The Back Story:
Transformer: Important to understand because Transformers are the Engine of the AI Model operation
What a Transformer is in the context of ChatGPT and Hugging Face's API:
Today we're going to talk about one of the most important components of ChatGPT and Hugging Face's API: the Transformer.
So, what is a Transformer? Simply put, it's a type of neural network architecture that's particularly well-suited for natural language processing tasks.
But why is it so important for ChatGPT and Hugging Face's API?
Well, let's dive into that.First, a little background. Natural language processing (NLP) is a field of computer science that deals with how computers interact with human language.
Natural Language Process is a really hard problem because human language is incredibly complex and nuanced.
We want our Language Models to be context sensitive (“nuanced”) and display emotional empathy with the human conversational partner.
For example, just think about all the different ways you can say "I love you" - there's the plain old "I love you," but then there's also "I totally adore you,"
"You're the best thing since sliced bread," and countless other variations.
This complexity makes it really difficult for computers to understand and process human language.
Traditional rule-based approaches to NLP rely on hand-coded rules to try to capture all the different ways humans communicate, but these rules quickly become overwhelmed by the sheer number of possible combinations.
That's where deep learning comes in. Deep learning models are trained on vast amounts of data to learn patterns and relationships that would be impossible to capture with hand-coded rules.
And within deep learning, the Transformer is a particularly powerful architecture for NLP tasks.
The Transformer was introduced in a research paper by Vaswani et al. in 2017 and has since become one of the most widely used models architectures in NLP.
So what makes it so special?
Well, first of all, the Transformer doesn't use any recurrence or convolution.
Some topics we will develop for the project include the operation of ANNs and GANs.
Artificial Neural Networks.
CNN: Convolutional Neural Networks.
GANs: Adversial Networks: In way of doing things: You have 2 AI agents: Remember the demonstration of Susan’s Perfect Birthday Party:
That might sound a bit technical, but basically, it means that the model processes input sequences of tokens (e.g. words or characters) in parallel, rather than sequentially.
This allows it to handle [Long Conversational memories] long-range dependencies much more effectively than previous architectures.
In other words, the Transformer can "see" the entire input sequence at once, rather than having to process it one step at a time: extends the range and span of the conversational memory.
This allows it to capture complex contextual relationships between tokens, which is essential for tasks like machine translation, question answering, and of course, chatbots!
So how does this relate to ChatGPT and Hugging Face's API?
Well, both of them rely heavily on Transformers to power their natural language processing capabilities.
In fact, ChatGPT uses a variant of the Transformer called the BERT (Bidirectional Encoder Representations from Transformers) architecture to generate text.
BERT is a pre-trained Transformer model that's been fine-tuned on a massive corpus of text data to learn high-level semantic (=meaning) and syntactic (syntax means proper grammar formulation) features of language.
When you ask ChatGPT a question or give it a prompt, it uses the BERT transformer to generate a response that's not just a random collection of words, but actually makes sense (it context nucanced) in the context of the conversation.
Similarly, Hugging Face's API makes available to us a variety of Transformer-based models to provide a range of NLP services, including text classification, sentiment analysis, named entity recognition, and more.
HF’s models are also pre-trained on large datasets and can be fine-tuned (hyper parameter optimization) for specific tasks to achieve state-of-the-art results.
2 things we will get from the HuggingSpaces Lab APIs:
n Access to Language Models (Chat GPT, Claude by Anthropic, Baby Lllama)
n Access to transformers