2024 Fine Tuning LLM with Hugging Face Transformers for NLP
Published 6/2024
Duration: 12h9m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 5.55 GB
Genre: eLearning | Language: English

Master Transformer Fine-Tuning for NLP

What you'll learn
Understand transformers and their role in NLP.
Gain hands-on experience with Hugging Face Transformers.
Learn about relevant datasets and evaluation metrics.
Fine-tune transformers for text classification, question answering, natural language inference, text summarization, and machine translation.
Understand the principles of transformer fine-tuning.
Apply transformer fine-tuning to real-world NLP problems.
Learn about different types of transformers, such as BERT, GPT-2, and T5.
Hands-on experience with the Hugging Face Transformers library

Requirements
Basic understanding of natural language processing (NLP)
Basic programming skills
Familiarity with machine learning concepts
Access to a computer with a GPU

Description
Section 1: Introduction to Transformers
In this introductory section, you will gain a comprehensive understanding of transformers and their role in natural language processing (NLP). You will delve into the transformer architecture, exploring its encoder-decoder structure, attention mechanism, and self-attention mechanism. You will also discover various types of transformers, such as BERT, GPT-2, and T5, and their unique characteristics.
Key takeaways:
Grasp the fundamentals of transformers and their impact on NLP
Understand the intricacies of the transformer architecture
Explore different types of transformers and their applications
Section 2: Relevant Tools for Transformer Fine-Tuning
Embrace the power of the Hugging Face Transformers library in this section. You will learn how to effectively utilize this library to work with pre-trained transformer models. You will discover how to load, fine-tune, and evaluate transformer models for various NLP tasks.
Key takeaways:
Master the Hugging Face Transformers library for transformer fine-tuning
Load, fine-tune, and evaluate transformer models with ease
Harness the capabilities of the Hugging Face Transformers library
Section 3: Fine-Tuning Transformers for NLP Tasks
Venture into the realm of fine-tuning transformers for various NLP tasks. You will explore techniques for fine-tuning transformers for text classification, question answering, natural language inference, text summarization, and machine translation. Gain hands-on experience with each task, mastering the art of transformer fine-tuning.
Key takeaways:
Fine-tune transformers for text classification, question answering, and more
Master the art of transformer fine-tuning for various NLP tasks
Gain hands-on experience with real-world NLP applications
Section 4: Basic Examples of LLM Fine-Tuning in NLP
Delve into practical examples of LLM fine-tuning in NLP. You will witness step-by-step demonstrations of fine-tuning transformers for sentiment analysis, question answering on SQuAD, natural language inference on MNLI, text summarization on CNN/Daily Mail, and machine translation on WMT14 English-German.
Key takeaways:
Witness real-world examples of LLM fine-tuning in NLP
Learn how to fine-tune transformers for specific NLP tasks
Apply LLM fine-tuning to practical NLP problems
Advanced Section: Advanced Techniques for Transformer Fine-Tuning
Elevate your transformer fine-tuning skills by exploring advanced techniques. You will delve into hyperparameter tuning, different fine-tuning strategies, and error analysis. Learn how to optimize your fine-tuning process for achieving state-of-the-art results.
Key takeaways:
Master advanced techniques for transformer fine-tuning
Optimize your fine-tuning process for peak performance
Achieve state-of-the-art results in NLP tasks
Who this course is for:
NLP practitioners: This course is designed for NLP practitioners who want to learn how to fine-tune pre-trained transformer models to achieve state-of-the-art results on a variety of NLP tasks.
Researchers: This course is also designed for researchers who are interested in exploring the potential of transformer fine-tuning for new NLP applications.
Students: This course is suitable for students who have taken an introductory NLP course and want to deepen their understanding of transformer models and their application to real-world NLP problems.
Developers: This course is beneficial for developers who want to incorporate transformer fine-tuning into their NLP applications.
Hobbyists: This course is accessible to hobbyists who are interested in learning about transformer fine-tuning and applying it to personal projects.

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