🔍 What You’ll Learn Today
How unstructured text is converted into structured sequences
The role of tokenization, vocabulary, and n-grams in NLP pipelines
Hands-on examples with a link to an interactive Colab notebook
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Visit SlayItCoder Blogsite for more practical AI/ML tutorials, weekend logs, and hands-on coding guides.
🧩 1. From Raw Text to Sequence
Think of raw text like uncut marble — beautiful, but unstructured. Machines can’t make sense of it until we break it down.
Example:
"The cat sat on the mat."
→ ["the", "cat", "sat", "on", "the", "mat"]
→ [0, 1, 2, 3, 0, 4]
🎯 Try this: Take a sentence from your favorite movie and break it into words. Then assign numbers to each word!
✂️ 2. Tokenization: Slicing Sentences Into Symbols
Tokenization breaks text into manageable pieces called tokens.
Types of Tokenization:
Word: "I love NLP" →
["I", "love", "NLP"]Subword (BPE): "unhappiness" →
["un", "happiness"]Character: "NLP" →
["N", "L", "P"]
🎯 Try this: Use Python to tokenize a string into characters, words, and subwords. Which gives more flexibility?
📖 3. Vocabulary: Teaching Words to a Machine
A vocabulary maps each unique token to a unique ID.
Example:
Corpus: ["I love NLP", "NLP is fun"]
Vocabulary: {"I": 0, "love": 1, "NLP": 2, "is": 3, "fun": 4}
🎯 Try this: Build a vocabulary from your recent chat or email thread.
📊 4. N-Grams: Short-Term Memory in Text
N-grams capture local context by grouping consecutive words together.
Example:
Sentence: "I love NLP"
Unigrams:
["I", "love", "NLP"]Bigrams:
["I love", "love NLP"]Trigrams:
["I love NLP"]
🎯 Try this: Create bigrams manually from any sentence. See how context improves.
🔁 Summary Flow
Raw Text → Cleaned → Tokenized → Vocabulary IDs → N-Grams → Model Input
🎯 Try It Yourself in Colab
We’ve bundled all the code into a hands-on Colab notebook for you to explore.
👉 Open Notebook: Tokenization & N-Grams ➜
📌 Action Checklist
Tokenize a tweet using word-level and character-level
Build a vocabulary from any document
Generate bigrams and trigrams from your own sentence
Compare how models react to different token types
📚 Weekend Read
Speech and Language Processing by Jurafsky & Martin
📌 Coming Next: Autoregressive Models & Time Series Forecasting
We’ve structured the text. Next, let’s learn how machines predict the next value or word in a sequence.