GloVe embeddings in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load GloVe embeddings from a file.

NLP

glove_vectors = {}
with open('glove.txt', 'r', encoding='utf8') as f:
    for line in f:
        values = line.split()
        word = values[0]
        vector = list(map(float, values[[1]]))
        glove_vectors[word] = vector

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

A3:

B0:

C2:

D1:

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0: which includes the word as a float and causes an error.

Using 2: or 3: which skips some vector values.

The first value in each line is the word, so the vector starts from index 1 onward.

2fill in blank

medium

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

NLP

word = 'apple'
embedding = glove_vectors.get([1], None)

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

A'apple'

B'APPLE'

C'apples'

D'Apple'

Attempts:

3 left

💡 Hint

Common Mistakes

Using uppercase or capitalized versions that do not exist in the dictionary.

Using plural form 'apples' which may not be in the embeddings.

GloVe embeddings are case sensitive and usually lowercase, so 'apple' is correct.

3fill in blank

hard

Fix the error in the code to compute cosine similarity between two GloVe vectors.

NLP

import numpy as np

def cosine_similarity(vec1, vec2):
    dot_product = np.dot(vec1, vec2)
    norm1 = np.linalg.norm(vec1)
    norm2 = np.linalg.norm([1])
    return dot_product / (norm1 * norm2)

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

Avec2

Bvec1 - vec2

Cvec1

Dvec1 + vec2

Attempts:

3 left

💡 Hint

Common Mistakes

Using the first vector twice causing incorrect similarity values.

Using vector addition or subtraction instead of the second vector.

The norm of the second vector should be computed using vec2 to calculate cosine similarity correctly.

4fill in blank

hard

Fill both blanks to create a dictionary of words with vectors longer than 50 dimensions.

NLP

filtered_glove = {word: vec for word, vec in glove_vectors.items() if len(vec) [1] [2]

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

A>

B50

C<

D100

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' which filters vectors shorter than 50.

Using 100 which is not the intended threshold.

We want vectors with length greater than 50, so use '>' and 50.

5fill in blank

hard

Fill all three blanks to create a list of words whose embedding vectors have norm greater than 1.0.

NLP

import numpy as np

words_with_norm = [word for word, vec in glove_vectors.items() if np.linalg.norm(vec) [1] [2]]

result = words_with_norm[:[3]]

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

A>

B1.0

C100

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' which selects vectors with norm less than 1.0.

Using wrong slice number causing too few or too many words.

We check if norm is greater than 1.0 and then take the first 100 words.

Practice

1. What is the main purpose of GloVe embeddings in natural language processing?

easy

A. To generate random text based on input

B. To translate text from one language to another

C. To count the frequency of words in a document

D. To convert words into numerical vectors that capture meaning and relationships

Solution

1. Step 1: Understand what embeddings do

   Embeddings convert words into numbers so machines can understand text.

2. Step 2: Identify GloVe's role

   GloVe embeddings specifically capture word meanings and relationships in vector form.

3. Final Answer:

   To convert words into numerical vectors that capture meaning and relationships -> Option D

4. Quick Check:

   GloVe = word vectors capturing meaning [OK]

Hint: Remember: embeddings = words to numbers showing meaning [OK]

Common Mistakes:

• Confusing embeddings with translation
• Thinking embeddings count word frequency
• Assuming embeddings generate text

2. Which of the following is the correct way to load pre-trained GloVe embeddings in Python using the gensim library?

easy

A. glove = gensim.models.FastText.load('glove.txt')

B. glove = gensim.models.Word2Vec.load('glove.txt')

C. glove = gensim.models.KeyedVectors.load_word2vec_format('glove.txt', binary=False)

D. glove = gensim.load('glove.txt')

Solution

1. Step 1: Recall GloVe loading method

   GloVe embeddings are loaded as KeyedVectors using load_word2vec_format with binary=False.

2. Step 2: Check options for correct syntax

   glove = gensim.models.KeyedVectors.load_word2vec_format('glove.txt', binary=False) uses the correct function and parameters for GloVe format.

3. Final Answer:

   glove = gensim.models.KeyedVectors.load_word2vec_format('glove.txt', binary=False) -> Option C

4. Quick Check:

   Use load_word2vec_format with binary=False for GloVe [OK]

Hint: Use load_word2vec_format with binary=False for GloVe files [OK]

Common Mistakes:

• Using Word2Vec.load for GloVe files
• Forgetting binary=False parameter
• Using FastText load for GloVe

3. Given the following Python code snippet using pre-trained GloVe embeddings, what will be the output?

from gensim.models import KeyedVectors

glove = KeyedVectors.load_word2vec_format('glove.6B.50d.txt', binary=False)
result = glove.similarity('king', 'queen')
print(round(result, 2))

medium

A. 0.00

B. 0.78

C. 1.00

D. -0.50

Solution

1. Step 1: Understand similarity method

   The similarity method returns a cosine similarity score between two word vectors, usually between 0 and 1 for related words.

2. Step 2: Interpret expected similarity for 'king' and 'queen'

   These words are closely related, so the similarity is high but less than 1, typically around 0.78.

3. Final Answer:

   0.78 -> Option B

4. Quick Check:

   Similarity('king','queen') ≈ 0.78 [OK]

Hint: Related words have similarity close to but less than 1 [OK]

Common Mistakes:

• Assuming similarity is always 1 for related words
• Confusing similarity with distance
• Expecting negative similarity for related words

4. You try to find the vector for the word 'unseenword' using GloVe embeddings with this code:

vector = glove['unseenword']

But it raises a KeyError. What is the best way to fix this error?

medium

A. Check if the word exists in the embeddings before accessing it

B. Use glove.get_vector('unseenword') without checking

C. Ignore the error and continue

D. Restart the Python kernel

Solution

1. Step 1: Understand cause of KeyError

   The word 'unseenword' is not in the GloVe vocabulary, so direct access raises KeyError.

2. Step 2: Use safe access method

   Check if the word exists using 'if word in glove' before accessing to avoid errors.

3. Final Answer:

   Check if the word exists in the embeddings before accessing it -> Option A

4. Quick Check:

   Check word presence before access to avoid KeyError [OK]

Hint: Always check word in embeddings before access [OK]

Common Mistakes:

• Trying to access vectors without checking existence
• Ignoring errors instead of handling them
• Restarting kernel does not fix missing words

5. You want to improve a text classification model by using GloVe embeddings. Which approach best combines GloVe vectors with your model to handle words not in the GloVe vocabulary?

hard

A. Initialize an embedding layer with GloVe vectors and allow it to be trainable with random vectors for unknown words

B. Use only GloVe vectors and ignore unknown words during training

C. Replace unknown words with a fixed zero vector and freeze the embedding layer

D. Train a new embedding from scratch without using GloVe

Solution

1. Step 1: Understand embedding layer initialization

   Initializing with GloVe vectors provides good starting word representations.

2. Step 2: Handle unknown words and training

   Allowing the embedding layer to be trainable lets the model learn vectors for unknown words starting from random initialization.

3. Final Answer:

   Initialize an embedding layer with GloVe vectors and allow it to be trainable with random vectors for unknown words -> Option A

4. Quick Check:

   Trainable embeddings + GloVe + random unknown vectors = best practice [OK]

Hint: Use trainable embeddings with GloVe plus random unknown vectors [OK]

Common Mistakes:

• Ignoring unknown words instead of learning their vectors
• Freezing embeddings and losing adaptability
• Not using pre-trained GloVe vectors at all