Semantic similarity with embeddings in NLP - Model Pipeline Trace

Model Pipeline - Semantic similarity with embeddings

This pipeline shows how we use word or sentence embeddings to find how similar two pieces of text are. We turn text into numbers, then compare those numbers to get a similarity score.

Data Flow - 4 Stages

1Input Text

2 sentences→Receive two sentences to compare→2 sentences

"I love apples." and "I enjoy eating fruit."

↓

2Text Preprocessing

2 sentences→Lowercase and remove punctuation→2 cleaned sentences

"i love apples" and "i enjoy eating fruit"

↓

3Embedding Generation

2 cleaned sentences→Convert each sentence to a 300-dimensional vector using a pretrained embedding model→2 vectors of shape (300,)

[0.12, -0.05, ..., 0.33] and [0.10, -0.02, ..., 0.30]

↓

4Similarity Calculation

2 vectors of shape (300,)→Calculate cosine similarity between the two vectors→1 similarity score (float between -1 and 1)

0.87

Training Trace - Epoch by Epoch

Loss
0.5 |****
0.4 |****
0.3 |****
0.2 |****
0.1 |
    +----
    1 2 3 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.6	Model starts learning to map sentences to vectors that reflect meaning.
2	0.3	0.75	Loss decreases and accuracy improves as embeddings better capture similarity.
3	0.2	0.85	Model converges with good semantic similarity detection.

Prediction Trace - 3 Layers

Layer 1: Input Sentences

Layer 2: Embedding Model

Layer 3: Cosine Similarity

Model Quiz - 3 Questions

What does the embedding model output for each sentence?

A A similarity score between sentences

B A cleaned text sentence

C A vector of numbers representing the sentence meaning

D A list of words in the sentence

Key Insight

Using embeddings transforms text into numbers that capture meaning. Comparing these numbers with cosine similarity helps us find how close two sentences are in meaning, which is useful for many language tasks.