Learn SPACY with Real Code Examples

spaCy enables developers to process and analyze large volumes of text efficiently.

It provides pre-trained models and pipelines for multiple languages.

spaCy integrates seamlessly with deep learning frameworks like TensorFlow and PyTorch for custom NLP tasks.

Tokenization and sentence segmentation

Part-of-speech tagging and morphological analysis

Named entity recognition (NER)

Dependency parsing and syntactic structure

Matcher and PhraseMatcher for rule-based extraction

Token: smallest unit of text

Doc: container for processed text

Span: slice of a Doc

Pipeline: sequence of components to process text

Vectors: numerical representations for similarity and ML

main.py / notebook.ipynb - main scripts or notebooks

data/ - raw and preprocessed text corpora

utils/ - helper functions for text cleaning and preprocessing

models/ - trained spaCy pipelines and custom components

notebooks/ - experimentation and prototyping

Load language model

Process raw text into Doc objects

Access tokens, entities, and syntactic dependencies

Apply custom pipeline components if needed

Use processed data for downstream ML or analytics tasks

Beginner: tokenization, lemmatization, and basic POS tagging

Intermediate: NER, dependency parsing, and text normalization

Advanced: custom pipeline components, entity linking

Expert: integrating spaCy with ML/DL workflows

Enterprise: large-scale text processing pipelines and multi-language models

spaCy vs NLTK: industrial-strength NLP vs educational toolkit

spaCy vs TextBlob: advanced NLP vs simple sentiment analysis

spaCy vs Hugging Face Transformers: pipeline efficiency vs large language models

spaCy vs Gensim: NLP vs topic modeling and word vectors

spaCy vs CoreNLP: Python-native vs Java-based NLP suite

2015 – spaCy created by Matthew Honnibal and Ines Montani

2016 – spaCy 1.x with core NLP components

2017 – spaCy 2.x with enhanced pipeline and models

2020 – spaCy 3.x with custom pipelines and transformers integration

2025 – spaCy 4.x with improved performance and multi-language support

Token: smallest meaningful unit of text

Doc: container for processed text

Span: slice of a Doc representing a phrase

NER: named entity recognition

Pipeline: sequence of text-processing components