Introduction

BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) are both models for natural language processing (NLP) developed by Google and OpenAI respectively. The main difference between the two models lies in their approach to processing text.

BERT is a bidirectional model, which means it processes text in both directions (from left to right and from right to left) to capture the context of the words. It is a transformer-based model that is pre-trained on a large corpus of text using two unsupervised learning tasks:

1. masked language modeling
   • A generative task
   • For example, consider the sentence: “The cat [MASK] on the mat”. If the word “sat” is randomly replaced with the mask token, the input to the model will be “The cat [MASK] on the mat”. The model then generates a probability distribution over the vocabulary and selects the most likely word to fill in the masked position based on the context provided by the other words in the sentence.
2. next sentence prediction
   • A classification task
   • Predict whether a given pair of sentences appear next to each other in the original document or not. The model takes two input sentences (A and B) and predicts whether sentence B is the next sentence that follows sentence A in the original document or not

BERT is designed to be fine-tuned on specific downstream tasks, such as question answering and other classification tasks.

GPT (Generative Pre-trained Transformer) is a unidirectional model that uses a transformer architecture to generate text based on a given input.

Unlike BERT, GPT is trained to predict the next word in a sequence of text. This makes it well-suited for tasks that involve generating natural language, such as language translation and text completion.

GPT-2 and GPT-3 are the successors of the GPT model. GPT-2 was released in 2019 and is a larger and more powerful version of the original GPT model. GPT-2 has 1.5 billion parameters, which makes it one of the largest NLP models at the time of its release. GPT-3, released in 2020, is an even larger model with 175 billion parameters. GPT-3 is designed to be able to generate human-like text, and has been used for a wide range of tasks, from language translation to chatbots and content creation.

GPT-J is a variant of GPT-3 created by EleutherAI, a group of independent researchers. GPT-J is similar to GPT-3, but it is a smaller model with 6 billion parameters that can be run on standard GPUs, unlike GPT-3 which requires specialized hardware.

GPT-J has been released as an open-source model, which means anyone can use it for free. The following repos are worth checking out:

• https://github.com/kingoflolz/mesh-transformer-jax
• https://github.com/TheProtaganist/gpt-j
• https://github.com/machaao/gpt-j-chatbot

Fine-tuning

Fine-tuning BERT involves adding task-specific layers to the pre-trained model and training the model on a task-specific dataset. In contrast, fine-tuning GPT involves using the pre-trained model as a language model and generating text for a specific task.

GPT models are better suited for tasks that require generating text, while BERT is more suitable for tasks that require understanding the meaning of text.

BERT can be fine-tuned for a wide range of tasks, including both classification and sequence labeling tasks, while GPT is primarily used for language generation tasks.

when fine-tuning GPT for text classification, you can give it a classifier head in the same way you do with BERT.

In fact, using a classifier head is a common approach for fine-tuning GPT for classification tasks. The classifier head is typically added on top of the GPT model’s output layer, which consists of a sequence of vectors that represent the probability distribution over the vocabulary of the next word in a given input sequence.

My Code

I have tried GPT-J here.