How to scale and improve your

NLP pipelines with

Freelance Senior Data Scientist
+7 years experience in Consulting, Tech, Startups
Interests in NLP, MLOps, and AI products

Ahmed BESBES

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Goals

In this presentation you will:

Get to know spaCy and discover some of its hidden features
Perform low-level NLP tasks
Speed up processing with state-of-the-art speed
Enhance statistical models with rule-based techniques
Use visualization to debug models

Don't be shy. Ask questions!

Agenda

Introduction to spaCy
Scaling and performance
Rule-based matching with the Matcher class
Custom Named Entity Recognizers with the EntityRuler
Multiple visualizers
Custom components
Open-source projects

1. Introduction to spaCy

Open-source library for advanced Natural Language Processing (NLP) in Python
Designed for production use
Used to build information extraction systems and preprocess text for deep learning

pip install -U pip setuptools wheel
pip install -U spacy
python -m spacy download en_core_web_sm

Multiple features under the hood

State-of-the-art processing speed

Multiple statistical models

import spacy

nlp = spacy.load("en_core_web_sm")

doc = nlp("Apple is looking at buying U.K. startup for $1 billion")

for token in doc:
    print(token.text, token.pos_, token.dep_)

  Apple PROPN nsubj
  is AUX aux
  looking VERB ROOT
  at ADP prep
  buying VERB pcomp
  U.K. PROPN dobj
  startup NOUN dobj
  for ADP prep
  $ SYM quantmod
  1 NUM compound
  billion NUM pobj

A clean and simple API

A robust processing pipeline

A pipeline is composed of multiple components
It turns an input text into a Doc object
Some components can be removed or deactivated
Custom components can be created and added to the pipeline

Multiple native components

The Doc object is the output of a processing pipeline
It's a list of Token objects
Each Token object stores multiple attributes
A Span is is a slice of the Doc object

Doc, Token, Span, ...?

Multiple token attributes

link

import spacy

nlp = spacy.load("en_core_web_sm")

doc = nlp("Apple is looking at buying U.K. startup for $1 billion")

for token in doc:
    print(token.text, token.pos_, token.dep_)

  Apple PROPN nsubj
  is AUX aux
  looking VERB ROOT
  at ADP prep
  buying VERB pcomp
  U.K. PROPN dobj
  startup NOUN dobj
  for ADP prep
  $ SYM quantmod
  1 NUM compound
  billion NUM pobj

Code example #0

2. Scaling and performance

Use nlp.pipe method

Preprocesses texts as a stream, yields Doc objects

Much faster than calling nlp on each texst

# BAD

docs = [nlp(text) for text in LOTS_OF_TEXTS]

# GOOD

docs = list(nlp.pipe(LOTS_OF_TEXTS))

import os
import spacy

nlp = spacy.load("en_core_news_sm")

texts = ... # a large list of documents

batch_size = 128

docs = []

for doc in nlp.pipe(texts, n_process=os.cpu_count()-1, batch_size=batch_size):
    docs.append(doc)

spaCy can also leverage multiprocessing and batching

Tip #1 to speed up the computation 💡

Disable unused components for the pipeline

import spacy
nlp = spacy.load("en_core_web_sm", disable=["tagger", "parser"])

Tip #2 to speed up the computation 💡

If you want to tokenize the text only, use the nlp.make_doc

# BAD
doc = nlp("Hello World")

# GOOD
doc = nlp.make_doc("Hello World")

3. Rule-based matching with the Matcher class

The Matcher class detects a sequence of tokens that match a specific rule
Each token must obey a given pattern
Patterns rely on token attributes and properties (text, tag_, dep_, lemma_)
Operators and properties can be used to create complex patterns

Example of patterns - #1

from spacy.matcher import Matcher

nlp = spacy.load("en_core_web_sm")

matcher = Matcher(nlp.vocab)

pattern = [
   {"TEXT": "Hello"}
]

matcher.add("HelloPattern", [pattern])

doc = nlp("Hello my friend!")
matcher(doc)

>>> [(10496072603676489703, 0, 1)]

match = matcher(doc)
match_id, start, end = match[0]

doc[start:end]

