Training: Transformer
Level
IntermediateDuration
16h / 2 daysDate
Individually arrangedPrice
Individually arrangedTraining: Transformer
The Transformer Models Training is an intensive two-day course focusing on the practical application of state-of-the-art NLP models, such as BERT and GPT, using Hugging Face and TensorFlow Hub. The program is designed so that 80% of the time is dedicated to hands-on workshops and 20% to theory. Participants will gain the essential skills to implement and fine-tune advanced transformer models across various NLP applications.
What will you learn?
- How to install and configure Hugging Face and TensorFlow Hub for working with transformer models
- How to apply transformer models to text classification, named entity recognition (NER), and text generation
- How to use and fine-tune pretrained transformer models such as BERT and GPT
- How to optimize model performance and deploy models into production environments
Prerequisites
- Basic knowledge of Python programming
- Experience with cloud services is an advantage
- Basic knowledge of machine learning
Who is this training for?
Developers and data engineers who want to expand their skills with the latest NLP techniques-
IT specialists who want to use transformer models for language processing automation
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Data scientists and analysts looking to process and analyze text with advanced models
Training Program
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Day 1: Introduction to Transformer Models and Hugging Face Basics
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Fundamentals of transformer models
- Introduction to transformer architecture: history and evolution
- Key components and core principles
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Overview of Hugging Face and TensorFlow Hub
- Installation and configuration of libraries
- Survey of available models and their applications
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Basic operations with transformer models
- Tokenization and text preprocessing
- Loading and using pretrained models
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Implementing transformers in text processing tasks
- Hands-on exercises: loading and testing models
- Analyzing results and basic optimization
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Day 2: Advanced Techniques and Practical Applications
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Training and fine-tuning transformer models
- Techniques for training and fine-tuning pretrained models
- Using custom datasets for training
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Applications of transformer models
- Text classification
- Named Entity Recognition (NER)
- Text generation and machine translation
- Sentiment analysis
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Fine-tuning and applications
- Implementing fine-tuning on real-world datasets
- Building projects with BERT and GPT
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Model optimization and deployment
- Techniques for optimizing model performance
- Deploying models in production environments
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