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)

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

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)

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

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 >.

Practice

1. Which Python library is best known for providing pre-trained models for advanced NLP tasks?

easy

A. NLTK

B. Hugging Face

C. spaCy

D. Scikit-learn

Solution

1. Step 1: Understand the role of each library

   NLTK is mainly for learning and basic NLP tasks, spaCy is for fast real-world processing, and Hugging Face offers powerful pre-trained models.

2. Step 2: Identify the library specialized in pre-trained models

   Hugging Face is known for its large collection of pre-trained transformer models for advanced NLP.

3. Final Answer:

   Hugging Face -> Option B

4. Quick Check:

   Pre-trained models = Hugging Face [OK]

Hint: Remember: Hugging Face = pre-trained models [OK]

Common Mistakes:

• Confusing NLTK as the source of pre-trained models
• Thinking spaCy provides many pre-trained transformer models
• Choosing Scikit-learn which is not specialized for NLP

2. Which of the following is the correct way to import the English language model in spaCy?

easy

A. import spacy; nlp = spacy.load('en_core_web_sm')

B. import spacy; nlp = spacy.load('english')

C. from spacy import English; nlp = English()

D. import spacy; nlp = spacy.load('en')

Solution

1. Step 1: Recall spaCy's model loading syntax

   spaCy loads models using spacy.load() with the model name like 'en_core_web_sm'.

2. Step 2: Check each option's syntax

   import spacy; nlp = spacy.load('en_core_web_sm') uses the correct model name for the small English core model. 'en' loads a blank model without components, 'english' is not a valid model name, and from spacy import English; nlp = English() only initializes a basic tokenizer without trained pipelines.

3. Final Answer:

   import spacy; nlp = spacy.load('en_core_web_sm') -> Option A

4. Quick Check:

   spaCy model load = spacy.load('en_core_web_sm') [OK]

Hint: Use spacy.load('en_core_web_sm') to load English model [OK]

Common Mistakes:

• Using 'english' or 'en' instead of 'en_core_web_sm'
• Trying to import English class instead of loading model
• Forgetting to install the model before loading

3. What will be the output of this NLTK code snippet?

import nltk
from nltk.tokenize import word_tokenize
text = "Hello world!"
tokens = word_tokenize(text)
print(tokens)

medium

A. ['Hello world!']

B. ['Hello', 'world']

C. ['Hello', 'world!']

D. ['Hello', 'world', '!']

Solution

1. Step 1: Understand word_tokenize behavior

   NLTK's word_tokenize splits text into words and punctuation separately.

2. Step 2: Apply tokenization to 'Hello world!'

   The text splits into three tokens: 'Hello', 'world', and '!'.

3. Final Answer:

   ['Hello', 'world', '!'] -> Option D

4. Quick Check:

   word_tokenize splits punctuation separately [OK]

Hint: word_tokenize splits punctuation as separate tokens [OK]

Common Mistakes:

• Expecting punctuation to stay attached to words
• Confusing tokenization with simple split()
• Ignoring that '!' is a separate token

4. Identify the error in this Hugging Face transformers code snippet:

from transformers import pipeline
classifier = pipeline('sentiment-analysis')
result = classifier('I love NLP!')
print(result[0])

medium

A. Missing model download before pipeline creation

B. Incorrect pipeline task name

C. No error, code runs correctly

D. Result indexing should be result[1]

Solution

1. Step 1: Check pipeline usage

   The pipeline function with 'sentiment-analysis' is correct and downloads the default model automatically if needed.

2. Step 2: Verify result usage

   The classifier returns a list of dicts; accessing result[0] is correct to get the first prediction.

3. Final Answer:

   No error, code runs correctly -> Option C

4. Quick Check:

   Hugging Face pipeline auto-downloads models [OK]

Hint: Hugging Face pipelines auto-download models [OK]

Common Mistakes:

• Thinking model must be downloaded manually first
• Using wrong pipeline task name
• Accessing wrong index of result list

5. You want to extract named entities from a text quickly and accurately. Which combination of tools and steps is best?

hard

A. Use spaCy's pre-trained model with nlp = spacy.load('en_core_web_sm') and then nlp(text).ents

B. Use NLTK's word_tokenize and then manually match entity patterns

C. Use Hugging Face pipeline('ner') without loading any model

D. Use spaCy's tokenizer only and ignore entity recognition

Solution

1. Step 1: Identify fast and accurate named entity extraction

   spaCy provides pre-trained models that include named entity recognition (NER) ready to use.

2. Step 2: Evaluate options for NER

   Use spaCy's pre-trained model with nlp = spacy.load('en_core_web_sm') and then nlp(text).ents uses spaCy's model and extracts entities with nlp(text).ents, which is efficient and accurate. Use NLTK's word_tokenize and then manually match entity patterns requires manual pattern matching, which is slow and error-prone. Use Hugging Face pipeline('ner') without loading any model misses loading a model explicitly, which is needed. Use spaCy's tokenizer only and ignore entity recognition ignores entity recognition.

3. Final Answer:

   Use spaCy's pre-trained model with nlp = spacy.load('en_core_web_sm') and then nlp(text).ents -> Option A

4. Quick Check:

   spaCy pre-trained models = fast NER [OK]

Hint: spaCy pre-trained models provide fast named entity recognition [OK]

Common Mistakes:

• Trying to do NER manually with NLTK tokens
• Using pipeline('ner') without model loading
• Ignoring entity extraction step