RoBERTa and DistilBERT in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the RoBERTa tokenizer.

NLP

from transformers import [1]
tokenizer = [1].from_pretrained('roberta-base')

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

ABertTokenizer

BDistilBertTokenizer

CRobertaTokenizer

DGPT2Tokenizer

Attempts:

3 left

💡 Hint

Common Mistakes

Using BertTokenizer instead of RobertaTokenizer

Using DistilBertTokenizer for RoBERTa

The RobertaTokenizer is used to load the tokenizer for RoBERTa models.

2fill in blank

medium

Complete the code to load the DistilBERT model for sequence classification.

NLP

from transformers import [1]
model = [1].from_pretrained('distilbert-base-uncased-finetuned-sst-2-english')

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

ABertForSequenceClassification

BDistilBertForSequenceClassification

CGPT2ForSequenceClassification

DRobertaForSequenceClassification

Attempts:

3 left

💡 Hint

Common Mistakes

Using RobertaForSequenceClassification for DistilBERT

Using GPT2ForSequenceClassification which is unrelated

DistilBertForSequenceClassification loads the DistilBERT model fine-tuned for classification tasks.

3fill in blank

hard

Fix the error in the code to tokenize input text using RoBERTa tokenizer.

NLP

inputs = tokenizer([1], return_tensors='pt')

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

A'Hello, how are you?'

B12345

Cb'Hello, how are you?'

D['Hello, how are you?']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list instead of a string

Passing bytes instead of string

Passing an integer

The tokenizer expects a string input, so passing a plain string like 'Hello, how are you?' is correct.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps tokens to their IDs using DistilBERT tokenizer.

NLP

token_ids = {token: [1] for token, [2] in tokenizer.get_vocab().items()}

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

Aid

Btoken_id

Cid_

Dtokenid

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names for the token ID

Using undefined variable names

The items() returns (token, token_id) pairs, so the variable for the ID should be token_id.

5fill in blank

hard

Fill all three blanks to prepare inputs, run the model, and get logits using RoBERTa.

NLP

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

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

A'This is a test sentence.'

B'pt'

Cinputs['input_ids']

D'tf'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the whole inputs dict instead of input IDs

Using TensorFlow tensor type 'tf' when model expects PyTorch

Passing a list instead of a string to tokenizer

We tokenize a string input, specify PyTorch tensors with 'pt', and pass inputs['input_ids'] to the model to get outputs.

Practice

1. Which statement best describes the main difference between RoBERTa and DistilBERT?

easy

A. Both models have the same size and speed but different training data.

B. DistilBERT is larger and more accurate, while RoBERTa is smaller and faster.

C. RoBERTa is designed only for translation, DistilBERT only for summarization.

D. RoBERTa is larger and more accurate, while DistilBERT is smaller and faster.

Solution

1. Step 1: Understand model size and purpose

   RoBERTa is a large language model designed for high accuracy in text understanding. DistilBERT is a smaller, compressed version of BERT focused on speed and efficiency.

2. Step 2: Compare their main characteristics

   RoBERTa offers better accuracy due to its size and training, while DistilBERT sacrifices some accuracy for faster performance and smaller size.

3. Final Answer:

   RoBERTa is larger and more accurate, while DistilBERT is smaller and faster. -> Option D

4. Quick Check:

   Model size and speed difference = C [OK]

Hint: Remember: RoBERTa = accuracy, DistilBERT = speed [OK]

Common Mistakes:

• Confusing which model is larger
• Thinking both models have the same speed
• Assuming DistilBERT is more accurate

2. Which of the following is the correct way to load a pre-trained DistilBERT model using Hugging Face Transformers in Python?

easy

A. from transformers import DistilBertModel
model = DistilBertModel.from_pretrained('distilbert-base-uncased')

B. from transformers import RobertaModel
model = RobertaModel.load('distilbert-base-uncased')

C. import transformers
model = transformers.DistilBert.load_pretrained('distilbert-base-uncased')

D. from transformers import DistilBertTokenizer
model = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')

Solution

1. Step 1: Identify correct import and method

   The Hugging Face library uses from_pretrained() to load models. DistilBertModel is the correct class for the DistilBERT model.