>>> Hello

Example of patterns - #2

matcher = Matcher(nlp.vocab)

pattern = [
  {"LOWER": "hello"}, 
  {"IS_PUNCT": True}, 
  {"LOWER": "world"}
]

matcher.add("HelloWorldPattern", [pattern])

doc = nlp("Hello, world! This is my first attempt using the Matcher class")
matcher(doc)

>>> [(15578876784678163569, 0, 3)]

match = matcher(doc)
match_id, start, end = match[0]

doc[start:end]

>>> Hello, world

Example of patterns - #3

matcher = Matcher(nlp.vocab)

pattern = [
  {"LEMMA": {"IN": ["like", "love"]}},
  {"POS": "NOUN"}
]

matcher.add("like_love_pattern", [pattern])

doc = nlp("I really love pasta!")
matcher(doc)

>>> [(2173185394966972186, 2, 4)]

match = matcher(doc)
match_id, start, end = match[0]

doc[start:end]

>>> love pasta

Example of patterns - #4

pattern = [
  {"LOWER": {"IN": ["iphones", "ipads", "imacs", "macbooks"]}}, 
  {"LEMMA": "be"}, 
  {"POS": "ADV", "OP": "*"}, 
  {"POS": "ADJ"}
]

matcher.add("apple_products", [pattern])
doc = nlp("""Here's what I think about Apple products: Iphones are expensive, 
Ipads are clunky and macbooks are professional.""")

matcher(doc)

>>> [(4184201092351343283, 9, 12),
     (4184201092351343283, 14, 17),
     (4184201092351343283, 18, 21)]

matches = matcher(doc)
for match_id, start, end in matches:
  	print(doc[start:end])

>>> Iphones are expensive
    Ipads are clunky
    macbooks are professional

More patterns - #5

pattern_length = [{"LENGTH": {">=": 10}}]

pattern_email = [{"LIKE_EMAIL": True}]

pattern_url = [{"LIKE_URL": True}]

pattern_digit = [{"IS_DIGIT": True}]

pattern_ent_type = [{"ENT_TYPE": "ORG"}]

pattern_regex = [{"TEXT": {"REGEX": "deff?in[ia]tely"}}]

pattern_bitcoin = [
  {"LEMMA": {"IN": ["buy", "sell"]}}, 
  {"LOWER": {"IN": ["bitcoin", "dogecoin"]}},
]

Why you should use the Matcher class

Extract expressions and noun phrases

Enhance regular expressions with token annotations (tag_, dep_, text, etc.)

A rich syntax

Create complex patterns with operators and properties...

Preannotate data for NER training

Try out the interactive online Matcher

4. Custom Named Entity Recognizers with the EntityRuler

spaCy provides multiple Named Entity Recognition models
NER models recognize multiple things
- Persons
- Organizations
- Locations

NER models can also be enhanced by data dictionaries and rules

Allows to combine statistical with rule-based models for more powerful pipelines

Useful to detect very specific entities not captured by statistical models

New entities are added as patterns in an EntityRuler component

import spacy

nlp = spacy.blanc("en")
doc_before = nlp("John lives in Atlanta")

# No entities are detected

print(doc_before.ents)
# ()

# Create an entity ruler and add it some patterns

entity_ruler = nlp.add_pipe("entity_ruler")

patterns = [
    {
        "label": "PERSON",
        "pattern": "John",
        "id": "john",
    },
    {
        "label": "GPE",
        "pattern": [{"LOWER": "atlanta"}],
        "id": "atlanta",
    },
]

entity_ruler.add_patterns(patterns)
doc_after = nlp("Jonh lives in Atlanta.")

for ent in doc.ents:
    print(ent.text, ":", ent.label_)  
# John : PERSON
# atlanta : GPE

import spacy
import scispacy

# load a spacy model that detects DNA, RNA and PROTEINS from
# biomedical documents

model = spacy.load(
    "en_ner_jnlpba_md",
    disable=["tok2vec", "tagger", "parser", "attribute_ruler", "lemmatizer"],
)

# build a list of patterns and inject them into the entity ruler. 
# these patterns contain entities that are not initially captured 
# by the model. 
# knowledge bases or ontologies could be used to construct the patterns

patterns = build_patterns_from_knowledge_base()

print(patterns[:3])
# [{'label': 'PROTEIN', 'pattern': 'tetraspanin-5'},
#  {'label': 'PROTEIN', 'pattern': 'estradiol 17-beta-dehydrogenase akr1b15'},
#  {'label': 'PROTEIN', 'pattern': 'moz, ybf2/sas3, sas2 and tip60 protein 4'}]

# define an entity ruler
entity_ruler = model.add_pipe("entity_ruler", after="ner")

# add the patterns to the entity ruler

Usecase: How to improve the detection of biomedical entities with an EntityRuler?

