BERT vs GPT vs T5 - Architecture Choices
The three dominant Transformer families have fundamentally different training objectives that make them suited to different tasks. Understanding these architectural differences tells you immediately which model family to reach for for any NLP task.
BERT, GPT, T5 Compared
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# THREE TRANSFORMER FAMILIES
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architectures = {
"BERT (2018, Google)": {
"type": "Encoder-only (bidirectional)",
"pretraining": "Masked Language Modeling (MLM) - predict 15% of masked tokens",
"sees": "Full context: both LEFT and RIGHT of each token simultaneously",
"good_for": "Understanding/classification: sentiment, NER, QA, entailment",
"bad_for": "Text generation (no causal mask - not autoregressive)",
"example": "Fill in: 'The [MASK] jumped over the lazy [MASK]'",
"variants": "RoBERTa, DistilBERT, ALBERT, DeBERTa-v3",
},
"GPT (2018-2024, OpenAI)": {
"type": "Decoder-only (causal / left-to-right)",
"pretraining": "Causal Language Modeling (CLM) - predict next token from past only",
"sees": "Only LEFT context (past tokens)",
"good_for": "Text generation, chat, code, completion",
"bad_for": "Bidirectional tasks that need full context",
"example": "Continue: 'The fox jumped over the...'",
"variants": "GPT-2 (1.5B), GPT-3 (175B), GPT-4 (est. 1.8T MoE), LLaMA, Mistral",
},
"T5 (2019, Google)": {
"type": "Encoder-Decoder (seq2seq)",
"pretraining": "Span corruption - mask spans, train to reconstruct",
"sees": "Encoder sees full input; decoder generates output autoregressively",
"good_for": "Translation, summarization, Q&A, any input→output task",
"example": "translate English to French: The cat sat on the mat → Le chat...",
"variants": "mT5 (multilingual), Flan-T5 (instruction-tuned), T5-11B",
},
}
for name, props in architectures.items():
print(f"\n{name}")
for k, v in props.items():
if k != "variants":
print(f" {k:15s}: {v}")
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# QUICK PROVIDER API COMPARISON (2024)
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from transformers import pipeline
# BERT: mask filling (understanding)
bert_fill = pipeline("fill-mask", model="bert-base-uncased")
result = bert_fill("The scientist discovered a new [MASK] that could cure cancer.")
print(f"\nBERT fills mask: {result[0]['token_str']} ({result[0]['score']:.1%})")
# GPT-2: text generation (generation)
gpt_gen = pipeline("text-generation", model="gpt2")
result = gpt_gen("Artificial intelligence will", max_new_tokens=30, do_sample=True)
print(f"GPT-2 continues: {result[0]['generated_text']}")
# DistilBERT: classification (understanding applied)
classifier = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
result = classifier("This transformer tutorial is incredibly clear and helpful!")
print(f"Sentiment: {result[0]['label']} ({result[0]['score']:.1%})")Tip
Tip
Practice BERT vs GPT vs T5 Architecture Choices in small, isolated examples before integrating into larger projects. Breaking concepts into small experiments builds genuine understanding faster than reading alone.
Technical diagram.
Practice Task
Note
Practice Task - (1) Write a working example of BERT vs GPT vs T5 Architecture Choices from scratch without looking at notes. (2) Modify it to handle an edge case (empty input, null value, or error state). (3) Share your solution in the Priygop community for feedback.
Quick Quiz
Common Mistake
Warning
A common mistake with BERT vs GPT vs T5 Architecture Choices is skipping edge case testing - empty inputs, null values, and unexpected data types. Always validate boundary conditions to write robust, production-ready ai code.