Custom QA model fine-tuning in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is fine-tuning in the context of a custom QA model?

Fine-tuning means taking a pre-trained language model and training it a bit more on your specific question-answer data so it learns to answer questions better for your topic.

beginner

Why do we use pre-trained models for fine-tuning instead of training from scratch?

Pre-trained models already know language basics from lots of text, so fine-tuning them on your data is faster, needs less data, and gives better results than starting fresh.

beginner

What kind of data is needed to fine-tune a custom QA model?

You need pairs of questions and their correct answers, often with the context text where the answer is found, so the model learns to find and give the right answers.

intermediate

How do we measure if a custom QA model is improving during fine-tuning?

We check metrics like accuracy or F1 score on a test set of questions and answers to see if the model gives more correct answers after training.

beginner

What is the role of the context passage in a QA fine-tuning dataset?

The context passage is the text where the answer is found. The model learns to read this passage and pick the right answer to the question from it.

What does fine-tuning a QA model usually require?

A Random text documents

B Only a list of questions

C Only a list of answers

D A dataset of questions, answers, and context passages

Fine-tuning needs question-answer pairs with context so the model learns where to find answers.

Why is fine-tuning faster than training a QA model from scratch?

A Because it uses less data

B Because it skips evaluation

C Because the model already knows language patterns from pre-training

D Because it uses simpler algorithms

Pre-trained models have learned language basics, so fine-tuning adjusts them quickly for your task.

Which metric is commonly used to evaluate QA model performance?

A Mean squared error

B F1 score

C BLEU score

D Perplexity

F1 score measures how well the model finds correct answers balancing precision and recall.

What is the main goal of fine-tuning a QA model?

A To make the model answer questions accurately for your specific data

B To increase the model size

C To reduce the number of questions

D To generate new questions

Fine-tuning adapts the model to your data so it answers questions correctly.

Which part of the QA dataset helps the model locate the answer?

A Context passage

B Question only

C Answer only

D Model architecture

The context passage contains the information where the answer is found.

Explain the process and purpose of fine-tuning a custom QA model.

Think about how a general language model learns to answer your specific questions better.

Describe the types of data and evaluation metrics used in fine-tuning a QA model.

Consider what the model needs to learn and how we check if it learned well.

Practice

1. What is the main purpose of fine-tuning a custom QA model?

easy

A. To reduce the training time of the model

B. To make the model answer questions better on your specific data

C. To increase the model's size and complexity

D. To change the model's language to another one

Solution

1. Step 1: Understand fine-tuning goal

   Fine-tuning adjusts a model to perform better on a specific task or dataset.

2. Step 2: Relate to QA models

   For QA, fine-tuning helps the model answer questions accurately on your own data.

3. Final Answer:

   To make the model answer questions better on your specific data -> Option B

4. Quick Check:

   Fine-tuning = better task-specific answers [OK]

Hint: Fine-tuning adapts model to your data for better answers [OK]

Common Mistakes:

• Thinking fine-tuning changes model size
• Confusing fine-tuning with faster training
• Assuming it changes the model's language

2. Which of the following is the correct way to prepare data for fine-tuning a QA model?

easy

A. A dataset with questions, contexts, and answers

B. A dataset with only questions and answers

C. A dataset with only contexts and answers

D. A dataset with random text and no labels

Solution

1. Step 1: Identify required data components

   QA models need questions, contexts (where answers are found), and answers to learn properly.

2. Step 2: Check options

   Only the dataset with questions, contexts, and answers includes all three necessary parts for training.

3. Final Answer:

   A dataset with questions, contexts, and answers -> Option A

4. Quick Check:

   QA data = questions + contexts + answers [OK]

Hint: QA fine-tuning needs question, context, and answer triplets [OK]

Common Mistakes:

• Omitting context in the dataset
• Using unlabeled or random text
• Ignoring the answer field

3. Given the following code snippet for fine-tuning a QA model using Hugging Face Trainer, what will be the output metric after training?

from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(output_dir='./results', num_train_epochs=1)
trainer = Trainer(model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset)
metrics = trainer.train()
print(metrics.metrics['eval_accuracy'])

medium

A. An integer count of training steps

B. A syntax error due to missing eval_accuracy metric

C. A float value representing evaluation accuracy

D. A KeyError because eval_accuracy is not computed by default

Solution

1. Step 1: Understand default metrics in Trainer

   By default, Trainer does not compute 'eval_accuracy' unless a compute_metrics function is provided.

2. Step 2: Analyze printed output

   Since no compute_metrics is defined, 'eval_accuracy' key won't exist, so accessing it causes a KeyError.

3. Final Answer:

   A KeyError because eval_accuracy is not computed by default -> Option D

4. Quick Check:

   Default Trainer lacks eval_accuracy metric [OK]

Hint: Without compute_metrics, eval_accuracy is not available [OK]

Common Mistakes:

• Assuming eval_accuracy is always computed
• Expecting a syntax error instead of missing metric
• Confusing training steps count with accuracy

4. You tried fine-tuning a QA model but got this error: ValueError: Expected input batch to have 3 elements (input_ids, attention_mask, token_type_ids). What is the most likely cause?

medium

A. You forgot to set num_train_epochs in TrainingArguments

B. The model architecture is incompatible with QA tasks

C. Your dataset does not return token_type_ids in __getitem__

D. You used the wrong optimizer in Trainer

Solution

1. Step 1: Understand the error message

   The error says the input batch misses token_type_ids, which are needed for some QA models.

2. Step 2: Check dataset output

   If the dataset's __getitem__ method does not return token_type_ids, the model input is incomplete causing this error.

3. Final Answer:

   Your dataset does not return token_type_ids in __getitem__ -> Option C

4. Quick Check:

   Missing token_type_ids in data causes input error [OK]

Hint: Check dataset returns all required inputs including token_type_ids [OK]

Common Mistakes:

• Blaming TrainingArguments settings
• Assuming model architecture is wrong
• Thinking optimizer causes input shape errors

5. You want to fine-tune a QA model on a small dataset but avoid overfitting. Which strategy is best to apply during fine-tuning?

hard

A. Use early stopping and lower learning rate

B. Increase number of epochs to 100

C. Remove the context from training data

D. Use a larger batch size without changing learning rate

Solution

1. Step 1: Identify overfitting risk factors

   Small datasets can cause models to memorize instead of generalize, leading to overfitting.

2. Step 2: Choose strategies to reduce overfitting

   Early stopping stops training when performance stops improving; lower learning rate helps gradual learning.

3. Final Answer:

   Use early stopping and lower learning rate -> Option A

4. Quick Check:

   Early stopping + low LR reduces overfitting [OK]

Hint: Stop early and slow learning to prevent overfitting [OK]

Common Mistakes:

• Training too many epochs on small data
• Removing context which is essential
• Increasing batch size without adjusting learning rate