Hugging Face fine-tuning in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pretrained model from Hugging Face.

Prompt Engineering / GenAI

from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained('[1]')

Drag options to blanks, or click blank then click option'

Ascikit-learn

Bpytorch-lightning

Cbert-base-uncased

Dtensorflow

Attempts:

3 left

💡 Hint

Common Mistakes

Using framework names like 'tensorflow' instead of model names.

Confusing model names with unrelated libraries.

The correct model name to load a pretrained BERT model is 'bert-base-uncased'.

2fill in blank

medium

Complete the code to prepare the tokenizer for the model.

Prompt Engineering / GenAI

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('[1]')

Drag options to blanks, or click blank then click option'

Agpt2

Broberta-base

Cdistilbert-base-uncased

Dbert-base-uncased

Attempts:

3 left

💡 Hint

Common Mistakes

Using a tokenizer from a different model family.

Using a tokenizer that does not match the model.

The tokenizer must match the model, so 'bert-base-uncased' is correct here.

3fill in blank

hard

Fix the error in the training loop by completing the missing optimizer step.

Prompt Engineering / GenAI

for batch in train_dataloader:
    outputs = model(**batch)
    loss = outputs.loss
    loss.backward()
    [1]
    optimizer.zero_grad()

Drag options to blanks, or click blank then click option'

Aoptimizer.step()

Bloss.step()

Cmodel.step()

Dloss.backward()

Attempts:

3 left

💡 Hint

Common Mistakes

Calling loss.backward() twice.

Calling step() on loss or model instead of optimizer.

After backward(), optimizer.step() updates the model weights.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Prompt Engineering / GenAI

{word: [1] for word in words if len(word) [2] 3}

Drag options to blanks, or click blank then click option'

Alen(word)

B<

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Using '<' instead of '>' in the condition.

We want the length as value and filter words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys, values as counts, and only include counts greater than 1.

Prompt Engineering / GenAI

filtered_counts = { [1]: [2] for [3], count in counts.items() if count > 1 }

Drag options to blanks, or click blank then click option'

Aword.upper()

Bcount

Cword

Dcount.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using count.upper() which is invalid for integers.

Swapping keys and values in the dictionary comprehension.

Keys are uppercase words, values are counts, and iteration uses word and count.

Practice

1. What is the main purpose of fine-tuning a pre-trained model using Hugging Face?

easy

A. To adapt the model to perform well on a specific new task

B. To train a model from scratch without any prior knowledge

C. To reduce the size of the model for faster inference

D. To convert the model into a different programming language

Solution

1. Step 1: Understand what fine-tuning means

   Fine-tuning means taking a model already trained on a large dataset and adjusting it to work well on a new, specific task.

2. Step 2: Identify the purpose in Hugging Face context

   Hugging Face fine-tuning adapts the pre-trained model's knowledge to your task, improving accuracy without training from scratch.

3. Final Answer:

   To adapt the model to perform well on a specific new task -> Option A

4. Quick Check:

   Fine-tuning = adapt model to new task [OK]

Hint: Fine-tuning means adjusting a model for your task [OK]

Common Mistakes:

• Thinking fine-tuning trains a model from scratch
• Confusing fine-tuning with model compression
• Assuming fine-tuning changes the programming language

2. Which of the following is the correct way to create a TrainingArguments object in Hugging Face?

easy

A. training_args = TrainArgs(directory='output', epochs=3)

B. training_args = TrainerArguments(output='output', epochs=3)

C. training_args = Training(output_dir='output', epochs=3)

D. training_args = TrainingArguments(output_dir='output', num_train_epochs=3)

Solution

1. Step 1: Recall the correct class name and parameters

   The Hugging Face library uses the class TrainingArguments with parameters like output_dir and num_train_epochs.

2. Step 2: Match the correct syntax

   training_args = TrainingArguments(output_dir='output', num_train_epochs=3) uses the correct class name and parameter names exactly as in the Hugging Face API.

3. Final Answer:

   training_args = TrainingArguments(output_dir='output', num_train_epochs=3) -> Option D

4. Quick Check:

   TrainingArguments with output_dir and num_train_epochs [OK]

Hint: Use TrainingArguments with output_dir and num_train_epochs [OK]

Common Mistakes:

• Using wrong class names like TrainerArguments or TrainArgs
• Using incorrect parameter names like epochs instead of num_train_epochs
• Confusing Trainer and TrainingArguments classes

3. Given the code snippet below, what will be the output of print(len(tokenized_datasets['train'][0]['input_ids']))?

from datasets import load_dataset
from transformers import AutoTokenizer

dataset = load_dataset('imdb', split='train[:1%]')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
tokenized_datasets = dataset.map(lambda x: tokenizer(x['text'], truncation=True, padding='max_length', max_length=128))

medium

A. None, it will raise an error

B. 128

C. 512

D. variable length depending on text

Solution

1. Step 1: Understand tokenizer parameters

   The tokenizer is called with padding='max_length' and max_length=128, so all sequences are padded or truncated to length 128.

2. Step 2: Check the length of input_ids

   Since padding to max_length is applied, each tokenized input's input_ids list length is exactly 128.

3. Final Answer:

   128 -> Option B

4. Quick Check:

   Padding to max_length = fixed length 128 [OK]

Hint: Padding with max_length fixes token length [OK]

Common Mistakes:

• Assuming variable length without padding
• Confusing max_length with 512 default
• Expecting error due to missing batch=True

4. You wrote this code to fine-tune a model but get an error: TypeError: Trainer() missing 1 required positional argument: 'model'. What is the likely fix?

medium

A. Change Trainer to TrainingArguments

B. Remove the 'model' argument from Trainer initialization

C. Pass the pre-trained model as the 'model' argument when creating Trainer

D. Call Trainer.train() before creating the Trainer object

Solution

1. Step 1: Understand the error message

   The error says the Trainer constructor needs a 'model' argument but it was not provided.

2. Step 2: Fix by providing the model

   When creating a Trainer, you must pass the pre-trained model as the 'model' parameter to avoid this error.

3. Final Answer:

   Pass the pre-trained model as the 'model' argument when creating Trainer -> Option C

4. Quick Check:

   Trainer requires model argument [OK]

Hint: Always pass model to Trainer constructor [OK]

Common Mistakes:

• Forgetting to pass model to Trainer
• Confusing Trainer with TrainingArguments
• Calling train() before creating Trainer

5. You want to fine-tune a Hugging Face model on a small dataset but avoid overfitting. Which combination of TrainingArguments settings is best?

hard

A. Set num_train_epochs=3 and use evaluation_strategy='steps' with early stopping

B. Set num_train_epochs=10 and learning_rate=5e-5

C. Set batch_size=1 and disable evaluation

D. Set num_train_epochs=1 and learning_rate=1.0

Solution

1. Step 1: Identify overfitting prevention methods

   Using fewer epochs and evaluation with early stopping helps stop training before overfitting.

2. Step 2: Evaluate options for best practice

   Set num_train_epochs=3 and use evaluation_strategy='steps' with early stopping sets a moderate number of epochs and enables evaluation with early stopping, which is best to avoid overfitting.

3. Final Answer:

   Set num_train_epochs=3 and use evaluation_strategy='steps' with early stopping -> Option A

4. Quick Check:

   Early stopping + moderate epochs prevent overfitting [OK]

Hint: Use early stopping and moderate epochs to avoid overfitting [OK]

Common Mistakes:

• Using too many epochs causing overfitting
• Setting learning rate too high or too low
• Ignoring evaluation and early stopping