In this blog post, I will introduce you to TextBlob, a Python library that provides an easy-to-use API for natural language processing (NLP) tasks. TextBlob comes with various features, including text completion, which allows for automatic word or sentence suggestions based on the given input.

Installation

Before getting started, make sure you have TextBlob installed. You can install it using pip:

pip install textblob

Basic Usage

To use TextBlob’s text completion functionality, follow these steps:

1. Import the necessary module:

   from textblob import TextBlob
   

2. Create a TextBlob object with your input text:

   text = "I love to eat p"
   blob = TextBlob(text)
   

3. Use the correct() method to get a suggested completion:

   completion = blob.correct()
   print(completion)
   

   Output: “I love to eat pizza”

Advanced Usage

TextBlob offers more advanced customization options for text completion. You can specify the number of suggestions you want, use different language models, and even train your own models.

Getting Multiple Suggestions

If you want to get multiple suggestions instead of just one, you can use the suggest() method instead of correct(). This method returns a list of suggested completions, ranked by their likelihood.

text = "I love to eat p"
blob = TextBlob(text)
suggestions = blob.suggest()
print(suggestions)

Output: [“pizza”, “pasta”, “popcorn”]

Using Language Models

By default, TextBlob uses its built-in language model for text completion. However, you can also specify a specific language model to use.

text = "J'aime m"
blob = TextBlob(text)
completion = blob.correct(language="fr")
print(completion)

Output: “J’aime manger” (French for “I like to eat”)

Training Custom Models

If the built-in language models do not provide accurate suggestions for your specific use case, you can train your own language models using the TextBlob class. Training a custom language model requires a labeled corpus of training data.

from textblob import Word
from textblob import WordList

train_data = WordList(["pizza", "pasta", "popcorn"])
Word("eat").spellcheck(trained=train_data)

Output: [(“eat”, 0.0)]

In this example, we trained the model with a small training data set consisting of pizza, pasta, and popcorn. As a result, when we used the spellcheck() method, it correctly identified the word “eat.”

Conclusion

TextBlob’s text completion capabilities make it a powerful tool for NLP tasks. Whether you need to suggest missing words or complete sentences, TextBlob provides a straightforward interface to accomplish these tasks. Give it a try and enhance your text processing applications with automatic completion functionality.