Hugging Face Transformers library in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output shape of the model's last hidden state?

Given the following code using Hugging Face Transformers, what is the shape of the last hidden state tensor?

NLP

from transformers import BertModel, BertTokenizer
import torch

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

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

print(last_hidden_state.shape)

A torch.Size([3, 768])

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

C torch.Size([1, 768])

D torch.Size([768, 3])

💡 Hint

Remember that BERT outputs batch size, sequence length, and hidden size dimensions.

The last_hidden_state tensor shape is (batch_size, sequence_length, hidden_size). Here batch size is 1, sequence length is 3 tokens (including special tokens), and hidden size is 768.

❓ Model Choice

intermediate

Which model is best suited for sequence classification tasks?

You want to classify movie reviews as positive or negative using Hugging Face Transformers. Which model class should you use?

A BertModel

B BertTokenizer

C BertForSequenceClassification

D BertForMaskedLM

💡 Hint

Look for the model class designed specifically for classification tasks.

BertForSequenceClassification adds a classification head on top of BERT for tasks like sentiment analysis.

❓ Hyperparameter

advanced

Which hyperparameter controls the learning rate in Hugging Face Trainer?

When fine-tuning a transformer model using the Hugging Face Trainer API, which argument sets the learning rate?

A learning_rate

B lr_scheduler_type

C num_train_epochs

D batch_size

💡 Hint

This hyperparameter directly affects how fast the model updates weights.

The 'learning_rate' parameter sets the step size for weight updates during training.

❓ Metrics

advanced

Which metric is most appropriate for evaluating a multi-class text classification model?

You trained a transformer model to classify news articles into 5 categories. Which metric should you use to evaluate its performance?

A Perplexity

B BLEU score

C Mean Squared Error

D Accuracy

💡 Hint

Choose a metric that measures correct label predictions.

Accuracy measures the proportion of correct predictions in classification tasks.

🔧 Debug

expert

What error does this code raise when loading a tokenizer?

Consider this code snippet: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('nonexistent-model')

A OSError: Model 'nonexistent-model' not found

B ValueError: Invalid tokenizer name

C TypeError: from_pretrained() missing required positional argument

D RuntimeError: Tokenizer loading failed due to corrupted files

💡 Hint

Check what happens when you try to load a model that does not exist on Hugging Face hub.

Trying to load a model that does not exist raises an OSError indicating the model was not found.