Text preprocessing (tokenization, stemming, lemmatization) in ML Python - Model Pipeline Trace

Model Pipeline - Text preprocessing (tokenization, stemming, lemmatization)

This pipeline shows how raw text is cleaned and prepared for machine learning. It breaks text into words (tokenization), simplifies words to their root forms (stemming), and converts words to their dictionary forms (lemmatization).

Data Flow - 4 Stages

1Raw Text Input

1000 sentences x variable length→Collect raw sentences from dataset→1000 sentences x variable length

"I am loving the sunny weather today!"

↓

2Tokenization

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

["I", "am", "loving", "the", "sunny", "weather", "today", "!"]

↓

3Stemming

1000 sentences x average 10 tokens→Reduce words to their root forms by chopping endings→1000 sentences x average 10 stemmed tokens

["I", "am", "love", "the", "sunni", "weather", "today", "!"]

↓

4Lemmatization

1000 sentences x average 10 stemmed tokens→Convert words to dictionary base forms using context→1000 sentences x average 10 lemmatized tokens

["I", "be", "love", "the", "sunny", "weather", "today", "!"]

Training Trace - Epoch by Epoch

Loss
1.0 |***************
0.8 |**********     
0.6 |*******        
0.4 |****           
0.2 |**             
0.0 +--------------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.60	Model starts learning with raw text features.
2	0.65	0.72	Tokenization helps model understand word boundaries.
3	0.50	0.80	Stemming reduces word variations, improving learning.
4	0.40	0.85	Lemmatization further refines word forms, boosting accuracy.
5	0.35	0.88	Model converges with stable loss and high accuracy.

Prediction Trace - 4 Layers

Layer 1: Input Raw Sentence

Layer 2: Tokenization

Layer 3: Stemming

Layer 4: Lemmatization

Model Quiz - 3 Questions

What is the main purpose of tokenization in text preprocessing?

A Reduce words to their root forms

B Split text into words or tokens

C Convert words to dictionary base forms

D Remove punctuation from text

Key Insight

Text preprocessing steps like tokenization, stemming, and lemmatization help the model understand and generalize language better by simplifying and structuring raw text data.