Why spaCy is production-grade NLP - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a spaCy model for English.

NLP

import spacy
nlp = spacy.load('[1]')

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

Aenglish_model

Bspacy_en

Cen_nlp

Den_core_web_sm

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect or made-up model names.

Forgetting to install the model before loading.

The correct model name to load the small English model in spaCy is en_core_web_sm.

2fill in blank

medium

Complete the code to process text and get tokens using spaCy.

NLP

doc = nlp('[1]')
tokens = [token.text for token in doc]

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Atoken.text

Bnlp

CThis is a test.

Ddoc

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the nlp object itself instead of text.

Passing token attributes instead of raw text.

You need to pass a string of text to the nlp object to process it and get tokens.

3fill in blank

hard

Fix the error in the code to get named entities from a spaCy doc.

NLP

for ent in doc.[1]:
    print(ent.text, ent.label_)

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Aentities

Bents

Ctokens

Dnamed_entities

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'entities' or 'named_entities' which do not exist.

Trying to iterate over 'tokens' for entities.

The correct attribute to access named entities in a spaCy doc is ents.

4fill in blank

hard

Fill both blanks to create a dictionary of token texts and their parts of speech.

NLP

pos_dict = {token.[1]: token.[2] for token in doc}

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Atext

Bpos_

Clemma_

Dtag_

Attempts:

3 left

💡 Hint

Common Mistakes

Using lemma_ instead of text for keys.

Using tag_ instead of pos_ for parts of speech.

Use text for token text and pos_ for part of speech tags.

5fill in blank

hard

Fill all three blanks to filter tokens that are alphabetic and lowercase their text.

NLP

filtered = [token.[1].lower() for token in doc if token.[2] and not token.[3]]

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

Atext

Bis_alpha

Cis_stop

Dis_punct

Attempts:

3 left

💡 Hint

Common Mistakes

Using is_stop instead of is_punct to exclude tokens.

Not converting text to lowercase.

We use text to get token text, is_alpha to check if token is alphabetic, and exclude punctuation with is_punct.