Visualizing embeddings (t-SNE) in NLP - Model Pipeline Trace

Model Pipeline - Visualizing embeddings (t-SNE)

This pipeline shows how word embeddings from text data are transformed and visualized using t-SNE, a method that helps us see high-dimensional data in 2D. It helps us understand how similar words group together.

Data Flow - 5 Stages

1Raw Text Data

1000 sentences x variable length→Collect sentences containing words→1000 sentences x variable length

"The cat sat on the mat."

↓

2Tokenization

1000 sentences x variable length→Split sentences into words (tokens)→1000 sentences x average 10 tokens

["The", "cat", "sat", "on", "the", "mat"]

↓

3Embedding Lookup

1000 sentences x 10 tokens→Convert each token to a 50-dimensional vector→1000 sentences x 10 tokens x 50 features

[[0.1, -0.2, ..., 0.05], ..., [0.3, 0.0, ..., -0.1]]

↓

4Average Pooling

1000 sentences x 10 tokens x 50 features→Average token vectors to get sentence embedding→1000 sentences x 50 features

[0.12, -0.05, ..., 0.07]

↓

5t-SNE Dimensionality Reduction

1000 sentences x 50 features→Reduce 50D embeddings to 2D for visualization→1000 sentences x 2 features

[[12.3, -5.6], [7.8, 3.4], ...]

Training Trace - Epoch by Epoch

Loss
1.0 |          *
0.9 |         * 
0.8 |        *  
0.7 |       *   
0.6 |      *    
0.5 |     **    
0.4 |    *      
    +------------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	N/A	Initial embedding vectors generated; no training loss as embeddings are pre-trained.
2	0.75	N/A	t-SNE starts organizing points; loss decreases indicating better neighborhood preservation.
3	0.60	N/A	Clusters of similar words start to form in 2D space.
4	0.50	N/A	t-SNE converges; loss stabilizes and clusters become clearer.
5	0.48	N/A	Final embedding visualization ready; minimal loss improvement.

Prediction Trace - 4 Layers

Layer 1: Tokenization

Layer 2: Embedding Lookup

Layer 3: Average Pooling

Layer 4: t-SNE Dimensionality Reduction

Model Quiz - 3 Questions

What does the t-SNE step do in this pipeline?

A Reduces high-dimensional embeddings to 2D for visualization

B Converts words into vectors

C Splits sentences into words

D Averages token vectors

Key Insight

This visualization shows how t-SNE helps us understand complex word or sentence embeddings by reducing their dimensions to 2D. It reveals groups of similar meanings, making abstract data easier to grasp.