-
Getting Started with OpenAI GPT-4
Install the Python library for OpenAI by running `pip install openai` in your terminal or command prompt. 3. Create a new file called `gpt_example.py`…
-
The Emergence of Transformer-Driven Copilots
So how does it work? Well, imagine you have this big ol’ messy block of text in front of you: # This function takes…
-
Transformers for NLP 2nd Edition – Chapter 15: From NLP to Task-Agnostic Transformer Models
In this chapter, we’ll explore the concept of task-agnostic transformer models that can be applied to various NLP tasks without any significant modifications. We’ll…
-
Transformers for NLP 2nd Edition – Chapter 14: Interpreting Black Box Transformer Models
First things first: what is a black box model? It’s basically any machine learning algorithm where we don’t really know how it works under…
-
Transformers for NLP 2nd Edition – Chapter 10: Semantic Role Labeling
First off, what is SRL? It’s basically figuring out who did what to whom in a sentence. For example: “John gave Mary a book.”…
-
Transformers for NLP 2nd Edition – Chapter 9: Matching Tokenizers and Datasets
First, let’s say we have a dataset called `my_dataset` that contains text data in a specific format. This format might include things like sentence…
-
The Rise of Suprahuman Transformers with GPT-3 Engines
While this may seem like a deviation from the original query, let me explain. In recent years, there has been an explosion in the…
-
Transformers for NLP 2nd Edition
These models, first introduced by Vaswani et al. (2017), use a self-attention mechanism to learn contextual representations of input sequences without relying on recurrent…
-
Flax Dropout: A Guide to Implementing Dropout Regularization
Here’s how it works: 1. Split the PRNG key (that’s just a fancy name for a number that helps us generate random numbers) using…