data. Its prowess lies in natural language processing (NLP) tasks like sentiment analysis, question-answering, and text classification.

2. GPT-3 (Generative Pretrained Transformer 3)

OpenAI’s flagship creation, GPT-3, stands tall as one of the most advanced AI models worldwide. Trained on massive text datasets, it boasts an exceptional ability to generate human-like responses across diverse topics, retaining an extensive conversational memory.

3. XLM-R (Cross-lingual Language Model – RoBERTa)

Facebook AI Research’s transformer-based behemoth, XLM-R, takes multilingual capabilities to new heights. It undergoes pre-training on colossal multilingual text corpora and excels in NLP tasks such as text classification, machine translation, and question-answering.

4. Whisper

OpenAI’s Whisper enters the scene as a powerful automatic speech recognition (ASR) system. Its training on a staggering 680,000 hours of diverse and multilingual data empowers it to transcribe speech in multiple languages and perform English translations with improved accuracy, even amidst accents, background noise, and technical jargon.

5. T5 (Text-to-Text Transfer Transformer)

Developed by Google Research, T5 proves its mettle as a versatile large language model. It tackles various NLP tasks like text generation, summarization, and translation through the magic of transfer learning, adapting its capabilities to different contexts.

6. M2M-100 (Multilingual Machine Translation 100):

A marvel in multilingual translation, M2M-100 obliterates language barriers. With training encompassing an astonishing 2,200 language directions, this model achieves remarkable translation accuracy across 100 languages without relying on English-centric data.

7. MPNet (Masked and Permuted Language Modeling Pre-training Network):

MPNet introduces a novel approach to language model pre-training. By combining masked language modeling (MLM) and permuted language modeling (PLM), it takes token dependency into account, building upon BERT’s classification methodologies.

As we assess these models’ performance and capabilities, it’s crucial to acknowledge their specificity for particular NLP tasks. The choice of the optimal model depends on the task at hand. Large language models exhibit impressive proficiency across various NLP domains and hold immense potential for transforming customer engagement, operational efficiency, and beyond.  

What are some of the benefits of LLMs? 

LLMs have a number of benefits over traditional AI methods. They are able to understand the meaning of text and code in a much more sophisticated way. This allows them to perform tasks that would be difficult or impossible for traditional AI methods. LLMs are also able to generate text that is very similar to human-written text. This makes them ideal for applications such as chatbots and translation tools.   

Applications for large language models

1. Streamlining language generation in IT:

Discover how generative AI can elevate IT teams by optimizing processes and delivering innovative solutions. Witness its potential in:

  • Recommending and creating knowledge articles and forms
  • Updating and editing knowledge repositories
  • Real-time translation of knowledge articles, forms, and employee communications
  • Crafting product documentation effortlessly

2. Boosting efficiency with language summarization

Explore how generative AI can revolutionize IT support teams, automating tasks and expediting solutions. Experience its benefits in:

  • Extracting topics, symptoms, and sentiments from IT tickets
  • Clustering IT tickets based on relevant topics
  • Generating narratives from analytics
  • Summarizing IT ticket solutions and lengthy threads
  • Condensing phone support transcripts and highlighting critical solutions

3. Unleashing code and data generation potential

Witness the transformative power of generative AI in IT infrastructure and chatbot development, saving time by automating laborious tasks such as:

  • Suggesting conversation flows and follow-up patterns
  • Generating training data for conversational AI systems
  • Testing knowledge articles and forms for relevance
  • Assisting in code generation for repetitive snippets from online sources


Future possibilities of LLMs

The future possibilities of LLMs are very exciting. They have the potential to revolutionize the way we interact with computers. They could be used to create new types of applications, such as chatbots that can understand and respond to natural language, or translation tools that can translate text with near-human accuracy. 

LLMs could also be used to improve our understanding of the world. They could be used to analyze large datasets of text and code and to identify patterns and trends that would be difficult or impossible to identify with traditional methods.

Wrapping up 

LLMs represent a highly potent and promising technology that presents numerous possibilities for various applications. While still in the development phase, these models have the capacity to fundamentally transform our interactions with computers.

Data Science Dojo specializes in delivering a diverse array of services aimed at enabling organizations to harness the capabilities of Large Language Models. Leveraging our extensive expertise and experience, we provide customized solutions that perfectly align with your specific needs and goals.

Check out —>  Large Language Models Bootcamp by Data Science Dojo

Register today

June 20, 2023

Related Topics

Statistics
Resources
Programming
Machine Learning
LLM
Generative AI
Data Visualization
Data Security
Data Science
Data Engineering
Data Analytics
Computer Vision
Career
Artificial Intelligence