Tokenization (word and sentence) in NLP - Model Pipeline Trace

Model Pipeline - Tokenization (word and sentence)

This pipeline breaks down text into smaller pieces called tokens. It splits text into sentences first, then splits each sentence into words. This helps computers understand and work with text better.

Data Flow - 3 Stages

1Input Text

1 text string→Raw text input→1 text string

"Hello world! How are you today?"

↓

2Sentence Tokenization

1 text string→Split text into sentences using punctuation marks→2 sentences

["Hello world!", "How are you today?"]

↓

3Word Tokenization

2 sentences→Split each sentence into words by spaces and punctuation→List of word lists (2 lists)

[["Hello", "world", "!"], ["How", "are", "you", "today", "?"]]

Training Trace - Epoch by Epoch

No training loss to show because tokenization is a fixed process.

Epoch	Loss ↓	Accuracy ↑	Observation
1	N/A	N/A	Tokenization is a rule-based process, no training needed.

Prediction Trace - 3 Layers

Layer 1: Input Text

Layer 2: Sentence Tokenization

Layer 3: Word Tokenization

Model Quiz - 3 Questions

What does sentence tokenization do?

A Splits text into sentences

B Splits sentences into words

C Removes punctuation

D Converts words to numbers

Key Insight

Tokenization breaks text into manageable pieces without learning from data. It prepares text for further analysis by splitting it into sentences and words, making it easier for machines to understand language.