Why transformers revolutionized NLP - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Transformer model from the Hugging Face library.

NLP

from transformers import [1]

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

Atransformers

BTransformerModelClass

CAutoModel

DTransformerModel

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase or plural form like 'transformers' which is the package name.

Using incorrect class names like 'TransformerModel'.

The correct import is AutoModel which is the base class for transformer models in Hugging Face.

2fill in blank

medium

Complete the code to tokenize input text using a pretrained tokenizer.

NLP

tokenizer = AutoTokenizer.from_pretrained('[1]')

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

Atransformer-base

Bbert-base-uncased

Cnlp-transformer

Dbert-large-cased

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent model names like 'transformer-base'.

Using 'bert-large-cased' which is a different model variant.

The bert-base-uncased is a common pretrained model name used for tokenization.

3fill in blank

hard

Fix the error in the code to correctly create attention masks for input tokens.

NLP

attention_mask = (input_ids != [1]).long()

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

A0

B1

CNone

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 instead of 0, which reverses the mask meaning.

Using None or -1 which causes errors.

Attention masks use 0 to mark padding tokens, so tokens not equal to 0 are attended to.

4fill in blank

hard

Fill both blanks to create a dictionary of token ids and attention masks for model input.

NLP

inputs = {'input_ids': [1], 'attention_mask': [2]

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

Ainput_ids

Battention_mask

Ctoken_ids

Dmask

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect variable names like 'token_ids' or 'mask'.

Swapping the values between keys.

The model expects keys 'input_ids' and 'attention_mask' with corresponding tensors.

5fill in blank

hard

Fill all three blanks to run the transformer model and get the output logits.

NLP

outputs = model([1], [2])
logits = outputs.[3]

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

Ainput_ids

Battention_mask

Clogits

Dhidden_states

Attempts:

3 left

💡 Hint

Common Mistakes

Passing wrong arguments or missing attention_mask.

Accessing 'hidden_states' instead of 'logits' for predictions.

The model is called with input_ids and attention_mask, and the logits are accessed from outputs.

Practice

1. Why did transformers change the way machines understand language in NLP?

easy

A. Because they use simple rules without learning

B. Because they consider the whole sentence context at once

C. Because they only look at one word at a time

D. Because they ignore word order completely

Solution

1. Step 1: Understand traditional NLP limits

   Older models processed words one by one or in small groups, missing full sentence meaning.

2. Step 2: Recognize transformer's key feature

   Transformers look at all words together, capturing context better.

3. Final Answer:

   Because they consider the whole sentence context at once -> Option B

4. Quick Check:

   Context awareness = C [OK]

Hint: Transformers see all words together, not one by one [OK]

Common Mistakes:

• Thinking transformers process words one at a time
• Believing transformers ignore word order
• Confusing transformers with rule-based systems

2. Which of the following is the correct way to describe the transformer's attention mechanism?

easy

A. It randomly selects words to ignore

B. It translates words without looking at context

C. It focuses on important words by assigning weights to them

D. It removes all punctuation before processing

Solution

1. Step 1: Recall attention purpose

   Attention helps the model decide which words matter more in a sentence.

2. Step 2: Match description to attention

   Assigning weights to words matches how attention works.

3. Final Answer:

   It focuses on important words by assigning weights to them -> Option C

4. Quick Check:

   Attention = weighted focus [OK]

Hint: Attention means weighting important words higher [OK]

Common Mistakes:

• Thinking attention ignores words randomly
• Believing attention removes punctuation
• Confusing attention with translation

3. Given this simplified transformer attention code snippet, what will be the output shape if input has shape (batch_size=2, seq_len=3, embed_dim=4)?

import torch
from torch.nn import MultiheadAttention

input_tensor = torch.rand(3, 2, 4)  # seq_len, batch_size, embed_dim
attention = MultiheadAttention(embed_dim=4, num_heads=2)
output, _ = attention(input_tensor, input_tensor, input_tensor)
print(output.shape)

medium

A. torch.Size([3, 2, 4])

B. torch.Size([2, 3, 4])

C. torch.Size([3, 4, 2])

D. torch.Size([2, 4, 3])

Solution

1. Step 1: Understand input shape format

   Input shape is (seq_len=3, batch_size=2, embed_dim=4) as required by PyTorch MultiheadAttention.

2. Step 2: Check output shape from attention

   Output shape matches input shape: (seq_len, batch_size, embed_dim) = (3, 2, 4).

3. Final Answer:

   torch.Size([3, 2, 4]) -> Option A

4. Quick Check:

   Output shape = input shape [OK]

Hint: Output shape matches input shape in PyTorch attention [OK]

Common Mistakes:

• Mixing batch and sequence dimensions
• Assuming output shape changes embed dimension
• Confusing PyTorch input format with batch-first

4. This code tries to create a transformer model but throws an error. What is the mistake?

from transformers import BertModel

model = BertModel()
output = model("Hello world")

medium

A. The string input should be a list, not a string

B. BertModel cannot be imported from transformers

C. The model must be trained before use

D. BertModel requires tokenized input, not raw text

Solution

1. Step 1: Check input type for BertModel

   BertModel expects token IDs (numbers), not raw text strings.

2. Step 2: Identify correct input preparation

   Text must be tokenized using a tokenizer before passing to the model.

3. Final Answer:

   BertModel requires tokenized input, not raw text -> Option D

4. Quick Check:

   Tokenize text before model input [OK]

Hint: Always tokenize text before feeding to transformer models [OK]

Common Mistakes:

• Passing raw strings directly to model
• Assuming model auto-tokenizes input
• Ignoring need for attention masks

5. You want to build a chatbot using transformers that can understand long conversations. Which feature of transformers helps handle long context better than older models?

hard

A. Self-attention mechanism that relates all words in the input

B. Using fixed-size windows to read text piece by piece

C. Ignoring previous sentences to focus on current input

D. Replacing words with fixed dictionaries without learning

Solution

1. Step 1: Understand chatbot context needs

   Chatbots must remember and relate words across long conversations.

2. Step 2: Identify transformer feature for long context

   Self-attention lets the model connect all words, even far apart, in one pass.

3. Final Answer:

   Self-attention mechanism that relates all words in the input -> Option A

4. Quick Check:

   Self-attention = long context handling [OK]

Hint: Self-attention links all words for long context [OK]

Common Mistakes:

• Thinking transformers read text in small fixed windows
• Believing transformers ignore previous sentences
• Confusing dictionary lookup with learning