2. Step 2: Check each option's correctness

   from transformers import DistilBertModel model = DistilBertModel.from_pretrained('distilbert-base-uncased') correctly imports DistilBertModel and calls from_pretrained with the right model name. Options A and C use wrong classes or methods. from transformers import DistilBertTokenizer model = DistilBertTokenizer.from_pretrained('distilbert-base-uncased') loads a tokenizer, not a model.

3. Final Answer:

   from transformers import DistilBertModel model = DistilBertModel.from_pretrained('distilbert-base-uncased') -> Option A

4. Quick Check:

   Correct import and method = B [OK]

Hint: Use from_pretrained() with correct model class [OK]

Common Mistakes:

• Confusing tokenizer with model loading
• Using load() instead of from_pretrained()
• Importing wrong model class

3. Given the following Python code using Hugging Face Transformers, what will be the output shape of outputs.last_hidden_state?

from transformers import RobertaModel, RobertaTokenizer
import torch

tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaModel.from_pretrained('roberta-base')

inputs = tokenizer('Hello', return_tensors='pt')
outputs = model(**inputs)
print(outputs.last_hidden_state.shape)

medium

A. torch.Size([768, 3])

B. torch.Size([1, 3, 768])

C. torch.Size([1, 768])

D. torch.Size([3, 768])

Solution

1. Step 1: Understand tokenizer output shape

   The tokenizer returns a batch with 1 sentence. The tokenized input includes special tokens, so 'Hello' becomes 3 tokens (<s>, Hello, </s>).

2. Step 2: Understand model output shape

   RobertaModel outputs last_hidden_state with shape (batch_size, sequence_length, hidden_size). Batch size is 1, sequence length is 3 tokens, hidden size is 768 for roberta-base.

3. Final Answer:

   torch.Size([1, 3, 768]) -> Option B

4. Quick Check:

   Output shape = (batch, tokens, features) = D [OK]

Hint: Output shape = (batch, tokens, hidden size) [OK]

Common Mistakes:

• Ignoring batch dimension
• Confusing sequence length with hidden size
• Assuming tokenizer returns 1 token

4. You try to load a DistilBERT model with this code but get an error:

from transformers import DistilBertModel
model = DistilBertModel.from_pretrained('roberta-base')

What is the main issue causing the error?

medium

A. The from_pretrained method does not exist for DistilBertModel.

B. You forgot to import the tokenizer.

C. The model name 'roberta-base' is incompatible with DistilBertModel class.

D. The model name should be 'distilbert-base-uncased' but you used 'roberta-base'.

Solution

1. Step 1: Check model class and model name compatibility

   DistilBertModel expects a DistilBERT model name. Using 'roberta-base' is for RobertaModel, so the class and model name mismatch causes error.

2. Step 2: Confirm correct usage

   To load 'roberta-base', use RobertaModel class. For DistilBERT, use 'distilbert-base-uncased' with DistilBertModel.

3. Final Answer:

   The model name 'roberta-base' is incompatible with DistilBertModel class. -> Option C

4. Quick Check:

   Model class and name must match = A [OK]

Hint: Match model class with correct pretrained name [OK]

Common Mistakes:

• Using wrong model name for the class
• Assuming from_pretrained method is missing
• Confusing tokenizer import with model loading

5. You want to deploy a text classification system that needs to run on a mobile device with limited memory but still maintain reasonable accuracy. Which model choice and approach is best?

hard

A. Use DistilBERT for faster inference and smaller size, accepting slight accuracy loss.

B. Use RoBERTa for best accuracy and compress it with quantization for mobile deployment.

C. Use full BERT model without compression for maximum accuracy.

D. Use RoBERTa with no compression for best speed.

Solution

1. Step 1: Consider device constraints and model size

   Mobile devices have limited memory and compute power, so smaller models are preferred for speed and size.

2. Step 2: Evaluate model trade-offs

   DistilBERT is designed to be smaller and faster than RoBERTa or full BERT, with only a small drop in accuracy, making it suitable for mobile.

3. Step 3: Assess other options

   RoBERTa is larger and slower; compressing it can help but adds complexity. Full BERT is too large. RoBERTa without compression is slow.

4. Final Answer:

   Use DistilBERT for faster inference and smaller size, accepting slight accuracy loss. -> Option A

5. Quick Check:

   Mobile deployment favors small, fast models = A [OK]

Hint: Choose smaller model for mobile speed and size [OK]

Common Mistakes:

• Choosing large models ignoring device limits
• Assuming compression is always best without trade-offs
• Confusing accuracy priority over speed on mobile