Python NLP ecosystem (NLTK, spaCy, Hugging Face) - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to tokenize a sentence using NLTK.

NLP

import nltk
nltk.download('punkt')
sentence = "Hello world!"
tokens = nltk.word_tokenize([1])
print(tokens)

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

A"Hello world!"

Btokens

Csentence

Dnltk

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the token list instead of the sentence string.

Passing the module name instead of the text.

The word_tokenize function takes a string input to split into words. Here, we pass the variable sentence.

2fill in blank

medium

Complete the code to load the English model in spaCy.

NLP

import spacy
nlp = spacy.load([1])
doc = nlp("This is a test.")
print([token.text for token in doc])

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

A"small_model"

B"english"

C"en"

D"en_core_web_sm"

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect model names like "english" or "en" which are not valid spaCy model names.

Forgetting the quotes around the model name.

spaCy English small model is loaded with the name "en_core_web_sm".

3fill in blank

hard

Fix the error in the Hugging Face pipeline code to perform sentiment analysis.

NLP

from transformers import pipeline
sentiment = pipeline([1])
result = sentiment("I love learning NLP!")
print(result)

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A"sentiment-analysis"

Bsentiment-analysis

C"sentiment_analysis"

Dsentiment_analysis

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the task name without quotes causing a NameError.

Using underscores instead of hyphens in the string.

The pipeline task name must be a string with hyphens, so "sentiment-analysis" is correct.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths for words longer than 3 characters.

NLP

words = ["apple", "is", "good", "for", "you"]
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length as the dictionary value.

Using less than symbol instead of greater than in the condition.

The dictionary maps each word to its length using len(word). The condition filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths for words longer than 3 characters.

NLP

words = ["apple", "is", "good", "for", "you"]
result = [1]: [2] for word in words if len(word) [3] 3
print(result)

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

Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word as key instead of uppercase.

Using less than symbol instead of greater than in the condition.

Swapping keys and values.

The dictionary keys are uppercase words using word.upper(). Values are lengths with len(word). The condition filters words longer than 3 using >.