5. Multiple visualizers (dependencies)

import spacy
from spacy import displacy

nlp = spacy.load("en_core_web_sm")
doc = nlp("Ahmed is a freelance data scientist and works in Paris")

displacy.serve(doc, style="dep")

Also possible from Jupyter

and ... Streamlit

https://github.com/explosion/spacy-streamlit

6. Custom components

A function that takes a doc, modifies it, and returns it

Registered using the Language.component decorator

Added using the nlp.add_pipe method

@Language.component("custom_component")
def custom_component_function(doc):
    # Do something to the doc here
    return doc

nlp.add_pipe("custom_component")

A simple custom component

import spacy
from spacy.language import Language


@Language.component("custom_component")
def custom_component(doc):
  	print(f"Doc length : {len(doc)}")
    return doc


nlp = spacy.load("en_core_web_sm")

nlp.add_pipe("custom_component", first=True)

>>> print("Pipeline:", nlp.pipe_names)
# Pipeline: ['custom_component', 'tok2vec', 'tagger', 'parser', 
# 			 'ner', 'attribute_ruler', 'lemmatizer']

>>> doc = nlp("I love pasta!")
# Doc length: 4

A more complex custom component

import spacy
from spacy.language import Language
from spacy.matcher import PhraseMatcher
from spacy.tokens import Span

nlp = spacy.load("en_core_web_sm")
animals = ["Golden Retriever", "cat", "turtle", "Rattus norvegicus"]
animal_patterns = list(nlp.pipe(animals))
print("animal_patterns:", animal_patterns)
matcher = PhraseMatcher(nlp.vocab)
matcher.add("ANIMAL", animal_patterns)

# Define the custom component
@Language.component("animal_component")
def animal_component_function(doc):
    # Apply the matcher to the doc
    matches = matcher(doc)
    # Create a Span for each match and assign the label "ANIMAL"
    spans = [Span(doc, start, end, label="ANIMAL") for match_id, start, end in matches]
    # Overwrite the doc.ents with the matched spans
    doc.ents = spans
    return doc


# Add the component to the pipeline after the "ner" component
nlp.add_pipe("animal_component", after="ner")
print(nlp.pipe_names)

# Process the text and print the text and label for the doc.ents
doc = nlp("I have a cat and a Golden Retriever")
print([(ent.text, ent.label_) for ent in doc.ents])

7. Open-source projects

Resources

https://spacy.io/

https://ner.pythonhumanities.com/intro.html

https://towardsdatascience.com/7-spacy-features-to-boost-your-nlp-pipelines-and-save-time-9e12d18c3742

https://www.youtube.com/playlist?list=PLBmcuObd5An5DOl2_IkB0JGQTGFHTAP1h

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How to scale and improve your

Ahmed BESBES

Goals

Agenda

1. Introduction to spaCy

Multiple features under the hood

State-of-the-art processing speed

Multiple statistical models

A clean and simple API

A robust processing pipeline

Multiple native components

Doc, Token, Span, ...?

Multiple token attributes

Code example #0

2. Scaling and performance

Tip #1 to speed up the computation 💡

Tip #2 to speed up the computation 💡

3.​ Rule-based matching with the Matcher class​

Example of patterns - #1

Example of patterns - #2

Example of patterns - #3

Example of patterns - #4

More patterns - #5

Why you should use the Matcher class

Try out the interactive online Matcher

4. Custom Named Entity Recognizers with the EntityRuler

NER models can also be enhanced by data dictionaries and rules

5. Multiple visualizers (dependencies)

Also possible from Jupyter

and ... Streamlit

6. Custom components

A simple custom component

A more complex custom component

7. Open-source projects

Resources

Thank you

Copy of How to scale and improve your NLP pipelines with spaCy.

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3. Rule-based matching with the Matcher class