Multi-class text classification in NLP - Model Pipeline Trace

Model Pipeline - Multi-class text classification

This pipeline takes text data and teaches a model to sort each text into one of several categories. It cleans the text, turns words into numbers, trains a model to learn patterns, and then predicts categories for new texts.

Data Flow - 6 Stages

1Raw Text Input

1000 rows x 1 column→Collect raw text samples with labels→1000 rows x 1 column

"I love sunny days"

↓

2Text Cleaning

1000 rows x 1 column→Lowercase, remove punctuation and stopwords→1000 rows x 1 column

"love sunny days"

↓

3Tokenization and Vectorization

1000 rows x 1 column→Split text into words and convert to numeric vectors using TF-IDF→1000 rows x 5000 columns

[0, 0, 0.3, ..., 0, 0.1, 0]

↓

4Train/Test Split

1000 rows x 5000 columns→Split data into training (80%) and testing (20%) sets→Train: 800 rows x 5000 columns, Test: 200 rows x 5000 columns

Train sample vector: [0, 0, 0.3, ..., 0, 0.1, 0]

↓

5Model Training

Train: 800 rows x 5000 columns→Train a neural network classifier with softmax output→Trained model

Model learns to map vectors to one of 5 classes

↓

6Prediction

Test: 200 rows x 5000 columns→Model predicts class probabilities for each test sample→200 rows x 5 columns (class probabilities)

[0.1, 0.7, 0.05, 0.1, 0.05]

Training Trace - Epoch by Epoch

Loss
1.5 |************
1.2 |*********
1.0 |*******
0.85|******
0.75|*****
0.68|****
0.62|***
0.58|**
0.55|*
0.53|

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.5	0.40	Model starts learning, accuracy is low
2	1.2	0.55	Loss decreases, accuracy improves
3	1.0	0.65	Model learns better patterns
4	0.85	0.72	Steady improvement in accuracy
5	0.75	0.78	Model converging well
6	0.68	0.82	Good accuracy reached
7	0.62	0.85	Model fine-tuning
8	0.58	0.87	Accuracy nearing peak
9	0.55	0.88	Small improvements
10	0.53	0.89	Training stabilizes

Prediction Trace - 4 Layers

Layer 1: Input Vector

Layer 2: Hidden Layer with ReLU

Layer 3: Output Layer with Softmax

Layer 4: Prediction

Model Quiz - 3 Questions

What happens to the data shape after tokenization and vectorization?

A It changes from 1000 rows x 1 column to 1000 rows x 5000 columns

B It changes from 1000 rows x 5000 columns to 1000 rows x 1 column

C It stays the same at 1000 rows x 1 column

D It changes to 5000 rows x 1000 columns

Key Insight

This visualization shows how text data is transformed into numbers, then a model learns to classify texts into multiple categories by improving accuracy and lowering loss over time. The softmax layer helps the model choose the most likely class by turning scores into probabilities.