In this spaCy NLP course, you will learn about core tasks like tokenization, NER, and POS tagging and advanced topics such as custom model training and complex NLP pipelines.

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spacy-code

This course extensively introduces the widely used Python library spaCy for natural language processing (NLP). It covers spaCy basics, such as tokenization and part-of-speech tagging, as well as advanced topics like custom model training and NLP pipeline creation. 

The course has three parts: 
Part 1 focuses on spaCy’s fundamentals, architecture, installation, and setup. It teaches common NLP tasks like tokenization, named entity recognition (NER), part-of-speech (POS) tagging, and dependency parsing. 

Part 2 delves into spaCy’s features, covering syntax and semantics. It explores pattern matching and semantics via word vectors and thoroughly discusses statistical information extraction techniques. 

Part 3 examines advanced topics, including developing complex NLP models that require expertise, analysis, and practical experience. Multiple experiments with various NLP tasks are conducted, including customizing statistical models to meet specific needs.

Mastering spaCy

import random
import spacy
from spacy.training import Example 
from spacy import displacy

train_set = [
  ("I love to eat apples and bananas for breakfast.",{"entities": [(14, 20, "FRUIT"), (25, 32, "FRUIT")]}),
    ("Oranges are a good source of vitamin C.", 
     {"entities": [(0, 7, "FRUIT"), (28, 38, "VITAMIN")]}),
    ("I need to buy some grapes at the grocery store.", 
     {"entities": [(19, 25, "FRUIT")]}),
    ("Pineapple is my favorite fruit.", 
     {"entities": [(0, 9, "FRUIT")]}),
    ("She made a delicious fruit salad with strawberries and kiwis.", 
     {"entities": [(38, 50, "FRUIT"), (55, 60, "FRUIT")]}),
]
entities = ["FRUIT","VITAMIN"]
nlp = spacy.blank("en")
ner = nlp.add_pipe("ner")
for ent in entities:
  ner.add_label(ent)

epochs = 25
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
with nlp.disable_pipes(*other_pipes):
    optimizer = nlp.begin_training()

    for i in range(epochs):
        random.shuffle(train_set)
        for text, annotation in train_set:
            doc = nlp.make_doc(text)
            example = Example.from_dict(doc, annotation)
            nlp.update([example], sgd=optimizer)


doc = nlp("I love to eat strawberries and blueberries for dessert.")
print(doc.ents)
print(doc.ents[0].label_)

colors = {'Fruit': "#00ff00"}
options = {"ents": ['Fruit'], "colors": colors} 
spacy.displacy.render(doc, style="ent", jupyter=True, options=options)




Let's look at the solutions to the previous exercise.

Getting Started

Core Operations with spaCy

Linguistic Features

Rule-Based Matchmaking

Working with Word Vectors and Semantic Similarity

Putting Everything Together: Semantic Parsing with spaCy

Assessment: spaCy Features

Customizing spaCy Models

Text Classification with spaCy

spaCy and Transformers

Putting Everything Together: Designing a Chatbot with spaCy

Appendix

Conclusion

Assessment - Machine Learning with spaCy

Solution: Customizing spaCy Models

Solution