Why this matters
As an NLP Engineer, you regularly decide which transformer family to use and how to fine-tune it. Picking BERT vs GPT vs T5 impacts accuracy, latency, memory, and development time. Real tasks include:
- Classifying support tickets by intent (BERT-like encoder models shine).
- Drafting short replies or product descriptions (GPT-like decoder-only models shine).
- Summarizing long documents or translating content (T5-like encoder–decoder models shine).
- Building retrieval-augmented search (encoders for embeddings; optional GPT/T5 for generation).
Concept explained simply
Three big transformer model families dominate common NLP tasks:
- BERT: An encoder trained to understand text bidirectionally by predicting masked tokens. Great for understanding tasks like classification and semantic search.
- GPT: A decoder-only model trained to predict the next token. Great for fluent generation and in-context learning.
- T5: An encoder–decoder trained with a text-to-text objective. You feed text, it outputs text for many tasks (summarization, translation, QA).
Mental model
- BERT: High-quality reader. Think "deep understanding" of a passage at once.
- GPT: Storyteller. Think "what word naturally comes next"—excellent for completing and creating text.
- T5: Translator of goals. You describe the task in text; it converts input text into the desired output text.
Model families overview
BERT family (encoders)
Training objective: Masked language modeling (predict masked tokens). Often next sentence prediction variants are used/omitted depending on the model.
Strengths: Understanding, classification, token-level labeling (NER), embeddings for search.
Variants: BERT-base/large, DistilBERT (smaller), RoBERTa-like improvements, domain-specific BERTs (biomedical, legal).
Typical use-cases:
- Text/intent classification
- Semantic similarity and embeddings
- NER, POS tagging, span classification
Fine-tuning recipe (typical):
- Max sequence length 128–512 depending on task
- Batch size tuned to memory; use gradient accumulation if needed
- Learning rate ~1e-5 to 5e-5; 2–5 epochs
GPT family (decoder-only)
Training objective: Causal language modeling (predict next token).
Strengths: Fluent generation, in-context learning, instruction-following (with proper fine-tuning or formatting), code/text synthesis.
Typical use-cases:
Fine-tuning or adaptation: Preference for parameter-efficient tuning (e.g., LoRA/adapter layers) on instruction data; careful with context length for long prompts.
T5 family (encoder–decoder)
Training objective: Span corruption denoising; all tasks cast as text-to-text.
Strengths: Flexible supervised mapping from input text to output text (summarization, translation, QA, data-to-text).
Typical use-cases:
Fine-tuning recipe (typical):
- Prefix task instruction in the input (e.g., "summarize:")
- Learning rate ~1e-4 to 3e-4 for base models; 1e-5 to 5e-5 for large
- Teacher forcing with sequence-to-sequence loss
Choosing the right model (quick rules)
- Need embeddings or classification? Start with BERT-like encoders.
- Need fluent generation or few-shot flexibility? Start with GPT-like decoders.
- Need supervised text-in/text-out mapping (summarize/translate/QA)? Start with T5-like encoder–decoders.
- Latency-sensitive? Try smaller distilled variants or adapters.
- Long documents? Prefer models with extended context or chunking plus retrieval.
Worked examples
Example 1 — Intent classification with BERT
- Task: Classify support tickets into {billing, technical, account}.
- Model: Distilled BERT encoder.
- Data: 10k labeled tickets; average length 60 tokens.
- Setup: Sequence length 128, batch size that fits memory, LR 2e-5, 3 epochs, early stopping.
- Output: Softmax over 3 labels.
- Expected result: High accuracy; fast inference. Export pooled embedding + linear head.
Example 2 — Short reply drafting with GPT
- Task: Suggest a polite 1–2 sentence reply to customer emails.
- Model: Small GPT-like decoder with LoRA adapters.
- Data: 5k email-reply pairs.
- Setup: Prompt format: system-style instruction + email body; train adapters with LR 1e-4 for 2–3 epochs.
- Inference: Provide short guidance in the prompt (tone, length) and the email text.
- Expected result: Fluent, reasonably on-topic drafts; human approves/edits.
Example 3 — Summarization with T5
- Task: Summarize product specs into a 3-bullet summary.
- Model: T5-base.
- Data: 8k doc-summary pairs.
