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Library/ NLP & Transformers/ Module 5 · Project 3: Customer Chat Summariser (fine-tuning T5 for text generation)/ 5.6 Fine-tune the model

subtopic

Fine-tune FLAN-T5 with the Seq2Seq Trainer

20 min applied25 min research2 curated resources

Fine-tune FLAN-T5-base (about 250M parameters) on the training dialogues: learning rate about 3e-4 (T5 likes higher rates than BERT), 3 epochs, batch 8–16, evaluate ROUGE on validation each epoch with generation switched on. Use a GPU; mixed precision bf16 if available. Keep the best checkpoint.

Builds on: 3.5 Fine-tune the Transformer

Notes, key code, what to skip and 2 curated resources are inside the platform.Join the waitlist and we'll build you a roadmap that includes this topic only if your goal needs it.

Fine-tune FLAN-T5 with the Seq2Seq Trainer · 1x1labs.ai