NLP & Transformers
Teach machines to understand, analyse, and generate human language — from tokenisation to BERT and GPT.
Start Learning NLP & TransformersWhat You'll Learn
- What NLP is and what problems it solves in the real world
- How text is preprocessed: tokenisation, stemming, and embeddings
- Classic NLP tasks: sentiment analysis, named entity recognition, text classification
- How the Transformer architecture and self-attention work (with intuition)
- How to use HuggingFace Transformers to run pre-trained models
- How to fine-tune BERT on your own classification dataset
- How word embeddings (Word2Vec, GloVe, sentence-transformers) work
- How to build a semantic search engine using sentence embeddings
Introduction to NLP & Transformers
Natural Language Processing (NLP) is the field of AI that enables computers to understand, interpret, and generate human language. Every time you use Google Search, get a spam email filtered, or interact with an AI assistant, NLP is at work. Modern NLP is powered by Transformer models — the architecture introduced in the famous "Attention Is All You Need" paper (2017) that now powers BERT, GPT, T5, and virtually every state-of-the-art language model.
Before Transformers, NLP relied on hand-crafted rules and simpler models like RNNs that processed text sequentially — one word at a time. Transformers changed everything by using "self-attention" to look at all words in a sentence simultaneously and understand how each word relates to every other word. This made them dramatically more powerful and allowed training on vastly larger datasets.
Today, you don't need to train these models from scratch. HuggingFace provides thousands of pre-trained models that you can download and fine-tune on your own data with just a few lines of Python. Whether you want to classify customer reviews, extract named entities, summarise documents, or build a chatbot, there's likely a model already trained for it.
Video Tutorials
Handpicked free YouTube videos to accelerate your understanding
Attention in transformers, step by step
Visual deep-dive into the self-attention mechanism — the key innovation behind ChatGPT, BERT, and all modern language models.
HuggingFace NLP Course — Getting Started
Official HuggingFace beginner course — tokenizers, pipelines, and fine-tuning BERT for text classification with real Python code.
Run NLP tasks with HuggingFace Transformers in 5 lines
Copy the code below and paste it into your Python environment or our free online compiler.
# Install: pip install transformers torch
from transformers import pipeline
# 1. Sentiment Analysis — is this review positive or negative?
sentiment = pipeline("sentiment-analysis")
result = sentiment("PySpark.in tutorials are really helpful for beginners!")
print(result)
# [{'label': 'POSITIVE', 'score': 0.9998}]
# 2. Named Entity Recognition — find people, places, organisations
ner = pipeline("ner", grouped_entities=True)
text = "Elon Musk founded SpaceX in Hawthorne, California in 2002."
entities = ner(text)
for e in entities:
print(f"{e['word']} -> {e['entity_group']} ({e['score']:.2f})")
# Elon Musk -> PER (0.99) ← Person
# SpaceX -> ORG (0.98) ← Organisation
# Hawthorne -> LOC (0.97) ← Location
# California -> LOC (0.99)
# 3. Text Summarisation — summarise a long paragraph
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
long_text = """
Apache Spark is an open-source unified analytics engine for large-scale
data processing. Spark provides an interface for programming entire clusters
with implicit data parallelism and fault tolerance. Originally developed at
the AMPLab at UC Berkeley, it was donated to the Apache Software Foundation
and has been maintained since 2013.
"""
summary = summarizer(long_text, max_length=50, min_length=20)
print(summary[0]['summary_text'])Key Concepts Explained
Master these terms and you'll understand 80% of the conversations in this field.
Tokenisation
Breaking text into smaller units (tokens) the model can process. BERT uses WordPiece tokenisation: "unhappiness" → ["un", "##happiness"]. Every model has its own tokenizer.
Word Embeddings
Representing words as dense vectors of numbers where similar words have similar vectors. "king" - "man" + "woman" ≈ "queen" is the classic example.
Self-Attention
The core Transformer mechanism. For each word, it computes a weighted sum of all other words, allowing the model to capture long-range dependencies like pronoun references.
BERT
Bidirectional Encoder Representations from Transformers. Pre-trained on masked language modelling, it reads text from both directions and produces rich contextual embeddings. Used for classification, NER, Q&A.
GPT (Generative Pre-trained Transformer)
A unidirectional decoder-only Transformer trained to predict the next token. Powers ChatGPT and similar generative models.
Fine-Tuning
Taking a large pre-trained model (like BERT) and continuing to train it for a few epochs on your smaller, labelled dataset to adapt it to your specific task.
HuggingFace
The GitHub of AI models. A platform hosting 300,000+ pre-trained models and the transformers Python library that makes downloading and running them trivial.
Sentence Transformers
Models that encode entire sentences into a single embedding vector. Used for semantic search, clustering, and building RAG systems.
Your NLP & Transformers Learning Path
Follow these steps in order — each one builds on the last. Designed for complete beginners.
- 1
Python Text Processing
Learn Python string operations, regex, and the NLTK library. Understand how to clean, tokenise, and normalise raw text.
- 2
Classic NLP
Study bag-of-words, TF-IDF, and n-grams. Build a spam classifier using scikit-learn to understand ML-based text classification.
- 3
Word Embeddings
Learn Word2Vec and GloVe. Understand why representing words as vectors captures semantic meaning.
- 4
HuggingFace Pipelines
Use pre-trained pipelines for sentiment analysis, NER, Q&A, and summarisation. Explore the Model Hub.
- 5
Transformer Architecture
Study the attention mechanism and Transformer architecture. Read the "Attention Is All You Need" paper summary.
- 6
Fine-tune BERT
Fine-tune a pre-trained BERT model on your own classification dataset using the HuggingFace Trainer API.
- 7
Generative AI & RAG
Use sentence-transformers to build a semantic search engine. Integrate with an LLM to build a RAG Q&A system.
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