- Setup: Input prefix "summarize:"; max input length 512 (chunk if needed), target length ~100 tokens; LR 1e-4 for 3 epochs.
- Expected result: Concise summaries capturing key specs; controllable with prompt text.
Who this is for
- NLP Engineers and ML practitioners choosing architectures for specific tasks.
- Data scientists moving from classical NLP to transformers.
- Engineers integrating LLM features into products.
Prerequisites
- Comfort with Python and basic ML training workflows.
- Understanding of attention and transformer blocks at a high level.
- Familiarity with metrics (accuracy, F1, ROUGE, perplexity).
Learning path
- Master differences: encoder vs decoder vs encoder–decoder.
- Map tasks to families using the quick rules.
- Practice fine-tuning small models on small datasets.
- Add parameter-efficient tuning (adapters/LoRA) for GPT/T5.
- Optimize for latency and memory (distillation, quantization).
Common mistakes and self-check
- Mistake: Using GPT for pure classification without need for generation. Self-check: Could a small BERT variant perform faster and cheaper?
- Mistake: Feeding very long documents into short-context models. Self-check: Did you chunk inputs or use retrieval?
- Mistake: Skipping task prompts for T5. Self-check: Did you specify the task (e.g., "summarize:")?
- Mistake: Overfitting due to high LR or too many epochs. Self-check: Monitor validation loss and early stop.
- Mistake: Ignoring tokenization differences. Self-check: Are you using the correct tokenizer for the chosen model family?
Practical projects
- Ticket triage: Fine-tune a BERT-like model on a small support dataset and deploy a fast API.
- Smart drafting assistant: Adapt a GPT-like model with LoRA to generate short email replies.
- Product brief generator: Fine-tune T5 to turn long specifications into concise bullet summaries.
Exercises
Try these hands-on prompts. Use a notebook or your favorite framework. A checklist is provided for each. Solutions are available, but attempt first.
Exercise 1 — Pick the right family
For each scenario, choose BERT, GPT, or T5 and justify briefly:
- A) Classify app reviews into sentiment labels.
- B) Generate a short, friendly response to a user complaint.
- C) Create a 3-sentence summary of meeting notes.
- D) Compute embeddings for semantic search over FAQs.
- [ ] You chose a model family for each scenario
- [ ] You wrote a one-sentence justification each
- [ ] You noted any constraints (latency, context length)
Show solution
A) BERT (encoder, classification). B) GPT (decoder-only generation). C) T5 (encoder–decoder summarization). D) BERT (encoder embeddings for retrieval).
Justification: Match task nature—understanding vs generation vs supervised text-to-text.
Exercise 2 — Design a fine-tuning plan
Task: Headline generation from product descriptions (1–2 lines). Data: 6k pairs. Constraints: Low latency API; moderate GPU memory.
Deliverables:
- Pick a model family and variant
- Choose full fine-tuning vs adapters (e.g., LoRA)
- Propose key hyperparameters (LR, epochs, max length)
- Mention at least 2 evaluation metrics
- [ ] Family and variant chosen with reason
- [ ] Tuning method justified
- [ ] Hyperparameters listed
- [ ] Metrics specified
Show solution
Family: GPT-like decoder for short headline generation. Variant: Small model or distilled variant for latency. Tuning: LoRA to reduce memory and speed training. Hyperparams: LR 1e-4, 2–3 epochs, max input 256, max output 32, early stopping, warmup 5%. Metrics: ROUGE-1/2, human preference rating; track perplexity for training sanity.
Mini tasks
- Write a one-line rule: When would you pick T5 over GPT for generation?
- Create a prompt prefix for T5 summarization that enforces a bullet style.
- Sketch a retrieval + BERT encoder pipeline for FAQ search.
Quick reference
- Encoders (BERT): Understanding, embeddings, classification; short to medium inputs.
- Decoders (GPT): Autoregressive generation; great for drafting and few-shot prompts.
- Encoder–decoders (T5): Supervised text-to-text mapping with explicit task prefix.
Next steps
- Try parameter-efficient tuning techniques on a small GPT/T5 model.
- Benchmark BERT vs distilled variants for latency on your dataset.
- Add retrieval to handle longer contexts before scaling model size.
Quick Test
The quick test is available to everyone. Only logged-in users get saved progress.