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Customize Tokens using Spacy

by vigneshchennai74 Updated: Jan 31, 2023

Tokenization in Python is the division of a text string into discrete tokens, which are typically words or punctuation. This task is handled by SpaCy's built-in tokenizer by default, but it also offers the option to personalize tokenization by building a custom tokenizer.  


There are several uses for customizing tokens in SpaCy, some of which include:  

  • Handling special input forms: A custom tokenizer can be used to handle specific input formats, such as those seen in emails or tweets, and tokenize the text in accordance.  
  • Enhancing model performance: Custom tokenization can help your model perform better by giving it access to more pertinent and instructive tokens.  
  • Managing non-standard text: Some text inputs may contain non-standard words or characters, which require special handling.  
  • Handling multi-language inputs: A custom tokenizer can be used to handle text inputs in multiple languages by using language-specific tokenization methods.  
  • Using customized tokenization in a particular field: Text can be tokenized appropriately by using customized tokenization in a particular field, such as the legal, medical, or scientific fields.  


Here is how you can customize tokens in SpaCy:  

Preview of the output that will get on running this code from your IDE

Code

In this solution we have used matcher function of Spacy library.

  1. Copy this code using "Copy" button above and paste it in your Python file IDE
  2. Enter the text that needed to be Tokenized
  3. Run the program to get Tokenize the given text.


I hope you found this useful i have added the Dependent Library ,versions and information in the following sections


I found this code snippet by searching "Customize Tokens using spacy" in Kandi. You can try any use case

Environment Tested

I tested this solution in the following version. Be mindful of changes when working with other versions


  1. This solution is created and executed in Python 3.7.15 version
  2. This solution is tested in Spacy on 3.4.3 version


Using this solution we can Tokenize the text which means it will break the text down into analytical units need for further processing. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us break the text in Python.

Dependent Libraries

spaCyby explosion

Python star image 25129 Version:3.4.4

License: Permissive (MIT)

💫 Industrial-strength Natural Language Processing (NLP) in Python

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spaCyby explosion

Python star image 25129 Version:3.4.4 License: Permissive (MIT)

💫 Industrial-strength Natural Language Processing (NLP) in Python
Support
Quality
Security
License
Reuse

If you do not have SpaCy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the Spacy page in kandi. You can search for any dependent library on kandi like Spacy

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