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Fine-Tuning T5 on Question Answer dataset using Huggingface Transformer & PyTorch
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📝 In this video, we explore Text-to-Text Transfer Transformers, or T5 for short. T5 takes NLP tasks and converts them into a text-to-text format, making it incredibly versatile. Whether it's text classification, language translation, or text summarization, T5 can handle it all without modifying its architecture.
⚠️Note: Due to the model's size, we recommend using Google Colab or a GPU with 10GB+ VRAM.
Here's what we cover in the video:
* Importing libraries for machine learning, creating a dataset pipeline, and using transformers for a pre-trained model and tokenizer.
* Loading the CoQA dataset into memory and preparing the data for training.
* Setting up the T5 model, optimizer, and defining parameters.
* Creating a data preprocessing pipeline and splitting the dataset for training and validation.
*Training the model, monitoring training and validation loss, and saving the model and tokenizer.
* Evaluating the model using the BLEU score, a metric for generated sentences compared to reference sentences.
* Demonstrating the model's performance on a sample from the dataset.
We're excited to continue exploring transformer models, fine-tuning, creating new models, and embarking on creative projects in this series. Drop your ideas for upcoming videos in the comments, and stay tuned for more exciting content.
Facing issue? Discuss with me on social media
Thank you ❤️ for watching and joining us on this Transformers journey!
Credits:
Chapters:
0:00 Overview
2:04 Pre-requisites
2:12 Dataset
2:21 Dataset Overview
2:45 Coding
8:14 Wrapping up
⚠️Note: Due to the model's size, we recommend using Google Colab or a GPU with 10GB+ VRAM.
Here's what we cover in the video:
* Importing libraries for machine learning, creating a dataset pipeline, and using transformers for a pre-trained model and tokenizer.
* Loading the CoQA dataset into memory and preparing the data for training.
* Setting up the T5 model, optimizer, and defining parameters.
* Creating a data preprocessing pipeline and splitting the dataset for training and validation.
*Training the model, monitoring training and validation loss, and saving the model and tokenizer.
* Evaluating the model using the BLEU score, a metric for generated sentences compared to reference sentences.
* Demonstrating the model's performance on a sample from the dataset.
We're excited to continue exploring transformer models, fine-tuning, creating new models, and embarking on creative projects in this series. Drop your ideas for upcoming videos in the comments, and stay tuned for more exciting content.
Facing issue? Discuss with me on social media
Thank you ❤️ for watching and joining us on this Transformers journey!
Credits:
Chapters:
0:00 Overview
2:04 Pre-requisites
2:12 Dataset
2:21 Dataset Overview
2:45 Coding
8:14 Wrapping up
0:08:35
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