[Solved] Identify the error in this code that tries to compute semantic similarity: from sklearn.metrics.pairwise import cosine_similarity emb1 = [0.1, 0.2, 0.3] emb2 = [0.1, 0.2, 0.... | NLP

NLP - Text Similarity and Search

Identify the error in this code that tries to compute semantic similarity:

from sklearn.metrics.pairwise import cosine_similarity

emb1 = [0.1, 0.2, 0.3]
emb2 = [0.1, 0.2, 0.3]
sim = cosine_similarity(emb1, emb2)
print(sim)

Aemb1 and emb2 should be 2D arrays, not 1D lists

Bcosine_similarity function does not exist in sklearn

Cembeddings must be strings, not numbers

Dprint statement syntax is incorrect

Step-by-Step Solution

Solution:

Step 1: Check input format for cosine_similarity
cosine_similarity expects 2D arrays (like [[...]]), but emb1 and emb2 are 1D lists.
Step 2: Confirm other options
cosine_similarity exists, embeddings are numeric vectors, and print syntax is correct in Python 3.
Final Answer:
emb1 and emb2 should be 2D arrays, not 1D lists -> Option A
Quick Check:
Input shape must be 2D arrays [OK]

Quick Trick: cosine_similarity needs 2D arrays, not 1D lists [OK]

Common Mistakes:

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