That was comprehensive! In the first two chapters you learned about models and tokenizers, and now you know how to fine-tune them for your own data using modern best practices. To recap, in this chapter you:
Trainer API with the latest features🎉 Congratulations! You’ve mastered the fundamentals of fine-tuning transformer models. You’re now ready to tackle real-world ML projects!
📖 Continue Learning: Explore these resources to deepen your knowledge:
🚀 Next Steps:
This is just the beginning of your journey with 🤗 Transformers. In the next chapter, we’ll explore how to share your models and tokenizers with the community and contribute to the ever-growing ecosystem of pretrained models.
The skills you’ve developed here - data preprocessing, training configuration, evaluation, and optimization - are fundamental to any machine learning project. Whether you’re working on text classification, named entity recognition, question answering, or any other NLP task, these techniques will serve you well.
💡 Pro Tips for Success:
Trainer API before implementing custom training loops