Sentence transformers in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained sentence transformer model.

Prompt Engineering / GenAI

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('[1]')

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

Abert-base-uncased

Ball-MiniLM-L6-v2

Cresnet50

Dgpt-3

Attempts:

3 left

💡 Hint

Common Mistakes

Using a model name from a different library like GPT or ResNet.

Using a base BERT model which is not fine-tuned for sentence embeddings.

The correct model name for a popular sentence transformer is all-MiniLM-L6-v2. Other options are either not sentence transformer models or unrelated.

2fill in blank

medium

Complete the code to encode a list of sentences into embeddings.

Prompt Engineering / GenAI

sentences = ['Hello world', 'Machine learning is fun']
embeddings = model.[1](sentences)

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

Aencode

Btransform

Cpredict

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' which is for training models, not encoding sentences.

Using 'predict' which is for classification or regression outputs.

The encode method converts sentences into vector embeddings suitable for similarity tasks.

3fill in blank

hard

Fix the error in the code to compute cosine similarity between two sentence embeddings.

Prompt Engineering / GenAI

from sklearn.metrics.pairwise import [1]
similarity = cosine_similarity([emb1], [emb2])[0][0]

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

Acosine_similarity

Beuclidean_distances

Cpairwise_distances

Dmanhattan_distances

Attempts:

3 left

💡 Hint

Common Mistakes

Using distance functions which give dissimilarity, not similarity.

Misspelling the function name.

The function cosine_similarity computes the cosine similarity between vectors, which is the correct metric here.

4fill in blank

hard

Fill both blanks to create a dictionary of sentence embeddings for given sentences.

Prompt Engineering / GenAI

sentences = ['AI is amazing', 'I love coding']
embeddings = model.encode(sentences)
embedding_dict = {sentences[[1]]: embeddings[[2]] for i in range(len(sentences))}

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

Ai

B0

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variables for sentence and embedding indices.

Using fixed indices instead of the loop variable.

We use the loop variable i to index both sentences and embeddings to build the dictionary.

5fill in blank

hard

Fill all three blanks to filter sentences with embeddings having norm greater than 1.0.

Prompt Engineering / GenAI

import numpy as np
filtered = {sent: emb for sent, emb in zip(sentences, embeddings) if np.linalg.[1](emb) [2] [3]

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

Anorm

B>

C1.0

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' instead of 'norm' which sums elements but does not compute vector length.

Using wrong comparison operators.

The np.linalg.norm function computes the length of the embedding vector. We filter embeddings with norm greater than 1.0.

Practice

1. What is the main purpose of sentence transformers in AI?

easy

A. To count the number of words in a sentence

B. To translate sentences from one language to another

C. To convert sentences into numbers that computers can understand

D. To generate new sentences from scratch

Solution

1. Step 1: Understand the role of sentence transformers

   Sentence transformers convert sentences into numerical vectors so computers can process them.

2. Step 2: Compare options with this understanding

   Only To convert sentences into numbers that computers can understand describes this conversion; others describe different tasks.

3. Final Answer:

   To convert sentences into numbers that computers can understand -> Option C

4. Quick Check:

   Sentence transformers = convert sentences to numbers [OK]

Hint: Remember: transformers turn text into numbers [OK]

Common Mistakes:

• Confusing sentence transformers with translation models
• Thinking they generate new sentences
• Assuming they only count words

2. Which of the following is the correct way to import a sentence transformer model in Python?

easy

A. from sentence_transformers import sentence_transformer

B. import SentenceTransformer from sentence_transformers

C. import sentence_transformers.SentenceTransformer

D. from sentence_transformers import SentenceTransformer

Solution

1. Step 1: Recall the correct Python import syntax for sentence transformers

   The correct syntax is 'from sentence_transformers import SentenceTransformer' with exact capitalization.

2. Step 2: Check each option for syntax correctness

   from sentence_transformers import SentenceTransformer matches the correct syntax; others have wrong order, case, or module names.

3. Final Answer:

   from sentence_transformers import SentenceTransformer -> Option D

4. Quick Check:

   Correct import syntax = from sentence_transformers import SentenceTransformer [OK]

Hint: Use 'from module import Class' format for imports [OK]

Common Mistakes:

• Swapping import order
• Using wrong capitalization
• Confusing module and class names

3. What will be the output type of the following code snippet?

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
sentence = 'Hello world'
embedding = model.encode(sentence)
print(type(embedding))

medium

A. <class 'list'>

B. <class 'numpy.ndarray'>

C. <class 'str'>

D. <class 'int'>

Solution

1. Step 1: Understand the output of model.encode()

   The encode method returns a numerical vector as a numpy array representing the sentence embedding.

2. Step 2: Identify the type printed

   Printing type(embedding) shows <class 'numpy.ndarray'> because embeddings are numpy arrays.

3. Final Answer:

   <class 'numpy.ndarray'> -> Option B

4. Quick Check:

   model.encode() output type = numpy.ndarray [OK]

Hint: model.encode returns numpy arrays for embeddings [OK]

Common Mistakes:

• Assuming output is a list
• Thinking output is a string
• Expecting an integer type

4. Identify the error in this code snippet using sentence transformers:

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
sentences = ['Hello world', 'Hi there']
embeddings = model.encode(sentences)
print(embeddings.shape)

medium

A. There is no error; the code runs correctly

B. model.encode() cannot take a list of sentences

C. embeddings does not have a shape attribute

D. The model name 'all-MiniLM-L6-v2' is incorrect

Solution

1. Step 1: Check model name validity

   'all-MiniLM-L6-v2' is a valid pre-trained model name for sentence transformers.

2. Step 2: Verify model.encode() input and output

   model.encode() accepts a list of sentences and returns a numpy array with shape attribute.

3. Step 3: Confirm no errors in code

   All syntax and usage are correct; printing embeddings.shape works as expected.

4. Final Answer:

   There is no error; the code runs correctly -> Option A

5. Quick Check:

   Valid model and input = code runs fine [OK]

Hint: model.encode accepts lists and returns arrays with shape [OK]

Common Mistakes:

• Thinking model.encode only accepts single sentences
• Assuming embeddings lack shape attribute
• Believing model name is invalid

5. You want to find the most similar sentence to 'I love machine learning' from a list using sentence transformers. Which approach is best?

hard

A. Encode all sentences, then use cosine similarity to find the closest embedding

B. Compare sentences by counting common words directly

C. Use a translation model to translate sentences before comparison

D. Manually check each sentence for similarity without encoding

Solution

1. Step 1: Understand the goal of similarity search

   Finding the most similar sentence requires comparing sentence meanings numerically.

2. Step 2: Identify the best method for semantic similarity

   Encoding sentences into embeddings and using cosine similarity is the standard and effective approach.

3. Step 3: Evaluate other options

   Counting words or manual checks ignore meaning; translation is unrelated here.

4. Final Answer:

   Encode all sentences, then use cosine similarity to find the closest embedding -> Option A

5. Quick Check:

   Semantic similarity = encode + cosine similarity [OK]

Hint: Use embeddings + cosine similarity for best sentence matching [OK]

Common Mistakes:

• Relying on word count instead of meaning
• Using translation unnecessarily
• Skipping encoding step