Generative Python Transformer p.3 - Preprocessing Dataset

TL;DR

This video explains the process of building a data set for training machine learning models using generative Python transformers like GPT and BERT.

Transcript

what is going on everybody and welcome to another generative python transformers video in this video we're gonna be doing is building our data set now with all machine learning when you're building a data set uh to train a model you have to be thinking okay what is my input what is my output with the hugging face transformers library that we're goi... Read More

Key Insights

• 🫠 GPT and BERT models have different approaches to generating data, with GPT models being more focused on generating new data and BERT models capable of reading and generating.
• 😫 The process of building a data set involves preparing the input and output data, considering the maximum length of tokens, and tokenizing the text.
• 🫥 Fixing new line characters, splitting the data into smaller samples, and writing them to files are important steps in building the data set.
• 😫 The size of the data set and the length of the samples can impact the performance of the machine learning models.

Questions & Answers

Q: What is the key difference between GPT and BERT models when it comes to generating data?

The main difference is that GPT models generate data by only looking at previous tokens, while BERT models look at both previous and future tokens, allowing them to fill in the gaps.

Q: Can both GPT and BERT models be used for tasks like summarization and question answering?

Yes, both models can be used for tasks like summarization and question answering. GPT models are better at generating new data, while BERT models can handle tasks that involve both reading and generating.

Q: How can the maximum length of tokens affect the data set for training the models?

The maximum length of tokens determines the length of the samples in the data set. It is important to ensure that the samples are as close as possible to the maximum length to optimize the model's performance.

Q: What is the purpose of tokenizing the data set?

Tokenizing the data set involves converting the text into numerical representations that the model can understand. This is necessary for the machine learning model to process and generate data effectively.

Summary & Key Takeaways

• The video discusses the differences between GPT (generative pre-trained transformer) and BERT (bidirectional encoder representation transformer) models in terms of their approach to tokenizing and generating data.

• The speaker explains the process of building a data set for training these models, emphasizing the need to think about the input and output data.

• The video covers the steps involved in preparing the data set, including reading and processing files, fixing new line characters, splitting the data sets, and writing them to files.