Word embeddings concept (Word2Vec) in ML Python - Model Pipeline Trace

Model Pipeline - Word embeddings concept (Word2Vec)

This pipeline shows how Word2Vec learns to turn words into numbers that capture their meaning. It starts with text data, processes it, trains a model to predict nearby words, and creates word vectors that help computers understand language.

Data Flow - 4 Stages

1Raw Text Data

10000 sentences x variable length→Collect sentences from a text corpus→10000 sentences x variable length

"The cat sat on the mat."

↓

2Tokenization

10000 sentences x variable length→Split sentences into words (tokens)→10000 sentences x variable length tokens

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

↓

3Context Window Creation

10000 sentences x variable length tokens→Create pairs of (target word, context word) within a window size (e.g., 2)→Approx. 50000 word pairs

("cat", "The"), ("cat", "sat"), ("sat", "cat"), ("sat", "on")

↓

4Model Training Input

50000 word pairs→Convert words to one-hot vectors for input and output→50000 pairs of one-hot vectors (vocab_size dimension)

Input: one-hot for "cat", Output: one-hot for "sat"

Training Trace - Epoch by Epoch

5.5 | *
5.0 | *
4.5 | 
4.0 |  *
3.5 |   *
3.0 |    *
2.5 |     *
2.0 |      *
1.5 |       *
1.0 |        
     +----------------
      1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	5.2	0.12	Model starts learning word relationships, loss is high, accuracy low.
2	3.8	0.25	Loss decreases as model better predicts context words.
3	2.7	0.40	Model improves, capturing more word context patterns.
4	2.0	0.55	Loss continues to drop, accuracy rises steadily.
5	1.5	0.65	Model converges, learning meaningful word embeddings.

Prediction Trace - 4 Layers

Layer 1: Input Word One-Hot Encoding

Layer 2: Hidden Layer (Embedding Lookup)

Layer 3: Output Layer (Context Word Prediction)

Layer 4: Softmax Activation

Model Quiz - 3 Questions

What does the embedding vector represent in Word2Vec?

A A dense numeric representation capturing word meaning

B A one-hot vector with a single 1

C The raw text of the word

D The frequency count of the word

Key Insight

Word2Vec learns word meanings by predicting nearby words. It converts words into dense vectors that capture relationships, making language understandable for computers.