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Interviews / AI & MLOps / Explain the difference between encoder-only, decoder-only, and encoder-decoder transformer architectures and when to use each.

Questions

Explain the difference between encoder-only, decoder-only, and encoder-decoder transformer architectures and when to use each.
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When would you use the Hugging Face `pipeline` function versus building a manual inference pipeline?
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How do you create a custom pipeline in the `transformers` library?
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How do you fine-tune a pre-trained model for a custom dataset?
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What are tokenizers and why are they important in NLP?
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You're running out of memory while fine-tuning a large model. How do you solve this?
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What is the Hugging Face Hub and what is it used for?
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How do you share a model on the Hugging Face Hub?
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How do you use the `accelerate` library to train a model on multiple GPUs?
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What are model cards and why are they important for responsible AI?
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How do you use the `datasets` library to load and process a custom dataset?
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What is the difference between `AutoModel` and a specific model class?
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Explain the difference between encoder-only, decoder-only, and encoder-decoder transformer architectures and when to use each.

conceptual Transformers Interactive Quiz Code Examples

The Scenario

You are the lead ML engineer at a fast-growing B2B SaaS company. Your team is tasked with building a new product, “InsightStream,” that analyzes customer feedback from various sources (support tickets, social media, call transcripts).

The product has three core features:

  1. Feedback Classifier: A multi-label classification system that automatically tags incoming feedback with categories like “Bug,” “Feature Request,” or “Pricing Issue.” Accuracy needs to be >95%.
  2. Generative Insights: A feature that generates weekly email summaries of the most critical customer feedback for product managers.
  3. Translation Service: An internal tool to translate non-English feedback into English for the product team.

The company has a limited budget for GPU resources, so you need to choose the most cost-effective and performant architecture for each feature.

The Challenge

For each of the three features, choose the best transformer architecture (encoder-only, decoder-only, or encoder-decoder). Justify your choice by explaining the architectural differences and trade-offs. Outline your implementation plan, including the specific model you would start with and the key steps to build each feature.

Wrong Approach

A junior engineer might suggest using a single, large decoder-only model (like GPT-3) for all three tasks. They might not consider the cost and performance implications of using a generative model for a classification task, or they might not be aware of the different transformer architectures and their specific strengths.

Right Approach

A senior engineer would recognize that each feature has different requirements and would choose the most appropriate architecture for each one. They would explain that an encoder-only model is best for the classifier, a decoder-only model is best for the generative insights, and an encoder-decoder model is best for the translation service. They would also be able to justify their choices with technical details and provide a clear implementation plan.

Step 1: Analyze the Requirements and Choose the Architectures

First, let’s break down the requirements for each feature and choose the best architecture:

FeatureTask TypeKey RequirementChosen ArchitectureJustification
Feedback ClassifierMulti-label classificationHigh accuracy, low costEncoder-onlyThis is a Natural Language Understanding (NLU) task. We need a model that can understand the content of the text, but we don’t need to generate any new text. Encoder-only models are highly performant and cost-effective for this type of task.
Generative InsightsConditional text generationHigh-quality, coherent textDecoder-onlyThis is a Natural Language Generation (NLG) task. We need a model that can generate creative and human-like text based on a prompt (the weekly feedback). Decoder-only models excel at this.
Translation ServiceSequence-to-sequenceHigh-quality translationEncoder-decoderThis is a sequence-to-sequence task, where the input is a sequence of text in one language and the output is a sequence of text in another language. Encoder-decoder models are specifically designed for this type of task.

Step 2: Implementation Plan - Feedback Classifier (Encoder-only)

Model: distilbert-base-uncased - It’s a smaller, faster, and cheaper version of BERT that is still very performant.

Plan:

  1. Load the pre-trained model and tokenizer: 
    from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=3, problem_type="multi_label_classification")
  2. Prepare the dataset: Use the datasets library to load the data and tokenize it.
  3. Fine-tune the model: Use the Trainer API to fine-tune the model on the custom dataset.
  4. Evaluate the model: Use metrics like F1-score, precision, and recall to evaluate the model’s performance.

Step 3: Implementation Plan - Generative Insights (Decoder-only)

Model: gpt2 - It’s a powerful and widely used generative model.

Plan:

  1. Load the pre-trained model and tokenizer: 
    from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")
  2. Prepare the dataset: Create a dataset of weekly feedback summaries.
  3. Fine-tune the model: Fine-tune the model on the dataset to generate summaries in the desired format.
  4. Implement a generation pipeline: Use the pipeline function or a custom generation loop to generate the summaries.

Step 4: Implementation Plan - Translation Service (Encoder-decoder)

Model: t5-small - It’s a powerful and flexible encoder-decoder model that can be used for a variety of sequence-to-sequence tasks.

Plan:

  1. Load the pre-trained model and tokenizer: 
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("t5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
  2. Prepare the dataset: Use a parallel dataset of English and non-English text.
  3. Fine-tune the model: Fine-tune the model on the dataset. T5 uses a specific prefix for each task, so we need to add a "translate English to German: " prefix to the input.
  4. Implement a translation pipeline: Use the pipeline function or a custom generation loop to perform the translation.

Practice Question

You need to build a system that can answer questions about a large document. Which transformer architecture would be the most suitable?