Creating training data (1)
spaCy's rule-based Matcher is a great way to quickly create training data for named entity models. A list of sentences is available as the variable TEXTS. You can print it the IPython shell to inspect it. We want to find all mentions of different iPhone models, so we can create training data to teach a model to recognize them as 'GADGET'.
The nlp object has already been created for you and the Matcher is available as the variable matcher.
Bu egzersiz
Advanced NLP with spaCy
kursunun bir parçasıdırEgzersiz talimatları
- Write a pattern for two tokens whose lowercase forms match
'iphone'and'x'. - Write a pattern for two tokens: one token whose lowercase form matches
'iphone'and an optional digit using the'?'operator.
Uygulamalı interaktif egzersiz
Bu örnek kodu tamamlayarak bu egzersizi bitirin.
# Two tokens whose lowercase forms match 'iphone' and 'x'
pattern1 = [{____: ____}, {____: ____}]
# Token whose lowercase form matches 'iphone' and an optional digit
pattern2 = [{____: ____}, {____: ____, ___: ____}]
# Add patterns to the matcher
matcher.add('GADGET', None, pattern1, pattern2)