GPT family overview in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the GPT model from the transformers library.

NLP

from transformers import [1]

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

AGPT2Model

BBertModel

CRobertaModel

DT5Model

Attempts:

3 left

💡 Hint

Common Mistakes

Importing BertModel or RobertaModel which are different model families.

Using T5Model which is unrelated to GPT.

The GPT family models are accessed via GPT2Model in the transformers library.

2fill in blank

medium

Complete the code to load the GPT-2 tokenizer.

NLP

from transformers import GPT2Tokenizer

tokenizer = GPT2Tokenizer.from_pretrained([1])

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

A't5-small'

B'gpt2'

C'roberta-base'

D'bert-base-uncased'

Attempts:

3 left

💡 Hint

Common Mistakes

Using tokenizer names from other model families like BERT or RoBERTa.

Forgetting the quotes around the model name.

The GPT-2 tokenizer is loaded using the model name 'gpt2'.

3fill in blank

hard

Fix the error in the code to generate text using GPT-2.

NLP

from transformers import GPT2LMHeadModel, GPT2Tokenizer

tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

input_ids = tokenizer.encode('Hello, how are you?', return_tensors=[1])
outputs = model.generate(input_ids, max_length=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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

Apt

Btf

Cnp

Dtorch

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'tf' which is for TensorFlow tensors.

Using 'torch' which is not a valid string argument here.

The return_tensors argument expects 'pt' for PyTorch tensors when using GPT-2 with transformers.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps GPT model names to their sizes in millions of parameters.

NLP

gpt_sizes = { [1] : [2] for [1] in ['gpt2', 'gpt2-medium', 'gpt2-large'] }

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

Amodel

B{'gpt2': 124, 'gpt2-medium': 355, 'gpt2-large': 774}

Csize

Dparams

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same variable name for key and value incorrectly.

Trying to use a variable that is not defined in the comprehension.

The variable model is used as the key, and the dictionary with sizes is used as the value for each key in the comprehension.

5fill in blank

hard

Fill both blanks to create a function that returns the number of tokens in a text using GPT-2 tokenizer.

NLP

def count_tokens(text):
    tokenizer = GPT2Tokenizer.from_pretrained([1])
    tokens = tokenizer.[2](text)
    return len(tokens)  # number of tokens

print(count_tokens('Hello world!'))

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

A'gpt2'

Bencode

Ctokenize

D'bert-base-uncased'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'tokenize' instead of 'encode'.

Loading tokenizer with a wrong model name.

The GPT-2 tokenizer is loaded with 'gpt2'. The method to convert text to tokens is encode. The function returns the length of the token list.

Practice

1. What is the main purpose of GPT models in natural language processing?

easy

A. To help computers understand and generate human-like text

B. To perform image recognition tasks

C. To analyze numerical data trends

D. To control robotic movements

Solution

1. Step 1: Understand GPT's role in NLP

   GPT models are designed to process and generate text that resembles human language.

2. Step 2: Compare options with GPT's function

   Only To help computers understand and generate human-like text matches the text-based purpose of GPT models.

3. Final Answer:

   To help computers understand and generate human-like text -> Option A

4. Quick Check:

   GPT purpose = text generation and understanding [OK]

Hint: GPT = text understanding and generation [OK]

Common Mistakes:

• Confusing GPT with image or numerical models
• Thinking GPT controls hardware
• Assuming GPT only analyzes data without generating text

2. Which of the following is the correct way to call a GPT model API to generate text?

easy

A. generate.gpt_text('Hello world')

B. gpt.generate_text(prompt='Hello world')

C. gpt.text_generate('Hello world')

D. text.gpt_generate(prompt='Hello world')

Solution

1. Step 1: Identify correct method naming conventions

   Common GPT APIs use a method like generate_text with a prompt argument.

2. Step 2: Match options to typical API call

   gpt.generate_text(prompt='Hello world') matches the expected syntax and naming style.

3. Final Answer:

   gpt.generate_text(prompt='Hello world') -> Option B

4. Quick Check:

   API call syntax = gpt.generate_text(prompt='Hello world') [OK]

Hint: Look for method named generate_text with prompt argument [OK]

Common Mistakes:

• Mixing method and object names incorrectly
• Using wrong method order or missing prompt keyword
• Confusing function names with invalid syntax

3. Given the following Python code using a GPT model API, what will be the output?

response = gpt.generate_text(prompt='Good morning')
print(response)

medium

A. 'Good morning! How can I help you today?'

B. SyntaxError: missing parentheses in call to 'print'

C. 'Error: prompt not provided'

D. 'Good morning'

Solution

1. Step 1: Understand the API call behavior

   The generate_text method returns a text response continuing the prompt.

2. Step 2: Predict output from the prompt 'Good morning'

   The model likely generates a polite continuation like 'Good morning! How can I help you today?'.

3. Final Answer:

   'Good morning! How can I help you today?' -> Option A

4. Quick Check:

   Output = polite text continuation [OK]

Hint: GPT outputs text continuing the prompt [OK]

Common Mistakes:

• Expecting exact prompt as output
• Confusing syntax errors with correct code
• Assuming error messages without cause

4. Identify the error in this GPT model usage code snippet:

response = gpt.generate_text('Hello')

medium

A. The string 'Hello' should be a list, not a string

B. Incorrect method name, should be generate_text instead of generate

C. The variable 'response' is not defined

D. Missing prompt keyword argument in function call

Solution

1. Step 1: Check function call syntax

   The generate_text method requires the prompt to be passed as a keyword argument like prompt='Hello'.

2. Step 2: Identify the error in the code

   The code passes 'Hello' as a positional argument, which causes an error.

3. Final Answer:

   Missing prompt keyword argument in function call -> Option D

4. Quick Check:

   Keyword argument prompt required [OK]

Hint: Check if prompt is passed as keyword argument [OK]

Common Mistakes:

• Passing prompt as positional argument
• Confusing method names
• Assuming variable declaration errors

5. You want to build a chatbot using a GPT model that can answer questions about weather. Which approach best combines GPT's capabilities with your goal?

hard

A. Train GPT from scratch only on weather data without any pretrained model

B. Use GPT only to fetch weather data from the internet

C. Use GPT to generate text responses and integrate a weather API to provide real data

D. Replace GPT with a simple keyword matching system for weather questions

Solution

1. Step 1: Understand GPT's strength and limitations

   GPT generates human-like text but does not access real-time data by itself.

2. Step 2: Combine GPT with external data source

   Integrating a weather API provides accurate data, while GPT formats responses naturally.

3. Final Answer:

   Use GPT to generate text responses and integrate a weather API to provide real data -> Option C

4. Quick Check:

   GPT + API = best chatbot design [OK]

Hint: Combine GPT text with real data API for accuracy [OK]

Common Mistakes:

• Training GPT from scratch unnecessarily
• Expecting GPT to fetch live data alone
• Ignoring natural language generation benefits