Pre-trained embedding usage in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained embedding model using gensim.

NLP

from gensim.models import KeyedVectors
embedding_model = KeyedVectors.load_word2vec_format('[1]', binary=True)

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

Aglove.6B.100d.txt

BGoogleNews-vectors-negative300.bin

Cfasttext.vec

Drandom_vectors.txt

Attempts:

3 left

💡 Hint

Common Mistakes

Using a text file instead of a binary file for Word2Vec loading.

Confusing GloVe or FastText files with Word2Vec binary format.

The GoogleNews-vectors-negative300.bin file is a common pre-trained Word2Vec binary format model used with gensim.

2fill in blank

medium

Complete the code to get the vector for the word 'apple' from the embedding model.

NLP

vector = embedding_model['[1]']

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Aapple

Bfruit

Cbanana

Dorange

Attempts:

3 left

💡 Hint

Common Mistakes

Using a different word than 'apple'.

Not using quotes around the word.

To get the vector for the word 'apple', you access the embedding model with the key 'apple'.

3fill in blank

hard

Fix the error in the code to check if the word 'computer' is in the embedding model vocabulary.

NLP

if '[1]' in embedding_model.key_to_index:
    print('Found')
else:
    print('Not found')

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

Avocab

Bembedding_model

Ckey_to_index

Dcomputer

Attempts:

3 left

💡 Hint

Common Mistakes

Checking the wrong variable or attribute.

Not using quotes around the word.

To check if 'computer' is in the vocabulary, you check if the string 'computer' is in embedding_model.key_to_index.

4fill in blank

hard

Fill both blanks to create a dictionary of word vectors for words longer than 5 characters.

NLP

word_vectors = {word: embedding_model[word] for word in embedding_model.key_to_index if [1] [2] 5}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong comparison operator.

Using the word variable instead of its length.

We use len(word) to get the length of the word and check if it is greater than 5.

5fill in blank

hard

Fill all three blanks to create a list of cosine similarities between 'king' and other words starting with 'q'.

NLP

from gensim.matutils import [1]
similarities = [[2](embedding_model['king'], embedding_model[word]) for word in embedding_model.key_to_index if word.startswith('[3]')]

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

Acossim

Cq

Dcosine

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong function name for cosine similarity.

Not filtering words starting with 'q'.

The function cossim computes cosine similarity. We use it to compare 'king' with words starting with 'q'.

Practice

1. What is the main benefit of using pre-trained embeddings in NLP tasks?

easy

A. They only work for images, not text.

B. They generate random word vectors for each run.

C. They replace the need for any model training.

D. They provide ready-made word meanings, saving training time.

Solution

1. Step 1: Understand what pre-trained embeddings are

   Pre-trained embeddings are word vectors learned from large text data before your task.

2. Step 2: Identify their benefit

   They save time because you don't train word meanings from scratch, improving efficiency.

3. Final Answer:

   They provide ready-made word meanings, saving training time. -> Option D

4. Quick Check:

   Pre-trained embeddings = ready-made word meanings [OK]

Hint: Pre-trained means already learned word meanings [OK]

Common Mistakes:

• Thinking embeddings generate random vectors each time
• Believing embeddings remove all model training
• Confusing embeddings with image features

2. Which Python code correctly loads a pre-trained embedding file named glove.txt into a dictionary called embeddings?

easy

A. embeddings = open('glove.txt').split()

B. embeddings = open('glove.txt').read()

C. embeddings = {line.split()[0]: list(map(float, line.split()[1:])) for line in open('glove.txt')}

D. embeddings = dict(open('glove.txt'))

Solution

1. Step 1: Understand the file format

   Each line has a word followed by numbers (vector components).

2. Step 2: Choose code that maps words to vectors

   embeddings = {line.split()[0]: list(map(float, line.split()[1:])) for line in open('glove.txt')} splits each line, uses first part as key, rest as float list values.

3. Final Answer:

   embeddings = {line.split()[0]: list(map(float, line.split()[1:])) for line in open('glove.txt')} -> Option C

4. Quick Check:

   Dictionary comprehension with split and float conversion = embeddings = {line.split()[0]: list(map(float, line.split()[1:])) for line in open('glove.txt')} [OK]

Hint: Use dict comprehension with split and float conversion [OK]

Common Mistakes:

• Using read() returns a string, not a dict
• Trying to split on file object directly
• Passing file object to dict() without processing

3. Given the code below, what will print(embeddings['cat']) output if glove.txt contains the line cat 0.1 0.2 0.3?

embeddings = {line.split()[0]: list(map(float, line.split()[1:])) for line in open('glove.txt')}
print(embeddings['cat'])

medium

A. [0.1, 0.2, 0.3]

B. 'cat 0.1 0.2 0.3'

C. ['cat', 0.1, 0.2, 0.3]

D. KeyError

Solution

1. Step 1: Understand dictionary comprehension

   Each word maps to a list of floats from the line after splitting.

2. Step 2: Check the key 'cat'

   It maps to [0.1, 0.2, 0.3] as floats in a list.

3. Final Answer:

   [0.1, 0.2, 0.3] -> Option A

4. Quick Check:

   embeddings['cat'] = float list [OK]

Hint: Split line, first word key, rest floats list [OK]

Common Mistakes:

• Expecting string instead of float list
• Confusing key with value
• Assuming KeyError without checking file content

4. The code below tries to load embeddings but causes type issues. What is the likely cause?

embeddings = {}
with open('glove.txt') as f:
    for line in f:
        word, vector = line.split()[0], line.split()[1:]
        embeddings[word] = vector
print(type(embeddings['dog'][0]))

medium

A. The file path 'glove.txt' is incorrect.

B. The vector values are strings, not floats, causing type issues.

C. The dictionary keys are not unique.

D. The print statement syntax is wrong.

Solution

1. Step 1: Analyze vector assignment

   Vector is assigned as list of strings from split, not converted to floats.

2. Step 2: Check print type

   Printing type of embeddings['dog'][0] shows string, not float, which may cause errors later.

3. Final Answer:

   The vector values are strings, not floats, causing type issues. -> Option B

4. Quick Check:

   Missing float conversion = The vector values are strings, not floats, causing type issues. [OK]

Hint: Convert vector strings to floats before storing [OK]

Common Mistakes:

• Ignoring need to convert strings to floats
• Assuming file path error without checking
• Thinking keys must be unique error

5. You want to use pre-trained embeddings in a text classification model. Which step is essential to correctly use these embeddings in your model's input layer?

hard

A. Map each word in your text to its embedding vector and create a matrix input.

B. Train embeddings from scratch ignoring pre-trained vectors.

C. Replace all words with their index positions only.

D. Use embeddings only for output layer predictions.

Solution

1. Step 1: Understand embedding usage in models

   Pre-trained embeddings provide vector representations for words to input into models.

2. Step 2: Identify correct input preparation

   Mapping words to their vectors and forming a matrix is needed to feed the model.

3. Final Answer:

   Map each word in your text to its embedding vector and create a matrix input. -> Option A

4. Quick Check:

   Embedding vectors as input = Map each word in your text to its embedding vector and create a matrix input. [OK]

Hint: Convert words to vectors matrix before model input [OK]

Common Mistakes:

• Ignoring pre-trained vectors and training from scratch
• Using word indices without embeddings
• Applying embeddings only at output layer